Engineering Chemistry Volume 1

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Volume 1 of
Engineering Chemistry

ISSN print 2813-6527
ISSN web 2813-6535

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Table of Contents

Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

Samuel P. Kusumocahyo, Nabilla S Maharani, Silvya Yusri

Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition

Rendy Mukti, Made Tri Ari Penia Kresnowati, Tjandra Setiadi

Enhance the Growth of Clostridium ljungdahlii Microbial Cells by Modifying the Medium Composition and Trace Metals

Noviani Arifina Istiqomah, Gustin Mustika Krista, Rendy Mukti, Made Tri Ari Penia Kresnowati, Tjandra Setiadi

Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

Budi Husodo Bisowarno, Putri Ramadhany, Tjioe Gerry Sebastian Wibowo

Peak Wavelength Mapping of Triangular Reflection in Spherical Colloidal Cluster with Icosahedral Symmetry

Ryosuke Ohnuki, Yukikazu Takeoka, Shinya Yoshioka

Extraction Technology for Manganese Sulfate Solution Purification

Chen Zhe Li, Hong Yan Xie, Hui Xin Jin

Carbonaceous Catalyst Activated Persulfate for Degradation of Antibiotic Pollutants in Water

Chen Kai Zhong

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-chemistry-vol-1/978-3-0364-1292-4

Engineering Chemistry, ISSN: 2813-6535, Vol. 1, pp 1-8

doi: 10.4028/p-ev6150

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-08-10

Revised:
2022-10-27

Accepted:
2022-11-10

Online:
2023-03-30

Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

The original version of this paper is available on https://www.scientific.net/EC.1.1.pdf

Samuel P. Kusumocahyo , Nabilla S. Maharani and Silvya Yusri

¹Department of Chemical Engineering, Faculty of Life Sciences and Technology, Swiss German University, Tangerang 15143, Indonesia

Corresponding author: samuel.kusumocahyo@sgu.ac.id, nabilla.smaharani@yahoo.com, silvya.yusri@sgu.ac.id

Keywords: Biodiesel purification, glycerol, microfiltration, ceramic membrane, alumina membrane

Abstract

In biodiesel industries, the removal of glycerol from biodiesel is very important in the downstream process of the biodiesel production since the presence of glycerol in biodiesel causes diesel engine problems. Glycerol is commonly separated from biodiesel by extraction method using water, however, this method results in a vast amount of wastewater and requires a high energy consumption. In this work, a ceramic microfiltration membrane made of -alumina was applied to remove glycerol from biodiesel. The microfiltration experiment was carried out using biodiesel containing various glycerol concentrations as the feed. For all investigated glycerol concentrations from 1000 ppm until in the feed, the membrane showed an excellent separation performance with rejection values of 91 to . The profile of the permeate flux against the permeation time showed a flux decline because of the fouling phenomenon during the crossflow microfiltration experiment, and stable permeate fluxes were obtained after 2 h of permeation time. The result of this work showed that the separation process using the microfiltration membrane is a promising method to purify biodiesel instead of the conventional water washing method.

Introduction

The original version of this paper is available on https://www.scientific.net/EC.1.1.pdf

In recent years, the demand of fossil fuels productions has increased because of the rapid growing of the global energy consumption. However, the combustion of fossil fuels contributed to around of the total global greenhouse gas emission causing climate change and global warming [1]. Moreover, since fossil fuels are not renewable, the depletion of fossil fuels leads to the global energy crisis [2]. To mitigate the depletion of fossil fuels and its impact on the gas emission, substituting fossil fuels with renewable energy sources have become a prominent solution. One of the renewable fuels is biodiesel that can be used to replace the petroleum diesel or can be blended with the petroleum diesel to reduce the consumption of fossil fuel [3]. Biodiesel is very popular in many countries and usually produced through a transesterification reaction of vegetable oils with alcohol with the addition of catalyst [4]. There are international standards that must be fulfilled by biodiesel manufacturers. One of them is the content of glycerol which is the by-product of the reaction. The maximum limit of free glycerol content in biodiesel product is as regulated by the international standards such as EN 14214 and ASTM D6751. Problems in diesel engines such as injector fouling, and hazardous emission might occur due to a high amount of glycerol in biodiesel [5]. In most biodiesel plants, extraction using water (water washing) is commonly used to remove glycerol in biodiesel. The method is applied by adding water to the crude biodiesel to extract all impurities in biodiesel including glycerol [6]. However, this conventional method utilizes a high amount of water water per L biodiesel) that consequently resulting in wastewater and consumes a lot of energy for heating during the washing process [7-10].

On the other hand, membrane separation technology has been known as an effective separation technique because of the high selectivity of the membranes and the low energy consumption. It has been widely applied for separation and purification processes in various industries such as chemical, pharmaceutical, food and beverage industries, as well as water treatment and wastewater treatment processes [11]. Several studies reported the effectiveness of membranes such as microfiltration and ultrafiltration membranes for the biodiesel purification as an alternative method to replace the
conventional washing method using water [12-16]. Porous polymer membranes can be used for biodiesel purification, but polymer membranes are usually of low chemical and thermal resistance. The use of ceramic membranes for biodiesel purification is desired since ceramic membranes exhibit a high chemical and thermal resistance [17-18]. Studies on the purification of crude biodiesel using ceramic microfiltration membranes were conducted by some researchers who reported that the membranes exhibited high rejection of glycerol with rejection values up ranging from 80 to , however, the effect of glycerol concentration in the feed solution on the separation performance was not studied yet [13, 14, 15]. It is known that the glycerol concentration in the crude biodiesel ranges from 0.1 to [7] and the glycerol concentration in the feed may affect the separation performance of the membrane. Moreover, there were no reports on the analysis of the membrane microstructure that is crucial to explain the separation mechanism. In this study, a ceramic microfiltration membrane made of -alumina was used to remove glycerol from biodiesel through a microfiltration process. The objectives of this research are to study the influence of the glycerol concentration in the feed solution on the microfiltration performance of the membrane and to understand the separation mechanism by studying the membrane microstructure.

Experimental

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Microfiltration Experiment. The -alumina ceramic membrane was a tubular membrane with an outer and inner diameter of 10 mm and 7 mm , respectively. The length of the membrane was 100 mm . The membrane was supplied by Nanjing Tangent Fluid Technology Co. Ltd., China. For the microfiltration experiment, the membrane was installed in a home-made microfiltration experimental set-up consisting of a beaker for the feed solution, a Masterflex pump, a control valve, and a pressure gauge installed at the retentate side. After installation, the effective membrane area of the membrane was . To study the permeation of biodiesel, first the microfiltration experiment was carried out using pure biodiesel as the feed. Then, the feed solution was changed with biodiesel containing glycerol and water with the weight ratio of glycerol and water of . The glycerol concentration in the feed was varied at 1000 , 5000 and . The microfiltration was performed at an ambient temperature, while a trans-membrane pressure of was applied and kept constant throughout the experiment. After attaining the steady state condition, the microfiltration experiment for each of the feed solutions was conducted for 120 minutes, and the permeate was collected every 10 min . Then, the permeate flux was calculated with Equation (1) as follows:

where and are the volume of the collected permeate, the effective membrane area, and the time interval, respectively.

Analysis of Glycerol Concentration. For the analysis of glycerol concentration in biodiesel, an analysis method proposed by Bondioli et al. was used [19]. Using this method, it was possible to measure the glycerol concentration in biodiesel using a UV-Vis spectrophotometer. The preliminary step of analysis included the preparation of working reagents and a calibration curve. Using the calibration curve, the glycerol concentration in the permeate samples could be determined. Then, the rejection value of the membrane towards glycerol was calculated using Equation (2):

where and are the rejection, the glycerol concentration in the permeate, and the glycerol concentration in the feed, respectively.

Flux Decline and Flux Recovery Ratio. The permeate flux decline through the membrane was measured by comparing the permeate flux of the feed solution biodiesel-glycerol mixture with the pure biodiesel permeate flux. The ratio of the flux reduction was determined with Equation (3) [20]:

Here, is the initial permeate flux using pure biodiesel as the feed and is the permeate flux that was measured using biodiesel containing glycerol as the feed. After the membrane was used for the microfiltration of biodiesel containing glycerol, the membrane was cleaned using pure biodiesel that was circulated using a pump through the membrane for 60 min . Further, to study the effect of membrane cleaning on the flux recovery, the flux recovery ratio was calculated using the equation below [20]:

where is the permeate flux of pure biodiesel after cleaning the membrane.
Microstructure Analysis. The analysis of the membrane microstructure was done to study the correlation of the membrane pore characteristics to the microfiltration performance. The analysis was conducted by using Scanning Electron Microscopy (SEM, Jeol JIB-4610F) and Brunauer-EmmettTeller (BET) analytical instrument (BET Quantachrome Nova 4200e). The SEM analysis was conducted to study the visual microscopic image of the surface as well as the cross-section of the membrane, while the BET analysis was performed to study the surface area, the pore volume, the porosity, and the average pore size .

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EC.1.1.pdf

The result of the microfiltration experiment using pure biodiesel as the feed is shown in Figure 1. In the beginning, the alumina membrane showed unstable pure biodiesel permeate fluxes, then stable permeate fluxes were obtained after 60 min of the permeation time, indicating that a steady state condition was achieved with an average permeate flux of . Then, the feed was changed with biodiesel containing various concentrations of glycerol. The range of glycerol concentration in the feed was chosen between 1000 ppm and because this concentration range represents the glycerol concentration of crude biodiesel in most biodiesel industries before the wet washing method [7]. Figure 2 shows the permeate flux profiles within 120 min of the permeation time for various feed concentrations of 1000,5000 and glycerol. For all feed concentrations, it was observed that the permeate flux decreased in the beginning of the permeation time, indicating that membrane fouling occurred. The membrane fouling is a very common phenomenon that occurs in separation processes using membranes and caused by the concentration polarization on the membrane surface and inside the membrane pores [21-23]. In the beginning of the experiment (at 10 min ), the initial permeate fluxes were 247,121 , and for the feed concentrations of 1000,5000 , and , respectively. However, after 60 min the permeate fluxes showed similar values for all feed concentrations due to the fouling phenomena. The permeate flux decline due to the membrane fouling is commonly expressed by the flux decline ratio that can be calculated using Equation (3). Table 1 depicts the flux decline ratio as a function of the glycerol concentration in the feed. The membrane showed a flux decline ratio of for the feed concentration of 1000 ppm . The value of the flux decline ratio increased to and when the feed concentration was increased to 5000 and , respectively. The increase in the flux decline ratio with increasing feed concentration indicated that more glycerol molecules were deposited on the surface of the membrane or inside the pores, resulting in a higher resistance for the biodiesel to permeate through the membrane. The membrane was then washed using pure biodiesel and the permeate flux of the
pure biodiesel was measured again to determine the flux recovery ratio using Equation (4). The result showed a flux recovery ratio of , indicating that glycerol molecules remained as foulants in the pores of the membrane. The -alumina membrane is categorized as a hydrophilic membrane since it is well known that hydroxyl groups are formed on the alumina surface. Glycerol molecules remained in the membrane pores because of the interaction between the glycerol molecules and the hydroxyl groups of the alumina membrane. Thus, for the future application of the membrane, it is necessary to use other cleaning methods such as physical or chemical cleaning to effectively remove the foulants [24, 25].

Figure 1. Permeate flux profile against permeation time using pure biodiesel as feed

Figure 1. Figure 1. Permeate flux profile against permeation time using pure biodiesel as feed

Further, the glycerol concentrations in the collected permeate samples were analyzed using a UVvis spectrophotometer and the rejection values were calculated. The results can be seen in Table 2. It was observed that the membrane exhibited very high rejection values for all feed concentrations. The rejection values of the membrane toward glycerol were , and for the feed concentrations of 1000, 5000 , and , respectively. The increase in the rejection value with increasing feed concentration was also correlated with the membrane fouling and the flux decline ratio as described previously. It is interesting to note, that for all feed concentrations, the glycerol concentrations in the permeate samples were below 200 ppm ( ), indicating the biodiesel purified with the alumina membrane fulfilled the international standards (e.g. EN 14214 and ASTM D6751) that limit the glycerol concentration in biodiesel product to be less than . Compared with other ceramic membranes as reported by other researchers, the -alumina membrane used in this study exhibited higher permeate fluxes and comparable rejection values. Other study reported the performance of a ceramic microfiltration membrane for the biodiesel purification that showed steady state permeate fluxes ranging from 20 to and a rejection value of about for the glycerol concentration in the feed of [14]. Another ceramic membrane made of alumina and titania was reported to have steady state permeate fluxes ranging from 12 to with a high rejection value of for the glycerol concentration in the feed of [15].

Figure 2. Permeate flux profiles against permeation time for various glycerol concentrations in feed

Figure 2. Figure 2. Permeate flux profiles against permeation time for various glycerol concentrations in feed

Table 1. Flux decline ratio of alumina membrane for various glycerol concentrations in feed

Glycerol Concentration in Feed CF (ppm) | Initial Permeate Flux of Pure Biodiesel JI [L/(m²h)] | Average Permeate Flux using Biodiesel Containing Glycerol JP [L/(m²h)] | Flux Decline Ratio Rt (%)
1000 | 245.0 | 101.8 | 58.5
5000 | 245.0 | 86.1 | 64.9
10000 | 245.0 | 82.2 | 66.5

Table 2. Permeate concentration and rejection values for various glycerol concentrations in feed

Glycerol Concentration in Feed CF (ppm) | Glycerol Concentration in Permeate CP (ppm) | Rejection R (%)
1000 | 91.1 | 90.9
5000 | 44.3 | 99.1
10000 | 70.6 | 99.3

Further, the microstructure analysis was employed using the SEM method, which was done to be able to have the visualization of the surface and the cross section of the alumina membrane. The SEM analysis was conducted for the membrane cross section and the inner surface of the tubular membrane as the active layer. Figure 3 shows the SEM image of the cross section and the inner surface of the alumina membrane. It can be clearly seen that the membrane is an asymmetric membrane consisting of a supporting layer having large alumina particles and a thin active layer consisting of smaller alumina particles. The high permeate flux and the high selectivity in the separation of glycerol and biodiesel exhibited by this membrane was correlated with the asymmetric structure of the membrane that had a thin active layer with small pores, while the thick supporting layer consisted of larger pores. Further, the membrane was characterized for their microstructure by using a BET analytical instrumentation to study the surface area, the pore volume, the porosity, and the average pore size of the membrane. From the BET analysis, it was observed that the adsorption-desorption isotherm graph of the membrane showed the adsorption isotherm of type III based on the category by the International Union of Pure and Applied Chemistry (IUPAC), indicating a macroporous structure of the membrane
with pore size of . The result of the BET analysis showed that the membrane had a surface area of , a pore volume of , a porosity of , and an average pore diameter of . The separation mechanism in the microfiltration membrane is based on the difference between the pore size of the membrane and the particle size in the feed. In the case of biodiesel containing glycerol as the feed solution, biodiesel molecules passed through the membrane, while glycerol was rejected. According to the study conducted by Saleh et al., glycerol dispersed in biodiesel formed micelles with water and soap, and the size of the micelles are approximately [14]. Thus, the glycerol micelles were rejected by the alumina membrane that had an average pore size of .

Figure 3. SEM image of the cross section and the surface of alumina membrane

Figure 3. Figure 3. SEM image of the cross section and the surface of alumina membrane

Figure 4. SEM image of the surface of alumina membrane

Figure 4. Figure 4. SEM image of the surface of alumina membrane

Conclusion

The original version of this paper is available on https://www.scientific.net/EC.1.1.pdf

The results of the microfiltration experiments of biodiesel containing glycerol using the -alumina microfiltration membrane showed that the membrane exhibited high permeate fluxes and high selectivity. The rejection values of the membrane toward glycerol were , and for the glycerol concentrations in the feed of 1000,5000 , and , respectively. The high permeate flux and rejection of the membrane was correlated with the microstructure of the membrane
that consisted of a supporting layer with large pores and a thin active layer with smaller pores. For all investigated feed concentrations, the membrane exhibited glycerol concentrations in the permeates with values lower than 200 ppm ( ), showing that the purified biodiesel met the international standards. The result of this work opens the possibility for the application of the ceramic microfiltration membrane for the downstream processing in biodiesel industries.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/EC.1.1.pdf

A sincere appreciation is given by the authors to the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia for the financial support through the PDUPT research grant program 2021/2022.

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The original version of this paper is available on https://www.scientific.net/EC.1.1.pdf

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The original version of this eBook is available on https://www.scientific.net/book/engineering-chemistry-vol-1/978-3-0364-1292-4

Engineering Chemistry, ISSN: 2813-6535, Vol. 1, pp 9-19

doi: 10.4028/p-9g14o1

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-08-19

Revised:
2022-11-17

Accepted:
2022-11-24

Online:
2023-03-30

Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition

The original version of this paper is available on https://www.scientific.net/EC.1.9.pdf

Rendy Mukti , Tjandra Setiadi and M.T.A.P Kresnowati

¹Department of Chemical Engineering, Institut Teknologi Bandung, Bandung, 40132, Indonesia

Department of Food Engineering, Institut Teknologi Bandung, Bandung, 40132, Indonesia

rendy01.mukti@gmail.com, tjandra@itb.ac.id, kresnowati@itb.ac.id*

Keywords: Bioethanol, Fermentation, Lignocellulosic biomass, Syngas.

Abstract

Energy challenges in developing countries are more significant if they continue to use fossil materials and impact air quality. Lignocellulosic biomass can be an alternative to new renewable sources to replace fossil materials. Indonesia produces various sources of lignocellulosic biomass, which can be used in multiple energy sources, such as bioethanol. The hybrid pathway is one of the routes for producing bioethanol. The first stage of the hybrid process is the conversion of biomass into , and (syngas) gas through the gasification process. Then the syngas is converted into bioethanol through fermentation using microorganisms as biocatalysts. The bioethanol production line is the Wood-Ljungdahlii pathway. Factors that affect syngas are the type of biomass (chemical, physical, and morphological properties) and the gasification process (type of gasifier, temperature, gasification agent, and ratio equilibrium (ER)). This paper reviews the challenges in implementing syngas fermentation. In particular, variations in the composition of syngas as a substrate for fermentation.

Introduction

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Energy challenges as a fundamental component of economic activity for developing countries are more significantly related to the dependence on dwindling fossil fuel sources and have a negative impact on air quality related to emissions produced [1,2]. An alternative to meeting the longterm energy mix, minimizing the use of fossil energy sources, and reducing emissions is to use biofuels produced from renewable resources such as biomass . Biomass is one of the abundant renewable carbon sources and can be used as an alternative energy source to replace fossil fuels [57]. The advantage of using plant-based biofuels in the transportation sector is that it can reduce greenhouse gas emissions because the content produced from the combustion process will be bound by plants for the photosynthesis process so that the content does not increase in the atmosphere [3,8]. In addition, biomass is more efficient if converted into liquid fuels such as ethanol [9]. Raw materials in the manufacture of bioethanol can be sourced from the first-generation (starchbased raw materials), second-generation (lignocellulosic biomass), or third-generation (algae raw materials) [10-12].

Generally, there are three routes for bioethanol production: biochemical, thermochemical, and hybrid. The hybrid pathway combines thermochemical and biochemical processes: biomass is transformed into syngas through the gasification process, and then the syngas is fermented into shortchain organic acids and alcohols such as ethanol by acetogenic bacteria [13,14]. The syngas fermentation process offers an advantage over the chemical catalytic process in that the microorganisms can work in the presence of some impurities in the syngas and are flexible to the ratio [13]. In addition, the process conditions, such as temperature and pressure required in the fermentation process, are lower than the chemical process, reducing operating costs [13,15]. The characteristics of syngas as the output of biomass gasification maybe affected by various factors which further may also affect the performance of the syngas fermentation process. It is the goal of this paper to review the factors affecting the characteristics of syngas and the corresponding effects on the syngas fermentation performance.

Lignocellulosic Biomass

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Lignocellulosic biomass can be obtained from agricultural residues, forest residues, and postharvest processing [16,17]. Lignocellulosic biomass has the main components, namely cellulose and hemicellulose that may compose up to of biomass, and lignin that may compose up to , and minerals . The selection of lignocellulosic biomass is essential because significant differences in the biomass chemical, physical, and morphological properties will affect the syngas composition [19,20]. High carbon and oxygen content in biomass can produce a higher percentage of combustibles in syngas [21]. The water content of more than reduces the calorific value of syngas because the energy consumed is used to remove water content; besides that, the humidity will inhibit changes in the oxidation temperature of the gasifier so that it can increase the tar content [22]. The high ash and mineral content causes the formation of slag, which causes obstacles to biomass feed to the gasifier [23]. The larger the size of the biomass feed will reduce the reaction surface, thereby inhibiting mass and heat transport for the downdraft gasifier [24,25].

Gasification

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Gasification is a thermochemical conversion process technology for energy production that has been used for a long time; conventionally, the raw materials used are fossil-based such as coal and natural gas, but other alternative raw materials that can be used are biomass, municipal solid waste (MSW), and plastic [26,27]. The advantages of biomass compared to fossil sources in the gasification process are that it contains less nitrogen, sulfur, and heavy metals, lower gasification temperature, lower pollutant emissions, and higher reactivity [28,29].

The gasification process is drying, pyrolysis, oxidation, and reduction [24]. Drying, this process is carried out at a temperature of until the water content reaches to [24]. As a result, energy efficiency is increased, and syngas quality is improved by reducing the moisture content [30]. Pyrolysis occurs without air, gas, or other gasification media, which causes the breakdown of hydrocarbon molecules into smaller gas molecules, tar, and leaves solid carbon biomass [31]. Pyrolysis occurs at a temperature of [32]. Oxidation is a reaction between solid biomass and oxygen from agent gasifying and biomass to form , and , formed from hydrogen in oxidation biomass [24]. The oxidation of carbon and hydrogen is an exothermic reaction that produces a large amount of heat. When oxygen is available in sufficient stoichiometric quantities, partial carbon oxidation will occur, forming carbon monoxide [24]. The reactions that occur in the oxidation process are as follows:

Reduction occurs when there is little oxygen in the system, a reduction reaction occurs in the temperature range of [33]. The reaction is as follows:

Reaksi water-gas
CO + H₂O ⇌ CO₂ + H₂
(4)
Bounded reaction

Gasification conditions affect the performance of the gasification process, such as the gasifier type, temperature, gasification agent (air, , or steam), and the equivalent ratio of supplied air demand to stoichiometry (ER) [27,28,34]. The ER represents the real air-to-biomass ratio in terms of stoichiometry [35]. There are three gasifier types categories: Fixed Bed, Fluidized Bed, and entrained flow. Fixed bed types include downdraft gasifiers, and updraft gasifiers, while fluidized bed types include circulating fluidized bed gasifiers and bubbling bed gasifiers [36]. A fixed bed gasifier is the simplest gasifier, with long solid residence time, low gas velocity, biomass that does not need to be
uniform, and low dust (ash) content [37]. The disadvantages of fixed bed gasifiers are that they produce high tar content and operate at high temperatures due to the biomass size [38]. Fluidized bed gasifiers are suitable for large-scale biomass gasification because the mixing and uniform temperature result in increased carbon conversion and are the most widely used for biomass gasification today [39].

The higher temperature in the gasification process causes an increase in the calorific value of syngas because the high temperature is suitable for the oxidation process and produces less tar, but if the temperature is too high, the gasifier will crack faster [40,41]. In addition, the type of gasifier agent affects the composition of the syngas, and if the air is used as a gasifying agent, it will produce nitrogen compounds in the syngas, which is higher when compared to the gasifying agent of steam or oxygen [42].

Gasification converts raw materials into carbon monoxide and other synthetic gases to become fuel gases or other chemicals using gasification agents such as oxygen, steam, or air [22]. The main components of syngas are carbon monoxide ( CO ), hydrogen ( ), and carbon dioxide ( ). In addition, it may contain other gases such as water , ammonia , nitrogen oxides , methane , ethene , ethane , ethine , benzene , naphthalene , hydrogen cyanide , sulfur dioxide , hydrogen sulfide , carbonyl sulfide, and condensable volatiles [15]. Impurities from the gasification process include tar and particulates [43]. The composition of syngas from various biomass and variations in gasification operating conditions can be seen in Table 1.1.Overall, the composition of syngas may be effected by the the types of biomass used as the raw materials, the types of gasifier, the types of gasification agent, and the applied ER.

Table 1.1 The composition of syngas from biomass gasification

Gasifier | Biomass | ER | Gasification agent | Temp (°C) | Composition (%volume Dry) | Ref
|  |  |  |  | CO | H₂ | CO₂ | CH₄ | N₂ |
Fluidized bed | Wood Chips | 0.3 | Air | 800 | 18.0 | 13.2 | 11.5 | 4.1 | 41.1 | [44]
0.3 | O₂ | 35.7 | 27.9 | 10.5 | 0.76 | 0.9
n/a | Steam | 14.0 | 31.1 | 14.1 | 7.3 | 0.0
n/a | Steam (dry) | 18.5 | 41.0 | 18.6 | 9.7 | 0.06
Fluidized bed | Cypress sawdust | 0.54 | Air | 700-800 | 6.9 | 5.6 | 18.1 | 1.4 | 68.0 | [45]
Mixed pine bark-spruce | 0.22 | Air | 21.4 | 5.4 | 14.7 | 4.6 | 53.9
Bubbling fluidized bed | Rice husk | n/a | Air | 702 | 21.3 | 4.4 | 11.3 | 4.3 | 57.1 | [46]
737 | 16.9 | 4.8 | 15.9 | 3.7 | 57.1
Fixed bed downdraft | Olive | n/a | Air | 1190 | 17.4 | 13.2 | 12.4 | 9.5 | 54.9 | [47]
Peach | 1170 | 17.7 | 15.0 | 13.5 | 5.8 | 51.7
Pine | 1140 | 16.0 | 12.0 | 11.4 | 8.9 | 59.4

Syngas can be converted through the Fischer-Tropsch (FT) process using a chemical catalyst to produce diesel, methanol, or ethanol [13]. The FT process is generally conducted at high operating temperatures and pressures and requires stable ratios. Gaseous impurities may become toxic during the process [13]. Syngas can also be converted using a microorganism catalyst to alcohols, carboxylic acids [48,49], and 3-butanediol [50].

Syngas fermentation

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The production of bioethanol via syngas fermentation offers an advantage when compared to the conventional lignocellulosic hydrolysis followed by sugar fermentation that it can increase product yields from the same amount of raw materials because all biomass components, including lignin, can be converted into syngas in the gasification process. On the other hand, the low solubility of syngas
to fermentation broth may presents as the main challenge for the process [51-54]. Some efforts have been made to address this issue, among others by developing a dedicated fermentor design equiped by a hollow fiber membrane contactor [55].

The syngas fermentation is facilitated by the acetogenic bacteria that can convert , and with flexible molar ratios and withstand some impurity gases in syngas [49,56]. Syngas fermentation is a biological process in which CO and from syngas are converted to acetyl-CoA with the help of acetogenic bacteria and subsequently to acetic acid, ethanol, and many other products via the Wood-Ljungdahl pathway [57]. The microorganisms used are anaerobic microorganisms which are classified as autotrophic. Autotrophs utilize C 1 compounds in syngas, including CO and/or as a carbon source and as an energy source [58].

The Wood-Ljungdahl pathway reduces to CO and formic acid or directly to formyl groups. The formyl group is reduced to a methyl group and then combined with carbon monoxide and coenzyme A to produce acetyl-CoA [59]. This path can be seen in Fig.1.

Fig. 1 Wood - Ljungdahl Pathway [9,15,49]

Fig. 1. Wood - Ljungdahl Pathway [9,15,49]

The initial step before the WLP process is to convert 1 mole of into 2 moles of reducing agent by the enzyme hydrogenase (HYA). Meanwhile, a water-gas shift reaction converts one mole of CO and into and 2 moles of reducing agent by carbon monoxide dehydrogenase . The reducing equivalent is used to fix carbon molecules from CO and into cell biomass and other metabolites via WLP. [60,61]. There are two main steps in the formation of acetylCoA . The first step is to reduce in the methyl group through several reduction reactions using several enzymes.

In contrast, in the carbonyl group, compounds are reduced to CO using the carbon monoxide dehydrogenase (CODH) enzyme. In the second step, the methyl and carbonyl groups are combined with the enzyme acetyl-CoA synthase (ACS) and carbon monoxide dehydrogenase (CODH) to produce acetyl-CoA. Then form acetic acid and ethanol. Some acetyl-CoA is used to create microbial cell mass [62].

Syngas Fermentation Conditions

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The syngas fermentation process is influenced by the type of microorganism, bioreactor type, syngas composition, and gas-liquid mass transfer [63].
Types of microorganisms. The microorganisms used for ethanol production are Clostridium ljungdahl, Clostridium autothenogenum, Clostridium ragsdelai, and Alkalibaculum bacchi [15]. C. ljungdahl energy sources are and or CO , whereas . autothenogenum uses CO as the only source of carbon and energy [64]. Alkalibaculum bacchi will produce more ethanol if the CO composition is greater [65].
Bioreactor type. Bioreactors with a high reaction surface area will support a high mass transfer ratio and get a high syngas conversion [56]. Bubble diameter will be one of the critical parameters in gas-liquid mass transfer in suspended growth bioreactors [56]. Several reactors used in the production of ethanol are stirred tank bioreactor (STB), Bubble column reactor (BCR), Trickling bed reactor (TBR), Moving bed biofilm reactor (MBBR), and membrane bioreactor (MBR) [56]. STB uses mechanical stirring to reduce bubbles, BCR, which is applied in the process, does not use a stirrer but makes the syngas stay longer in the reactor, TBR is a reactor design with the main influencing parameters, there is the size of the reactor gasket, liquid recirculation rate, and gas flow rate, MBBR is a reactor where microbes grow on the surface of certain media then form a biofilm layer that increases mass transfer [66]. One type of MBR is a hollow filter membrane bioreactor (HFMR) which can produce micro solid bubbles [67]. The maximum concentration of ethanol obtained was more significant when using STR with HFM, which was compared to without HFM [55].

Gas-liquid Transfer mass. Gas-liquid mass transfer is needed to balance the kinetic needs of the cell without inhibiting metabolism so that the fermentation process can run efficiently [68] The limited mass transfer causes a reduction in the availability of substrate for the growth of microorganisms in the growth medium [69]. The solubility of syngas in the fermentation broth will affect the availability of substrate for the growth of microorganisms in the medium [70]. The volumetric mass transfer coefficient ( ) is often used to measure the solubility of gases in the liquid phase [71]. The a for , and gases are respectively , dan 277, 1 hour [72]. Simulations were carried out to calculate the utilization of CO and with the utilization of CO and of 0.92 mol and 2.03 mol with a consumption rate of -day and -day resulting in the conversion rate of dissolved CO in the fermentation broth is while the dissolved in the fermentation broth is because the solubility of is more significant than CO [72,73]

Effect of Impurity Gases in Syngas. Overall, the syngas fermentation process is said to be more resistant to the presence of impurities in the syngas than the chemical catalytic process [63]. However, the literature also mentioned that some impurities could inhibit the activity of acetogenic bacteria even at low concentrations by limiting enzyme activity and cell growth or by changing physicochemical conditions such as osmolarity, redox potential, pH , etc. [63].

Volatile hydrocarbon compounds such as in a concentration of 4,5% did not affect the gas utilization process by C. carboxidivorans [74]. In addition, the utilization of the same microbes using syngas containing , and did not affect growth during the fermentation process [75].

Tar causes inhibition of cell growth until microbial microbes can adapt to tar. In addition, tar can also increase the ratio of ethanol to acetic acid because tar will inhibit the production process of acetic acid [75]. C. butyricum can be grown 500 times in a bioreactor using tar-free syngas compared to tar-containing syngas [76]. The conventional technology for separating tar from syngas is the scrubbing system [77]. Filtration can also remove ash, tar, and other particulates from the produced gas [78].

Some species can utilize certain impurities in the syngas fermentation process. and gas can increase the growth and formation of alcohol (ethanol, 1-hexanol, and 1-butanol) during fermentation using C. carboxidivorans [79]. These bacteria can withstand rapidly turning into ammonium ions ( ) in the fermentation medium, which causes inhibition of hydrogenase activity and the growth of acetogenic bacterial cells [80]

Nitrogen oxides, nitrates, and nitrites can reduce biomass growth and alcohol concentration during syngas fermentation using Clostridium carboxidivorans [81]. In addition, Nitric oxide, which is present in syngas at 150 ppm , inhibits the hydrogenase enzyme that consumes [75].

The presence of impurities affects the performance of syngas fermentation, it may either improve the ethanol productivity or reduce it. The net effects should be researched further for a specific syngas composition.

Effect of Composition Gas on Syngas Fermentation Process. The carbon source for acetogenic bacteria in the Wood-Ljungdahl is CO or CO2, which CO2 can be used as a carbon source in the presence of . The energy source used is CO or . One of the advantages of the fermentation process is that it is flexible to the ratio. Still, acetogenic prefer a low ratio because most organisms grow better on CO than on will increase the conversion of CO into organism growth and ethanol production because acts as a source of electrons [53]. If is reduced, the source of electrons used is CO ; this causes reduced ethanol production because the amount of carbon used is reduced according to the required reducing equivalents [53].

The fermentation process using the microbe C. ljungdahlii with an ratio of 2.0 produced acetate of 35.21 mM and ethanol of 5.39 mM , while the ratio of 0.5 produced acetate of 22.64 mM and ethanol of 7.44 mM [83].

According to Henry's law, only a small amount of CO and are soluble in water, and their solubility depends on the partial pressure of various species [49]. Decreasing the partial pressure of CO from 2.0 to 0.35 atm reduced cell growth and reduced acetic acid to ethanol. In addition, the partial pressure ratio of CO to will affect the production of electrons and ATP [84]. can also stimulate acetogenesis, the ratio of 70:30 increases the concentration of acetic acid compared to only pure CO . The produced during the solventogenesis process from CO conversion can be involved in the re(oxidation) of ethanol to acetic acid [85].

However, the microbes normally need some times to different process condition, significant changes in the gas composition may affect the performance of the fermentation. The time required by C.ljungdahl to reach the maximum cell concentration with a syngas compositin , , and for 20 hours [86], while with a syngas composition of and it takes 72 hours [55]. On the other hand, due to process instability, the composition of syngas produced during the gasification process has changed in a certain range. Therefore, it becomes a challenge in the fermentation process.

Conclusion

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The combination of the thermochemical-biological process to convert lignocellulosic biomass into bioethanol is the gasification of lignocellulosic biomass into syngas followed by fermentation of syngas into bioethanol is an emerging technology. The biomass type and the gasification process conditions will affect the gas composition and the resulting impurities, such as tar. These factors may affect fermentation performance and the corresponding bioethanol productivity. Further research needs to be conducted to address these problems.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-chemistry-vol-1/978-3-0364-1292-4

Engineering Chemistry, ISSN: 2813-6535, Vol. 1, pp 21-29

doi: 10.4028/p-t9r224

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-08-21

Revised:
2022-10-26

Accepted:
2022-11-10

Online:
2023-03-30

Enhance the Growth of Clostridium ljungdahlii Microbial Cells by Modifying the Medium Composition and Trace Metals

The original version of this paper is available on https://www.scientific.net/EC.1.21.pdf

Noviani Arifina Istiqomah , Gustin Mustika Krista , Rendy Mukti , Made Tri Ari Penia Kresnowati , Tjandra Setiadi

¹Department of Chemical Engineering, Institut Teknologi Bandung, Bandung, 40132, Indonesia

Department of Food Engineering, Institut Teknologi Bandung, Jatinangor, Indonesia

noviani.a.i@gmail.com, gustin.krista@gmail.com, rendy01.mukti@gmail.com, kresnowati@che.itb.ac.id,

Keywords: Bioethanol; Syngas fermentation; Trace metal; Growth medium; Clostridium ljungdahlii.

Abstract

Syngas fermentation is an alternative route that combines the advantages of thermochemical and biochemical processes have been proposed for biomass conversion to ethanol. One of the main obstacles to syngas fermentation is the low yield of ethanol, caused by the limited utilization of the syngas substrate due to low microbial cell concentration in the fermentation system. This research examined the modification of fermentation medium to improve microbial cell growth. The modifications were to increase the concentration of micronutrients/trace metals and macronutrients in the medium. The results showed that the maximum mass cell and maximum growth rate produced by microbial growth in the modified trace metal medium were and , while in a modified macronutrient medium were and . Modification of the macronutrient medium was able to increase the yield of biomass, but the opposite occurred in the modification of the trace metals. Meanwhile, the maximum concentration of ethanol from syngas fermentation in the modified macronutrient medium was lower than the recovery of ethanol in the standard medium.

Introduction

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The need for global fossil fuels has risen sharply along with the increase in the human population. Plant biomass is one of the world's fourth largest non-fossil renewable energy sources after geothermal, solar, and wind. One of the uses of biomass as an energy source that is more efficient than direct combustion is to convert it into fuel/biofuel such as ethanol [1].

The two main pathways for biofuel production, particularly ethanol from lignocellulose, are biochemical and thermochemical. In the biochemical pathway, biomass is pretreated with the addition of acid, alkali, or steam to break the cellulose-hemicellulose-lignin interaction. This pretreatment makes the biomass more accessible to enzymes. Pretreated biomass becomes a target for hydrolysis enzymes to obtain fermentable sugars. After that, the hydrolyzate is fermented to produce ethanol [2]. The advantages of the biochemical pathway are the high selectivity of biocatalyst products [3], the reactor is not too complex, and operating at ambient temperature and pressure [4]. However, the biochemical pathway faces several challenges such as high pretreatment and enzyme costs, relatively slow process, lignin as a by-product, soluble inhibitory compounds (e.g., acetic acid, furfural, 5hydroxymethylfurfural, phenolic compounds [5].

The thermochemical pathway involves biomass gasification into syngas (a mixture of CO and ). Syngas then converted into biofuels using a chemical catalyst known as the Fischer-Tropsch (FT). The advantage of the FT thermochemical process is that it can process heterogeneous raw materials, including lignin, and the process is relatively fast. However, constraints in the FT thermochemical pathway include the need for extensive infrastructure, high cost of metal catalysts, high operating temperatures and pressures, requiring consistent gas quality, and stable gas composition ratio [6].

The third pathway, namely syngas fermentation, emerged as a midpoint combining the advantages of the biochemical and thermochemical pathways. First, the biomass feedstock is
thermochemically processed to produce syngas. The produced syngas is then used as the carbon source for the microbial biocatalysts to be converted into ethanol [7].

The potential microbes for conducting the syngas fermentation process are the acetogenic bacteria that is capable of reducing organic acids to alcohol [8], including Clostridium ljungdahlii, Clostridium autoethanogenum, Clostridium carboxidivorans P7, Acetobacterium woodii [9]. Acetogens can grow on and CO , sugars, one-carbon compounds, methoxylated aromatic compounds, and alcohols. Acetogens can directly generate 1 mole of hexose to 3 moles of acetate [10], utilizing all the carbon substrates in the solution product.

The ability of microbes to convert syngas into metabolic products occurs through the reductive acetyl-CoA pathway called the Wood-Ljungdahl pathway [11]. The fermentation stage is typically divided into two stages: acidogenesis and solventogenesis [12]. The acidogenesis stage is the growth stage where cells will grow accompanied by acetate and butyrate products, while the solventogenesis stage is when the cells produce ethanol solvent compounds.

The concentration of microbial cells formed during the acetogenesis phase determines the number of fermented ethanol products. The maximum growth of microbial cells is closely related to growth media that provide all essential nutrients such as minerals, trace metals, vitamins, and reducing agents for maximum microbial growth. Therefore, the choice of growth medium depends on the species selected and the end product targeted. Reducing agents (such as sodium thioglycolate, ascorbic acid, and benzyl-viologen) cause an electron flow shift, diverting carbon flow from acid production to alcohol [13].

Two types of nutrients are required for cell growth: macronutrients and micronutrients (trace elements). The macronutrient medium is composed of carbon and magnesium compounds. Fructose is a common carbon source for Clostridium ljungdahlii inoculum growth. The majority of carbon substrate during anaerobic fermentation is converted into products, while a small portion is converted into cell mass. The primary source of carbon and energy for cells is carbon compounds. Magnesium is a cofactor for a number of enzymes and is found in cell membranes and cell walls. Ribosomes are dependent on ions. During the exponential growth phase, magnesium and sodium play unique roles [14]. The buffer is another essential component of the microbial growth medium. functions as a buffer to maintain and regulate the pH of the medium. However, it can also support microbial growth as a carbon and source [15]. Sodium is crucial during the exponential growth phase [16].

Trace metals are essential for the growth of acetogens, including cobalt, iron, molybdenum, nickel, selenium, and tungsten, all of which are constituents of enzymes involved in synthesizing acetate from . Several recent studies have shown that certain elements are essential in ethanol production [17]. The vital role of metalloenzymes in syngas fermentation makes optimization of trace metal concentrations in the growth medium important. Ethanol synthesis and production costs will be inefficient if the metalloenzyme element is deficient or excess. Several studies have reported reducing and increasing the concentration of trace metal components on microbial growth and the formation of syngas fermentation products [18]-[24]. This study aims to increase the growth of . ljungdahlii inoculum as an essential part of the syngas fermentation process to obtain optimal acetic acid and ethanol. So, the effect of modifying the concentration of trace metal components in the medium was examined.

Material and Methods

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Material and culture medium. The microorganism used in the experiment was Clostridium ljungdahlii DSM 13528 from the Deutsche Sammlung von Mikroorganismen und Zullkulturen GmbH collection center, Braunschweign, Germany. The basal culture medium used for acetogen culture was DSMZ 879, with the composition: , , yeast extract , trace metal solution , Na-resazurin solution ( , D-fructose , vitamin solution , L-cysteine- , and . The trace metal solution contains: nitrilotriacetic acid ,
, , , and . The vitamin solution consists of: biotin , folic acid , pyridoxine , thiamine HCl 5 , riboflavin , nicotinic acid , D-Ca- pantothenate , vitamin B12 , p-aminobenzoic acid , and lipoic acid . The ATCC medium procedure for anaerobic cultivation was applied in the preparation and use of the media.

Culture Experimental Method. After placing 225 mL of culture medium in a 500 mL vial, purging using gas for 15 minutes, then sealing the bottle using aluminum, the vial containing the media was autoclaved at for 15 minutes. After that, Wolin's vitamin solution was added, sterilized by filtration, , D-fructose, and a mixture of -L cysteine HCl, each of which had been autoclaved in separate bottles at for 15 minutes. Next, the precultured microbial solution was inoculated with of the total media. The culture medium was then grown for 10 days at with a stirring speed of 200 rpm .

As a modification variation, the use of trace metals and are five times higher, while and are ten times higher than that of the standard medium. Meanwhile, in the modified macronutrient medium, the use of fructose, and are double, while is ten times than that of the standard medium. There are two variations of medium modification for microbial growth, namely: (1) macronutrient modification with trace element composition is standard and (2) trace element modification with macronutrient composition is standard.

When the modified macronutrient medium was used for fermentation by C. ljungdahlii, the carbon source of fructose was replaced by CO and from syngas. As a result, gas is derived from pure gas with a composition of , and . Two variations of the medium used in syngas fermentation were: (1) standard macronutrient and trace element medium, used for inoculum growth and fermentation, and (2) modified macronutrient medium and standard trace element, used for inoculum growth and fermentation.

The fermentation process was carried out on a 1.5 L scale fermenter with a working volume of 1 L , run for 20 days, with a temperature of and an agitation rate of 300 rpm . In the fermentation process, the pH of the fermenter is controlled so that it is not lower than 4.5 to avoid cell death, and pH control is carried out with 2 M HCl and 2 M NaOH solutions. Details configuration of the bioreactor used was described in Anggraini [25].

The variables reviewed in the syngas fermentation process are the yield of cell biomass, ethanol, and acetate. Hence, the analyzes carried out in this study, namely (a) analysis of the concentration of biomass cells during the fermentation process using a spectrophotometer by calculating optical density (OD) at 600 nm , (b) analysis of metabolites formed in the tank medium using High Performance Liquid Chromatography (HPLC), and (c) analysis of the gas composition contained in the reservoir tank headspace using Gas Chromatography (GC).

Result and Discussion

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The Effect of Increasing the Concentration of Trace Metals on Microbial Growth. The modified trace metal components, namely and , became ten times that of the standard trace metal composition, while and increased five times that of the standard composition. The addition of trace metal components was carried out and used simultaneously on the growth medium of C. ljungdahlii. The results of mass production of C. ljungdahlii cells using standard and modified trace metals are shown in Fig.1.

Fig. 1 Comparative curve of C. ljungdahlii cell growth in the medium using standard and modified trace metals.

Fig. 1. Comparative curve of C. ljungdahlii cell growth in the medium using standard and modified trace metals.

The maximum cell mass obtained by microbial growth in the standard and modified trace metal medium was and , which was obtained at the 96th hour. Meanwhile, the maximum growth rate produced by microbial growth in medium with standard and modified trace metal was and , respectively. The inoculum growth curve showed a decrease in the mass cell concentration when the modified trace metal was used in the medium.

Trace metal component has a different effect on the development and production of metabolites in every bacterium, which is determined mainly by the energy efficiency mechanism of the bacteria and the existence at the enzyme's active site. For example, the enzymes that participate in the Wood Ljungdahl pathway are metalloenzymes, whose activity depends on certain metals' presence in the medium.

WLP activity increased with availability because an essential enzyme in WLP, , which is vital for the acetyl-CoA formation, is highly nickel required. Nickel is also found in ase, which catalyzes hydrogen oxidation to generate an equivalent reduction for CO 2 reduction. Thus, there is a high possibility of an increase in activity, resulting in higher reassimilation in the nickel-added medium [26]. However, nickel forms a complex with Cu in the ACS cluster. The inactive ACS ( ) enzyme decreases carbon flow to growth and metabolites, inhibiting biomass and ethanol production [27]. Therefore, obtaining the optimum conditions for adding nickel must be balanced with a review of the concentration of Cu in the medium.

Tungsten (W) and selenium (Se) are components of FDH that function as cofactors, which catalyze the reduction of to formate. For example, in C. ragsdalei, increasing the concentration of tungstate and selenite can optimize FDH activity [28]. Likewise, aldehyde ferredoxin oxidoreductase (AFOR) requires tungsten as a cofactor to catalyze the conversion of acids to aldehydes, utilizing the electrons supplied by reduced ferredoxin. Tungsten has a major impact on the ethanol production of C. ljungdahlii [24].

Zinc has shown a variable effect on various bacterial strains. The effect of Zn on growth and metabolite synthesis depends on the concentration available. A small amount of Zn , which is required for growth, is readily available by the inoculum [24].

The effect and the optimum concentration of trace metals on acetogens vary across acetogenic strains. The optimum concentration of trace metal in C. ragsdelei used by Saxena and Tanner [18] does not necessarily produce optimum results in C. ljungdahlii. The saturation level of the medium with these minor metals impacts the overall growth or metabolite production. The simultaneous increase in trace metals in a medium does not necessarily increase biomass growth. Excessive and simultaneous utilisation of trace metals can inhibit growth [23], [24]. In this study, Wolin's standard trace metal formulation gave the best results for the rapid growth of C. ljungdahlii microbes to be used as an inoculum in the syngas fermentation medium.

Effect of Increasing Concentration of Macronutrient Medium on Microbial Growth. The yield of C. ljungdahlii cell mass on standard and modified macronutrient medium is shown in Fig.2.

The trace element used is the standard trace element. The maximum cell mass obtained by microbial growth on standard and modified macronutrient medium was (96th hour) and (48th hour, respectively). Meanwhile, the maximum growth rate produced by microbial growth on standard and modified macronutrient medium was and , respectively. A modified macronutrient medium was able to increase the yield of microbial cell mass.

Fig. 2 Comparison curve of C. ljungdahlii cell growth in the medium using standard and modified macronutrient medium.

Fig. 2. Comparison curve of C. ljungdahlii cell growth in the medium using standard and modified macronutrient medium.

When grown in a medium, bacteria should adapt to the nutrient composition in the medium, synthesizing the essential amino acids, growth factors, and enzymes. The modified macronutrient medium allows cells to adapt to the environment so that they can go through the lag phase better and quickly enter the exponential growth phase. The rapid multiplication causes the cell mass and density to rise exponentially over time. The lag phase and the exponential growth phase of the inoculum occurred within 24 hours.

The maximum growth rate of microbes in a modified macronutrient medium is higher than the maximum growth rate in a standard medium due to fructose, magnesium in , and sodium in components added to the medium.

Carbohydrates as a carbon source provide an easily accessible and non-toxic substrate. The addition of fructose was doubled compared to the composition of the standard medium, causing an increase in biomass growth. Growth with fructose substrates allows acetogens to conserve more energy than organisms without WLP.

Rolfe [16] states that magnesium and sodium have a special role during the exponential growth phase. MgtA, corA, and mgtBC are transcriptional genes encoding for magnesium transport. MgtA was responsible for increased magnesium concentration during the exponential phase and showed high expression during the initial lag phase. The corA gene was most abundant during the stationary phase, while mgtBC was most abundant during the early lag phase. Meanwhile, the gene sequence of the sodium/proton nhaA transporter was expressed the highest during the mid-exponential growth, correlated with the highest level of sodium ions. The increase in the sodium ion level inside the cell can balance the increase in the external proton concentration due to the increased proton-motive force through exponential growth. Thus, increasing the concentration of magnesium and sodium as macronutrients in the modified medium could optimize the gene expression of mgtA , cor , and proton nhaA, which affected increasing biomass growth by 1.4 times compared to the standard medium.

Effect of Using the Modified Medium on Syngas Fermentation.

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Maximum Mass Cell Concentration. Inoculum transfer from a complex medium (with a carbon source of fructose) to a minimal fermentation medium (with a carbon source of synthesis gas with low solubility in liquid medium) extended the lag phase. The lag phase follows inoculation and
is a period of cell adaptation to the new environment. Microorganisms rearrange their molecular elements when relocated to a new medium. During this phase, there is only a slight increase in cell mass, with no increase in cell density. Both biomass growth curves in the synthesis gas fermentation medium show it (Fig. 3a and Fig.3b). Syngas fermentation using a standard medium was only carried out in 240 hours because biomass, acetate, and ethanol concentration had decreased.

Fig. 3 Syngas fermentation product curve using (a) standard fermentation medium (b)modified fermentation medium

Fig. 3. Syngas fermentation product curve using (a) standard fermentation medium (b)modified fermentation medium

Fermentation in the modified medium experienced a fast exponential phase in 24 hours. However, the growth started slowing down at 48 hours, and at 72 hours, the growth became very slow (Fig.3b). As growth slowed down, nutrient depletion occurred, and ethanol began to accumulate. At this time, the cell also undergoes lysis, so the available cell mass decreases.

At 240 hours, the cell then undergoes a second growth phase. Cells can grow on the lysis products of lysed cells (occult growth). However, at that time, the ethanol concentration decreased. The possibility is caused by using ethanol as a growth substrate for growing cells. The Wood Ljungdahl Pathway allowed acetogens to access many growth substrates such as lactate or ethanol, which are products of many anaerobic fermentations. Both compounds are common in anaerobic environments and are unusable to many other organisms as carbon and energy sources [26].

Cellular regulation changes when the concentration of specific metabolites (carbon, nitrogen, phosphate) is limited. Thus, the cause of growth cessation in syngas fermentation may be due to depletion of essential nutrients or accumulation of inhibitory products. The growth rate will slow if producing inhibitory products accumulate in the medium, and the growth will stop at a certain inhibitory concentration level. In order to produce further cell growth, when the amount of ethanol has not increased, ethanol can be diluted with new media so that the high ethanol concentration does not interfere with cell growth. Before adding the new medium to the cells, harvest ethanol [14]. However, the composition of the medium used in the inoculum did not affect the initial concentration of cells in the fermenter.

Maximum Acetic Acid Concentration. The highest acetate yield resulted from synthesis gas fermentation on day 3 on the standard medium, which was . The maximum acetate gain on the modified medium was on day 20. Acetate concentration increases with cell concentration during the syngas fermentation process, indicating that acetate is a growth-associated product. Because acetate is the most abundant fermentative product, it can lower the pH . In the presence of ions, the acid produced is undissociated, can penetrate the cell wall, accumulates in the cell, and lowers the internal pH . [29]. The metabolism shifts from the acetogenic phase at neutral pH to the solventogenic phase at acidic pH due to this process. It happened in synthetic gas fermentation with a standard medium, but in the modified medium, the pH of the medium did not decrease; it remained above 6, even higher. It caused the addition of , which acts as a buffer solution, so the pH of the medium stays at the initial conditions.

Maximum Ethanol Concentration. The highest yield of ethanol resulted from synthesis gas fermentation on day 2 on standard medium, amounting to , while the maximum ethanol gain on the modified medium was on day 10 . On the ethanol production curve in the modified medium on days 10 to 12 , the ethanol concentration decreased while the biomass concentration increased. It is probably due to the consumption of ethanol for use as a growth substrate [26].

The higher concentrations of magnesium and sodium than those used in the standard medium did not cause the production of acetic acid or ethanol to increase significantly. It is most likely due to the limited carbon source in the medium due to the use of syngas as a carbon source so that cellular regulation also changes, in contrast to when the carbon source used is fructose.

Conclusion

The original version of this paper is available on https://www.scientific.net/EC.1.21.pdf

This study examines the modified medium's effect on the growth of C. ljungdahlii inoculum. The concentration of the macronutrients: fructose, , and the micronutrients of trace metal , was modified. The results showed that the medium with the modification of trace metal micronutrient components did not increase mass cell gain compared to the gain in standard medium. Meanwhile, modification of macronutrient medium concentration increased mass cell gain in the inoculum. However, when the modified macronutrient medium was used as the syngas fermentation medium, the yield of biomass, acetate, and ethanol decreased compared to that of the standard medium. Therefore, further research to increase ethanol production from syngas fermentation needs to be carried out.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/EC.1.21.pdf

The research fund Funding Assistance supported this research for State Universities Legal Entities (BP-PTNBH) Indonesian, Ministry of Research and Technology/National Research and Innovation Agency (BRIN) 2021 (PN-1) with contract number 2/E1/KP.PTNBH/2021.

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Engineering Chemistry, ISSN: 2813-6535, Vol. 1, pp 31-37

doi: 10.4028/p-j7p864

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-08-22

Revised:
2022-10-31

Accepted:
2022-11-10

Online:
2023-03-30

Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

The original version of this paper is available on https://www.scientific.net/EC.1.31.pdf

Budi Husodo Bisowarno , Putri Ramadhany and Tjioe Gerry Sebastian Wibowo

Department of Chemical Engineering, Faculty of Industrial Techonology, Parahyangan Catholic University, West Java, Indonesia

Keywords: Dimetil Eter, Reactive Distillation, Data Driven Soft Sensors, Principal Component Analysis, Partial Least Squares Regression

Abstract

DME production by methanol dehydration using reactive distillation has a lot of potentials. However, the DME purity and methanol conversion is hard to be controlled and need inferential variable to be controlled. Data driven soft sensors can be utilised to select inferential variables, which can be used to control DME production by using reactive distillation. The data was collected from process simulation using ASPEN and analyzed by using PCA (Principal Component Analysis) and PLSR (Partial Least Squares Regression). The results show that based on the data driven soft sensors method, the DME purity can be controlled by using T4 as an inferential variable and ratio reflux as the manipulated variable. However, the methanol conversion is hard to be controlled because the potential inferential temperature was not significantly affected by reflux ratio and reboiler duty as the candidate manipulated variables.

Introduction

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Fuel is one of the most important aspects of our life, in 2020 Indonesia needed almost 8,02 million ton LPG (Liquefied Petroleum Gas) in which of it was still fulfilled by imports. One of the alternative of LPG is DME (Dimetil Eter) which is dubbed to be more efficient than LPG and environmentally friendly [1]. Beside its use as LPG alternative, DME can also be used as Diesel fuel alternative. DME can be produced by direct or indirect reaction. The direct reaction is a reaction in which the syngas formation process can be produced through the coal/biomass gasification process or the partial oxidation of natural gas, which is then converted into DME. Indirect reaction is a reaction in which the process begins with the manufacture of MeOH or methanol, followed by dehydration of MeOH in a separate reactor for producing DME [2].

In this research, it will be focused on the indirect reaction by using reactive distillation. The methanol dehydration can be shown in Eq. 1. Reactive distillation has advantages as reducing operational cost as the reaction and separation process will be done in single equipment [3]. In addition, reactive distillation also can increase the reactan conversion and selectivity [4]. However, the reactive distillation should be tighly controlled in term of the product purity and reactan conversion. These disadvantages can be solved by using data driven soft sensor.

Data driven soft sensors or black box model is a model based on measuring data on industrial processes. In the modeling procedure the relationship between inputs and outputs data of the factory can be emphasized while advanced process knowledge can be ignored. The use of soft sensors with the help of data is useful for extracting important information behind the data, so as to build better soft sensors performance. A wide variety of artificial intelligence and machine learning techniques provide a powerful modeling toolbox for black box models. Besides, the statistical method can also used to provide black box models [5]. This research utilized statistical method to make data driven soft sensors by using PCA (Principal Component Analysis) and PLSR (Partial Least Squares Regression).

Experimental Procedure

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The research was conducted by analyzing the data generated from simulation using ASPEN Plus and ASPEN Plus Dynamic®. The steady state simulation model was based on the simulation thas has been conducted by Bildea and the scheme can be shown in Fig. 1. The dynamic simulation data was collected by changing the value of input variables, which were reflux ratio and reboiler duty from the initial values. The selection of these two input variables as manipulated variables for controlling the DME purity and methanol conversion with inferential temperatures T5 and T47 was based on the research by Wahid and Putra [6].

Fig. 1 Simulation scheme of DME production using reactive distillation [7]

Fig. 1. Simulation scheme of DME production using reactive distillation [7]

The collected data was then analyed by using PCA and PLSR method in The Unscambler X software. The first statistical analysis was used to analyze the influence of temperatures stages to DME purity and methanol conversion. The second statistical analysis was used to analyze the influence of input variables (reflux ration and reboiler duty) to temperature stages especially temperatures that have significant influence to DME purity and methanol conversion.

Results and Discussion

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Process Simulation. Based on the steady state simulation process, the results obtained are in accordance with the literature where for the existing flow rate specifications are shown in Table 1. Based on the dynamic simulation, if the the reflux ratio increased, the purity of DME increased while distillate flow rate and temperature profile decreased. If the reboiler duty increased, the purity of DME decreased while distillate flow rate, methanol conversion, and temperature profile decreased. The opposite happened if the two variables were decreased.

Table 1. Steady state process simulation results

Parameter / [Unit] | Feed | Distillate | Bottom Product
Total flow rate / [kmol/h] | 546 | 273 | 273
Methanol flow rate / [kmol/h] | 543,27 | 2,73 | 4,41 x 10⁻⁶
Water flow rate / [kmol/h] | 2,73 | 1,33 x 10⁻⁵ | 273
DME flow rate / [kmol/h] | 0 | 270,27 | 5,46 x 10⁻¹²

PCA Method on DME Purity and Methanol Conversion Values. Based on the descriptive statistics results that can be shown in Fig. 2, for simulation range methanol conversion has wider range than DME purity with the minimum value of DME purity is and minimum value of methanol conversion is . The total data analyzed were 25,896 data.

Fig. 2. Descriptive statistics result of DME purity and methanol conversion

Fig. 2. Descriptive statistics result of DME purity and methanol conversion

Based on the correlations loadings as shown in Fig. 3, the DME purity has negative correlation with methanol conversion.

Fig. 3 Correlation loadings between DME purity and methanol conversion

Fig. 3. Correlation loadings between DME purity and methanol conversion

The potentially outliers data was analysed by using score plot and influence plot that can be seen in Fig. 4. Based on the the score plot, the potential outliers are marked by black color. However in the influence plot those data only have high residual value and low leverage value, because of it the data can be assumed not outliers.

Fig. 4 Score plot and influence plot of DME purity and methanol conversion

Fig. 4. Score plot and influence plot of DME purity and methanol conversion

Influence of Temperatures Stage to DME Purity and Methanol Conversion. The correlation loadings of stage temperatures as input data and DME purity and methanol conversion as output data that can be shown in Fig. 5. Based on the correlation loadings, the DME purity has negative correlation with the stage temperatures in general while the methanol conversion has positive correlation with the stage temperatures in general.

Fig. 5 Correlation loadings of stage temperature with DME purity and methanol conversion

Fig. 5. Correlation loadings of stage temperature with DME purity and methanol conversion

Fig. 6 Weighted regression coefficients distribution of influence of stage temperatures to DME purity

Fig. 6. Weighted regression coefficients distribution of influence of stage temperatures to DME purity

The weighted regression coefficients distribution for influence of stage temperatures to DME purity can be shown in Fig. 6 while influence of stage temperatures to methanol conversion can be shown in Fig. 7. Based on the Fig. 6, the stage temperatures that have significant influence on DME purity sequentially are T4, T3, T45 (positive correlation) and T51 (negative correlation). While based on Fig. 7, the stage temperatures that have significant influence on methanol conversion sequentially are T54, T53, T52 (negative correlation).

Fig. 7 Weighted regression coefficients distribution of influence of stage temperatures to methanol conversion

Fig. 7. Weighted regression coefficients distribution of influence of stage temperatures to methanol conversion

PCA Method on Stage Temperatures. Based on the descriptive statistics results that can be shown in Fig. 8. For simulation range the stage temperatures that has wide range are temperatures from rectifying section. The stage temperatures from stripping section have slightly change but the stage temperatures from reaction section didn't change and can be assumed stable.

Fig. 8 Descriptive statistics result of stage temperatures

Fig. 8. Descriptive statistics result of stage temperatures

Fig. 9 Correlation loadings of stage temperatures

Fig. 9. Correlation loadings of stage temperatures

Based on the correlations loadings that can be shown in Fig. 9, the stage temperatures have positive correlation with each other in general.

The data that are potentially outliers were analyzed using score plot and influence plot as shown in Fig. 10. Based on the the score plot, data that is potentially be an outliers are marked by black color. However in the influence plot those data only have high leverage value and low residual value, because of it the data can be assumed not outliers.

Fig. 10 Score plot and influence plot of stage temperatures

Fig. 10. Score plot and influence plot of stage temperatures

Influence of Reflux Ratio and Reboiler Duty to Stage Temperatures. The correlation loadings of reflux ratio and reboiler duty as input data and stage temperatures as output data can be seen in Fig. 11. Based on the correlation loadings, the reflux ratio has negative correlation with the stage temperatures in general while the reboiler duty has positive correlation with the stage temperatures in general.

Fig. 11 Correlation loadings of reflux ratio and reboiler duty with stage temperatures

Fig. 11. Correlation loadings of reflux ratio and reboiler duty with stage temperatures

Fig. 12 Weighted regression coefficients distribution of influence of ratio reflux to stage temperatures

Fig. 12. Weighted regression coefficients distribution of influence of ratio reflux to stage temperatures

Fig. 13 Weighted regression coefficients distribution of influence of reboiler duty to stage temperatures

Fig. 13. Weighted regression coefficients distribution of influence of reboiler duty to stage temperatures

The weighted regression coefficients distribution for influence of reflux ratio to stage temperatures can be shown in Fig. 12 while influence of reboiler duty to stage temperatures can be shown in Fig. 13. Based on the Fig. 12, the stage temperatures that significantly influenced by reflux ratio
sequentially are T5, T6, T7, and T4. While based on the Fig. 13, the stage temperatures that significantly influenced by reboiler duty sequentially are T5, T4, T6.

Conclusions

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From this research, it could be concluded that based on data driven soft sensors method, the DME purity can be controlled by controlling temperature on stage 4 as the inferential temperature and reflux ratio as the manipulated variable. While based on the data driven soft sensors method, the methanol conversion is difficult to controlled. This is because of the temperature that significantly affect methanol conversion are temperature on stage 52, 53, and 54, which are not significantly affected by either manipulated variables of reflux ratio or reboiler duty.

References

The original version of this paper is available on https://www.scientific.net/EC.1.31.pdf

Information on https://www.esdm.go.id/id/berita-unit/direktorat-jenderal-minyak-dan-gas-bumi/ dme-alternatif-pengganti-lpg

A. Chumaidi, D. Moentamaria, A. Murdani, Dehidrasi Metanol Menjadi Dimetil Eter Dengan Memodifikasi Katasis (2016) 8.

V. Sakhre, U. P. Singh, Reactive Distillation: Advanced Control Using Neural Networks (2019)

A. Gorak, E. Sorensen, Distillation, (2014)

W. Yan, D. Tang, Y. Lin, A data-driven soft sensor modeling method based on deep learning and its application., IEEE Transactions on Industrial Electronics, 64 (2017) 4237-4245

A. Wahid, I. G. E. P. Putra, Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production., IOP Conference Series: Materials Science and Engineering, 334 (2018)

C. S. Bildea, R. Grorgy, C. C. Brunchi, A. A. Kiss, Optimal design of intensified processes for DME synthesis., Computers and Chemical Engineering, 105 (2017) 142-151

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Engineering Chemistry, ISSN: 2813-6535, Vol. 1, pp 39-44

doi: 10.4028/p-489m23

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-12-12

Accepted:
2022-12-28

Online:
2023-03-30

Peak Wavelength Mapping of Triangular Reflection in Spherical Colloidal Cluster with Icosahedral Symmetry

The original version of this paper is available on https://www.scientific.net/EC.1.39.pdf

Ryosuke Ohnuki , Yukikazu Takeoka and Shinya Yoshioka

Graduate School of Science and Technology, Tokyo University of Science, Yamazaki, Noda 278-8510, Japan

Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

6220701@ed.tus.ac.jp, ytakeoka@chembio.nagoya-u.ac.jp, syoshi@rs.tus.ac.jp

Keywords: Colloidal cluster, Structural color, Icosahedral symmetry

Abstract

Spherical colloidal clusters have various types of particle arrangements. Interestingly, one type has an icosahedron symmetry, characterized by the existence of five-fold axes. When the colloidal particle size is comparable to the wavelength of light, icosahedral colloidal clusters exhibit a unique triangular reflection with a specific wavelength, owing to optical interference. In this paper, we report the results of a detailed optical study on the position-dependent peak wavelength within the triangular region. Based on the map of the peak wavelength and spectral shape, we propose a structural model of the icosahedral colloidal cluster and discuss its formation process.

Introduction

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Colloidal clusters with spherical shapes can be easily produced from water-in-oil-type emulsions by evaporating water . When the size of colloidal particles is comparable to the wavelength of light, the clusters produce brilliant structural colors because of the interference of light. Thus, colloidal clusters have been widely applied in optical materials, such as colorimetric sensors [3-8] and pigments [9-11]. It was recently discovered that spherical colloidal clusters have different particle arrangements depending on the preparation conditions, for example, the number of particles inside micelles and the shrinking speed of micelles. These types include clusters with icosahedral symmetry [12-19], decahedral symmetry [18,19], and an onion-like structure in which layers of the arranged particles are stacked beneath the sphere surface .

We were particularly interested in the icosahedral type. The particle arrangement is characterized by six five-fold axes, which are incompatible with the translational symmetry of the seven crystal system types. In our previous study, we performed an optical investigation of a large icosahedral colloidal cluster [14]. A unique triangular reflection appears on the spherical cluster surface, corresponding to one of the 20 triangles of the icosahedron; when the sample is rotated, a different triangle is reflected under an optical microscope [14,19].

It has been presumed that spherical colloidal clusters with icosahedral symmetry consist of 20 Mackay tetrahedra [23] as shown in Fig. 1(a). The Mackay tetrahedron has a slightly deformed shape, different from that of a regular tetrahedron. One of the four triangles is larger than the others, and this large triangle faces the outside of the colloidal cluster (Fig. 1(b)). The reflected wavelength is sensitive to the distance between the colloidal particle layers. We previously reported that the reflectance spectrum of the triangular reflection has a large peak, and the peak position is reasonably consistent with the wavelength calculated from the Bragg condition, assuming the particle arrangement of the Mackay tetrahedron [14]. However, a thorough inspection revealed that the experimental peak wavelength was slightly longer than the theoretically calculated wavelength. In addition, the peak shape was not observed to be symmetrical but appeared as an additional component at a wavelength longer than the peak [14]. Thus, we further investigated the triangular reflection.

In this study, we investigated the variation in the peak wavelength with the position within the triangular reflection. Reflectance spectra were measured at many positions using microspectroscopy,
and the peak wavelength was mapped. From the resultant map, we propose a modified particle arrangement of a large icosahedral cluster consistent with the observed optical properties.

Method

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Preparation of Spherical Colloid Clusters. The icosahedral colloidal cluster used in this study was the same as that used in our previous study [14]. A summary of the preparation method is presented as follows: a suspension containing 310 nm colloidal particles at a concentration of and hexadecane containing Span 80 was prepared. Hexadecane was presaturated with water to slow down the droplet evaporation. Next, droplets of the suspension in hexadecane, that is, W/O emulsion, were prepared, and the water was slowly evaporated at room temperature (approximately ). Finally, after "three weeks", the samples were washed several times with hexane and dried at room temperature.
Microspectroscopy. To measure the reflectance spectrum of a small region within the triangular reflection of clusters with icosahedral symmetry, we applied microspectroscopy using an optical microscope (Olympus BX51). A xenon lamp was used as the light source, and a objective lens (Olympus, SLMPlan N N.A.= 0.60) was used for observation. An optical fiber with a diameter of 200 was placed at the imaging position of the microscope to direct the reflected light from the microscopic region to the spectrometer (Ocean Optics, USB2000). The measurement area was a spot with a diameter of . Reflectance was calculated as the ratio of the reflection of the sample to that of a diffused reflection standard (Labsphere Spectralon).

Results and Discussion

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A spherical colloidal cluster with icosahedral symmetry can be modeled by combining 20 Mackay tetrahedra, slightly distorted from a regular tetrahedron, as shown in Fig. 1. The icosahedron was truncated to a spherical shape to obtain a realistic model of the colloidal cluster [15,23]. The direction from the center of the icosahedron to the surface triangle center corresponds to the [111] direction of the face-centered cubic (FCC) lattice (Fig. 1(b)). Although the particle arrangement is not exactly the FCC lattice in the Mackay tetrahedron owing to the deformation, the Millar index [111] is often used to refer to the direction because the particle arrangement is hexagonal in the plane of the surface triangle (Fig. 1). The calculated height in this direction was 0.795 when the distance from the center

Figure 1. Model structure of spherical colloidal cluster with icosahedral symmetry. The cluster consists of 20 Mackay tetrahedra, and each Mackay structure is color-coded for identification. One Mackay structure is shifted upward (red grain). (b) Ratio of side and height of Mackay tetrahedron. The light-blue triangle corresponds to the icosahedron surface, of which lengths of the three sides are 1.05 each when the other sides have a length of 1.00 . The height of the Mackay tetrahedron is 0.795 , defined as the length between the center of the blue triangle and the other vertex.

Figure 1. Figure 1. Model structure of spherical colloidal cluster with icosahedral symmetry. The cluster consists of 20 Mackay tetrahedra, and each Mackay structure is color-coded for identification. One Mackay structure is shifted upward (red grain). (b) Ratio of side and height of Mackay tetrahedron. The light-blue triangle corresponds to the icosahedron surface, of which lengths of the three sides are 1.05 each when the other sides have a length of 1.00 . The height of the Mackay tetrahedron is 0.795 , defined as the length between the center of the blue triangle and the other vertex.

of the icosahedron to the vertices on the surface was 1.00 . From this length, the plane distance in the [111] direction of the Mackay tetrahedron, , is expressed as

where is the diameter of the colloidal particles, as the colloidal particles were arranged at the tetrahedron vertices. Based on the size of the colloidal particles used in this study, , and Eq. (1), the plane distance is . The mean refractive index, , of the icosahedral cluster was estimated as

where and are the refractive indices of silica and air, respectively, and is the volume fraction of the Mackay structure. When the refractive indices of silica and air are and , respectively, and the volume fraction is [23], the mean refractive index is from Eq. (2). The wavelength of the reflected light from this direction is expected from the Bragg condition to be

where is the incidence angle on the (111) plane. Substituting the values of and and into Eq. (3) yields .

Keeping this wavelength in mind, we measured the reflectance spectrum at many positions inside the triangular reflection, as shown in Fig. 2(a). For example, the reflectance spectrum near the center of the triangular reflection is shown in Fig. 2(b). The spectrum at most positions shows a large peak with a few small side peaks, and the peak wavelength ranges from approximately 650 to 700 nm . Fig. 2(c) shows the color map obtained from the wavelength of the main reflectance peak.

At all the measurement positions, the experimentally determined wavelength was longer than the calculated value of 649 nm . In addition, the wavelength was longer at the center of the triangle than near the edge, as shown in Fig. 2(c). A possible reason for this variation is the difference in the incidence angle to the (111) plane, as the Bragg condition includes the cosine factor. However, quantitative analysis shows that this effect does not sufficiently explain the wavelength variation. The incident angle to the spherical surface at the edge of the triangle was approximately , which could be estimated from the structural model (Fig. 1(a)). When refraction at the surface is considered using the mean refractive index, it is expected that the incident angle to the (111) plane becomes approximately . The wavelength under the Bragg condition calculated using this angle was

Figure 2. Measurement positions in triangular reflection. The color of the small filled circle at the measurement position indicates the peak wavelength, which corresponds to the color scale in (c). The scale bar is . (b) Typical reflectance spectrum around center of triangular reflection. (c) Map of peak wavelength in triangular region. The scale bar is .

Figure 2. Figure 2. Measurement positions in triangular reflection. The color of the small filled circle at the measurement position indicates the peak wavelength, which corresponds to the color scale in (c). The scale bar is . (b) Typical reflectance spectrum around center of triangular reflection. (c) Map of peak wavelength in triangular region. The scale bar is .

Figure 3. (a) Model of unit structure composed of spherical colloidal cluster with icosahedral symmetry. The inner part of the cluster has the Mackay structure, with the FCC structure on surface part. (b) Schematic of difference in reflectance at different positions of triangular region. Reflections from the FCC structure are indicated by red arrows, and those from the Mackay structure are indicated by orange arrows. The thickness of the arrows indicates the reflection intensity.

Figure 3. Figure 3. (a) Model of unit structure composed of spherical colloidal cluster with icosahedral symmetry. The inner part of the cluster has the Mackay structure, with the FCC structure on surface part. (b) Schematic of difference in reflectance at different positions of triangular region. Reflections from the FCC structure are indicated by red arrows, and those from the Mackay structure are indicated by orange arrows. The thickness of the arrows indicates the reflection intensity.

approximately 20 nm shorter than the wavelength under normal incidence. However, the experimentally observed shift was approximately 50 nm , more than twice the shift expected from a tilted incidence.

We heuristically consider the FCC lattice to explain why the observed peak wavelength is longer than the theoretical wavelength calculated using Eq. (3). Because the Mackay tetrahedron has a shape compressed from the regular tetrahedron in the height direction (Fig. 1(b)), the plane distance is shorter than that of the non-deformed FCC lattice. The plane distance in the [111] direction of the FCC lattice, , is expressed as

From and Eq. (4), the plane distance is . The mean refractive index, , of the FCC lattice was estimated as

where is the volume fraction of the FCC lattice. Using a volume fraction , the mean refractive index is from Eq. (5). The wavelength from the (111) planes of the FCC lattice was calculated from the Bragg condition as

Substituting the values of and and into Eq. (6) yields . This wavelength is approximately 30 nm longer than the wavelength, , calculated for the Mackay structure, which may explain the longer experimentally observed wavelength.

From the above calculation of and the plotted color map shown in Fig. 2, we developed a model structure (Fig. 3). In this model, 20 Mackay tetrahedra form the core of the spherical cluster, and particle arrangements of the FCC lattice occur near the surface. The surface FCC lattice has a different number of colloidal particle layers depending on the position because of the spherical shape of the clusters: the number of layers is higher in the center of the triangle and smaller near the edge.

Figure 4. Reflectance spectra in the triangular region. (a) Example of a reflection peak with a shoulder and (b) example of two reflection peaks. The inset image is an optical micrograph indicating the measurement position (white circle). The scale bar is .

Figure 4. Figure 4. Reflectance spectra in the triangular region. (a) Example of a reflection peak with a shoulder and (b) example of two reflection peaks. The inset image is an optical micrograph indicating the measurement position (white circle). The scale bar is .

This difference affects the magnitude of reflectance at wavelength . Thicker FCC layers result in higher reflectance, whereas a lower reflectance is expected near the edge (Fig. 3(b)). This model expects two contributions in the reflectance spectrum originating from Mackay and FCC structures. The different magnitudes of the contributions explain the variation in the peak wavelength of the triangular reflection because, at the edge of the triangle, the reflection from the Mackay tetrahedron is thought to be dominant at wavelength .

Fig. 4 shows examples of the spectra that support the hypothetical model above. Fig. 4(a) shows the reflectance spectrum observed near the edge of the triangle, which has a peak located at a wavelength close to wavelength calculated from the Mackay structure. However, in-depth examination reveals that there appears to be a spectral component at approximately 690 nm as a shoulder, which can be attributed to reflections from the FCC structure. Fig. 4(b) shows another spectrum observed at a different position, where the two reflection peaks are observed separately. In this case, the wavelength of the largest peak was used for mapping.

The model structure shown in Fig. 3 is still hypothetical; thus, further investigation is required. Although the particle arrangement is expected to be incommensurate in the interfacial plane between the Mackay and FCC structures, this model may be supported by previous studies on the process of colloidal cluster formation. It has been experimentally observed that crystallization commences at the water droplet surface [24]. Based on computer simulations, crystallization is also expected to occur in the interior of the droplet [13,15,25] when the droplet shrinks and the filling fraction of particles increases to approximately 0.50 . Correspondingly, Chen et al. reported that a mismatch in the lattice constant occurs in the interior and surface parts of a cluster [26]. Therefore, the peak wavelength variation observed in this study might indicate that the cluster formation is a competition between the FCC lattice crystallization from the surface and the Mackay structure formation in the interior.

Summary and Conclusion

The original version of this paper is available on https://www.scientific.net/EC.1.39.pdf

In conclusion, we investigated triangular reflection in spherical colloidal clusters with icosahedral symmetry by mapping the wavelength of the peak reflectance. The peak wavelength distribution and reflectance spectra shape were interpreted using a model structure combining the Mackay tetrahedron and FCC lattice. This model is consistent with those of previous studies on the formation mechanism of spherical colloidal clusters. The results of this study are expected to provide a clue to the formation process of spherical colloidal clusters into the icosahedral structure.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/EC.1.39.pdf

This study was supported by JSPS KAKENHI (Grant Number 22J12152).

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The original version of this paper is available on https://www.scientific.net/EC.1.39.pdf

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

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Engineering Chemistry, ISSN: 2813-6535, Vol. 1, pp 45-56

doi: 10.4028/p-6oe21m

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-12-28

Revised:
2023-01-14

Accepted:
2023-01-16

Online:
2023-03-30

Extraction Technology for Manganese Sulfate Solution Purification

The original version of this paper is available on https://www.scientific.net/EC.1.45.pdf

Chenzhe , Hongyan Xie , Huixin Jin

Guizhou University, College of Material and Metallurgy, Guiyang, China

Guizhou Province Key Laboratory of Metallurgical Engineering and Process Energy Saving, Guiyang, China

State Key Laboratory of Pressure Hydrometallurgical Technology of Associated Nonferrous Metal Resources, Kunming, China

Chenzhe2022@163.com, *Corresponding author e-mail: hyxie@gzu.edu.cn, HuixinJin2022@163.com

Keywords: Manganese Sulfate, Removal Technology, Manganese Ion.

Abstract

Manganese sulfate is an important base manganese salt; nearly of the world's manganese products are produced using manganese sulfate or manganese sulfate solution. Furthermore, manganese sulfate has many applications in industry and agriculture; thus, manganese sulfate solution impurity removal technology is important. This study aims to remove impurities from manganese sulfate solution -using complex low-grade manganese ore and manganese-rich fumes after pressure acid leaching to obtain a manganese sulfate solution, which is then purified through iron removal by oxidation neutralization; finally, the purified liquid is treated using extraction-backextraction. We investigated the effects of various extraction parameters on the extraction rate of manganese, as well as the effects of various back-extraction parameters on the manganese backextraction rate and manganese ion concentration in the back-extraction solution, and studied the extraction-back-extraction process. We found that the extraction and back-extraction rates of manganese could reach and , respectively, under optimal conditions.

Introduction

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Manganese sulfate is a water-soluble salt [1]and an indispensable intermediate product in the production of other manganese oxides, electrolytic manganese products [2], lithium manganate cathode material [3], and manganese salts. Consequently, it plays an important role in manganese series products [4]. At present, China's manganese sulfate is primarily obtained by leaching manganese ore with sulfuric acid and then removing impurities, the purpose of impurity removal is to remove iron, calcium, magnesium and other heavy metals and other impurities [5]. However, there are few studies on the deep removal of iron, calcium, magnesium and heavy metals from manganese sulfate solutions [6-8], most of the studies focusing on the purification of some elements, such as the removal of nickel and cobalt using sulfides, the removal of calcium and magnesium using fluorides [9-10], and the extraction and separation of manganese from calcium, magnesium, potassium, sodium, etc. [11].

Depending on the type and content of impurity elements in a manganese sulfate solution, different impurity removal methods can be used, including flocculation precipitation, chemical precipitation, extraction or ion exchange methods, electrolysis, metal manganese replacement, and combinations of a variety of purification methods.

Among them, the chemical precipitation method [12]is relatively mature and the most widely used impurity removal method currently. However, when the precipitation reaction occurs, there may be mutual influence-that is, new compounds that exist stably in the solution may be formed between different ions, resulting in little to no precipitation or incomplete precipitation being achieved.

The solvent extraction method has the advantages of simple equipment, simple operation, the ease of realizing continuous, automatic control, and high recovery rates [13]. However, the extraction method usually extracts other impurity ions too, so that the removal of impurities is incomplete, making this method only suitable for the preliminary separation and purification of manganese
sulfate. If the impurity ions in the solution are to be completely removed, other purification methods need to supplement the process.

The manganese replacement method [14] does not introduce other impurities into the solution, but manganese can only replace less active than its metal ions-that is, non-metallic impurities, such as silicon and phosphorus cannot be removed at all, making the removal of impurities insufficiently thorough.

The combined purification process combines chemical precipitation [15], electrolysis, and other techniques, which can generally obtain better results, but such combined methods are more complicated, inconvenient to operate, and more expensive.

The primary reasons for using the extraction-back-extraction technique to treat manganese sulfate solutions in this paper are as follows:
(1) Deep impurity removal from manganese sulfate solution. Although manganese sulfate solutions can be treated using several purification and impurity removal techniques-such as oxidation and concentration-a small quantity of metal impurities will remain in the purified solution, which will need to be removed. Because P204 is an acidic phosphorus extractant, it is also one of four types of extractant commonly used in China [16]. Its water solubility is small, its properties are stable, its extraction ability is strong, and its phase separation effects are good. Consequently, P204 was selected as the extractant for this study;
(2) Instead of using the traditional evaporation and concentration technique, a lower concentration of manganese sulfate solution was produced to reach the manganese ion concentration required by electrolytic manganese. Due to the large area, high energy consumption, and disturbing environmental pollution of the traditional evaporation and concentration technique, the extraction and back-extraction technology was adopted in this study to replace the previous evaporation and concentration process. As such, a concentration process for low-concentration manganese sulfate solution with low cost, low energy consumption, and environmentally friendly outcomes could be realized [17].

Instruments and Reagents

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Instruments: Electronic balance, acidity meter, separatory funnel, Korn oscillator, beaker, measuring cylinder, dropper, pipette.

Reagents: P204, commercial kerosene, sulfuric acid, sodium hydroxide.
In this experiment, the extraction dope is the purification liquid for leaching manganese sulfate solutions under pressure. Since this solution is obtained after purification and impurity removal, the influence of impurities on the extraction rate of manganese in the extraction process can be ignored. The chemical composition of extraction dope is shown in Table 1. The process flow chart is shown in Fig. 1.

Table 1 The chemical composition of extraction dope.

Component | Mn | Fe | Al | Ca | Mg | Co | Ni | Cu | H₂SO₄
Content(g/L) | 21.20 | 0.000387 | 0.00016 | 0.06 | 0.21 | 0.00023 | 0.00046 | 0.00007 | 0.46

Fig. 1 (a) Process flow chart of extraction process of manganese sulfate purification solution

Fig. 1. (a) Process flow chart of extraction process of manganese sulfate purification solution

Fig. 1 (b) Process flow chart of manganese sulfate purification liquid extraction equipment

Fig. 1. (b) Process flow chart of manganese sulfate purification liquid extraction equipment

Results: Extraction

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Effect of Saponification Rate of P204 on Manganese Extraction

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Different saponification rates of P204 had different influences on the extraction efficiency of manganese. Firstly, different volumes of P204 were saponified at different saponification rates. Next, a pressure leaching purification solution containing was extracted using P204 (with different saponification rates). The content of in the raffinate was determined and the manganese extraction rate was calculated. Other experimental conditions were as follows: the phase ratio ( ) was , the concentration of P 204 was , the extraction temperature was , the extraction stage was level 1 , and the extraction time was 5 min . The test results are shown in Fig. 2.

Fig. 2 Effect of the P204 saponification rate on the extraction of manganese

Fig. 2. Effect of the P204 saponification rate on the extraction of manganese

It can be seen from Fig. 2, that when the P204 saponification rate was , the manganese extraction rate was only . When the P204 saponification rate was , the manganese extraction rate increased to , after which there was no obvious increase in the manganese extraction rate as the P204 saponification rate increased further. The results shown that excellent manganese extraction could be obtained with a P204 saponification rate of .

Determination of Saturation Capacity of P204 Extracted Manganese

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Before the extraction experiment, the saturation capacity of manganese in its organic phase was measured, at different concentrations of P 204 . The experiment was conducted at , and the ratio of organic phase to the aqueous phase ( ) was 1:1. The extraction was repeated several times until the concentration of manganese ions in the aqueous phase no longer changed . The aqueous phase resulted in a purification solution for leaching manganese sulfate solution under pressure, the manganese ion concentration being . The test results are shown in Fig. 3.

Fig. 3 Saturation capacity of manganese in the organic phase

Fig. 3. Saturation capacity of manganese in the organic phase

Based on Fig. 3, the content of manganese in the organic phase increased linearly with P204 concentration-that is, each concentration of P204 could extract of manganese-indicating that the extractant P204 could stabilize the complexation of manganese ions, that is, the number of molecules of P204 complex extractant per manganese ion was stable. However, it was found that phase separation in the manganese extraction test process was slow when the volume fraction of P204 was greater than . Consequently, the volume fraction of P204 was set to be in the extraction test process in this study.

Effect of Extraction Phase Ratio (O/A) on Manganese Extraction

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The extraction phase ratio ( ) is the volume ratio of the P 204 -kerosene mixture to the extracted purified solution containing manganese via pressure leaching, and was used to examine the maximum extraction capacity of different extractant concentrations. We set the extraction phase ratio to be and , respectively, to examine the effect of the extraction phase ratio on the extraction of manganese. Other experimental conditions were as follows: the concentration of P204 was , the saponification rate was , the extraction equilibrium time was 5 min , the extraction temperature was , and the extraction stage of was level 1. The test results are shown in Fig. 4.

Fig. 4 Effect of extraction phase ratio on the extraction of manganese

Fig. 4. Effect of extraction phase ratio on the extraction of manganese

It can be seen from the Fig.4, that when the phase ratio of ( ) was 1:2, the extraction rate of manganese was just , as the manganese content in the purification solution exceeded the extraction capacity of the P204-kerosene solution. However, the extraction rate reached more than when the extraction phase ratio was more than 1:1, although the extraction efficiency was not significantly increased by further increases in the extraction phase ratio. Theoretically, the extraction rate should increase with increasing phase ratio, but the increasing phase ratio also increases the use of the extraction agent P204, leading to increasing production costs [19]. After full consideration, the extraction phase ratio (O/A) was set to be 1:1.

Effect of Extractant Concentration on Manganese Extraction

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We investigated the effects of different P204 concentrations on the extraction rate of manganese. Consequently, we set the P204 concentration to be , and , respectively. Other experimental conditions were as follows: the P204 saponification rate was , the extraction equilibrium time was 5 min , the phase ratio was , the extraction temperature was , and the extraction stage was level 1. The test results are shown in Fig. 5.

Fig. 5 Effect of extractant concentration and temperature on extraction of manganese

Fig. 5. Effect of extractant concentration and temperature on extraction of manganese

It can be seen from Fig. 5 (red line), that when the concentration of P204 was 20%, the extraction rate of manganese was just . However, when the concentration of P204 increased to , the extraction rate increased to . Similarly, when the concentration was , the extraction rate rose to . On the basis of the above results, it was more than reasonable to set the concentration of extraction agent P204 to be .

Effect of Extraction Temperature on Manganese Extraction

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The experimental conditions were as follows: the organic phase of P204 was and of sulfonated kerosene, , the extraction phase ratio ( ) was , the extraction equilibrium time was 5 min , the extraction equilibrium pH was , and the P 204 saponification rate was . The experimental results are shown in Fig. 5.

It can be seen from Fig. 5 (violet line) that with the increasing extraction temperature, the manganese extraction rate increased initially before decreasing slowly. When the extraction temperature was between and , the extraction rate was better. However, when the extraction temperature was higher than , the extraction rate showed a downward trend. Since the ambient temperature was rarely greater than , although the extraction rate increased slightly after the increase in temperature, the volatilization of the organic phase increased too, resulting in an increase in the consumption of extractant. Therefore, the optimal manganese extraction temperature was set to be .

Effect of Extraction Equilibrium pH on Manganese Extraction

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The experimental conditions were as follows: the organic phase of P204 was and of sulfonated kerosene, , the extraction phase ratio ( ) was , the extraction equilibrium time was 5 min , the extraction temperature was , the P204 saponification rate was . During the test, different volumes of sulfuric acid solution and sodium hydroxide solution were added to control the terminal pH value. The test results are shown in Fig. 6.

Fig. 6 Effect of pH and extraction time on extraction of manganese

Fig. 6. Effect of pH and extraction time on extraction of manganese

It can be seen from Fig. 6 (blue line) that the extraction equilibrium pH exhibited a significant influence on the manganese extraction rate, the manganese extraction rate increasing with increasing extraction equilibrium pH . When the pH of the solution was maintained at more than 3 during the extraction process, the extraction rate of manganese was greater than , the concentration of manganese ions in the raffinate being less than . When the extraction equilibria pH was greater than 4 , the manganese extraction rate increased slightly and when it was approximately 5 , practically all of the manganese ions in the solution were extracted. Consequently, the pH of the purified manganese sulfate solution was set to be the most suitable extraction equilibrium pH .

Effect of Extraction Equilibrium Time on Manganese Extraction

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The extraction equilibrium time also has an important effect on the extraction efficiency of manganese, and the appropriate extractant should be efficient and fast. In this experiment, the extraction times were set to be and 9 min , respectively, to examine the influence of the extraction equilibrium time on the manganese extraction rate. Other experimental conditions were as follows: the organic phase of P204 was and of sulfonated kerosene, , the extraction phase ratio was , the extraction equilibrium pH was , the extraction temperature was , and the P204 saponification rate was . The test results are shown in Fig. 6.

It can be seen from Fig. 6 (orange line) that the manganese extraction rate increased with the increasing extraction equilibrium time. However, when the extraction equilibrium time exceeded 5 min , the manganese extraction rate did not change significantly. Considering potential errors in the test process and test results, it was considered that the manganese extraction rate exhibited practically no difference. During the extraction process, if the oscillation time was too long, the organic and the aqueous phase would form an emulsion, making phase separation difficult [20]. Knowing that a long extraction equilibrium time in industrial production would reduce production efficiency, and the fact that full mixing of the organic and aqueous phases had been completely realized after an extraction time of 5 min , it was clear that extraction equilibrium had been reached. Consequently, the extraction equilibrium time was set to be 5 min .

Drawing of Extraction Isotherm and Determination of Extraction Stage

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A curve indicating the change of metal concentration in the organic phase and the aqueous phase is generally referred to as an extraction (or back-extraction) isotherm [21]. The position of the isotherm is related to the concentration of the extractant, the shape of the isotherm changing with changes of the extraction system-that is, the location of the extraction isotherm for the same extractant with different concentrations changes, but it shape remains much the same.

There are two methods for drawing extraction isotherms [22]:
(1) The determination of phase radio method: here, the concentration of the organic phase, the concentration of the feed solution, the extraction temperature, and the extraction equilibrium pH used during the test must remain the same. Due to differences in the phase ratio, the pH after extraction equilibrium may be different. In this case, it may be necessary to adjust the equilibrium pH with acid or alkali and then re-contact until the pH of the raffinate of each sample is the same. Based on the specified phase ratio, the organic and the aqueous phase are loaded into the separation funnel, and then oscillated on the Kane oscillator to equilibrium-that is, where the pH of the raffinate phase ratio can be measured directly, with the pH being constant as the criterion. After phase separation, the metal content is analyzed in the two phases, making sure to avoid entrainment before sampling and analysis. The aqueous phase analysis sample should be filtered to remove the entrained organic phase. After the loaded organic phase is washed with distilled water, the separated phase is discharged, and the metal content in the organic phase can be analyzed after separation by centrifuge.
(2) In this method, first determine an appropriate phase ratio, the organic and the aqueous phases in this phase ratio reacting and reaching an equilibrium. Then, after two phase separations to release the aqueous phase, different volume of organic and aqueous phase can be analysed for their metal content. Next, based on the original phase ratio, a new aqueous phase is added to the separator funnel to react with the remaining organic phase. When the two phases reach equilibrium again, a sample analysis can be conducted as before. This process is repeated several times until the metal load in the
organic phase reaches saturation, the extraction equilibrium pH being maintained each time. Finally, the extraction isotherm can be plotted by plotting the analysis results of each group on a graph with the concentration of the aqueous phase metal on the X-axis and the concentration of the organic phase metal on the Y -axis.

If there is still a large volume of extractant in the raffinate after the first extraction stage, it would be necessary to conduct multi-stage extraction treatment to achieve the maximum extractant extraction. After the optimized selection of a series of factors, such as the saponification rate of the P204-kerosene extraction system, the extraction equilibrium time, the extraction phase ratio, the concentration of P 204 , the extraction temperature, and the extraction equilibrium pH , an extraction isotherm could be under the following conditions: the organic phase consisted of P204 and sulfonated kerosene, the phase ratio ( ) was , the pH of the solution was between 5 and 6 , the extraction equilibrium time was 5 min , the extraction temperature was , and the saponification rate was . The simplest method to estimate the number of extraction stages required for multistage extraction is the McCabe-Thiele diagram method. The extraction isotherm of manganese drawn based the test results is shown in Fig. 7.

Fig. 7 Manganese extraction isotherm using P204

Fig. 7. Manganese extraction isotherm using P204

It can be seen from Fig. 7 that for the manganese sulfate purification solution with a manganese ion concentration of under pressure leaching, using the organic phase-which was of P204 and of sulfonated kerosene-and where O/A was 1 , the manganese extraction rate could reach more than , the concentration of manganese ions in the raffinate being less than , to achieve the maximum recovery of . However, an increasing number of extraction stages would undoubtedly increase industrial production costs. Consequently, the extraction stage for this experiment was set to be level 1 .

Results: Back-extraction

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The back-extraction solution was prepared with analytically pure H 2 SO 4 and distilled water, the volume fraction of P204 in the loaded organic phase was 40%, containing of manganese.

Effect of Back-Extraction Acidity on Manganese Back-Extraction Rate

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The experimental conditions were as follows: the volume fraction of P204 in the loaded organic phase was , the back-extraction phase ratio ( ) was 2:1, the back-extraction temperature was , and the mixing time of the back-extraction was 3 min . We examined the changes in the backextraction rate of manganese under different back-extraction acidity. The test results are shown in Fig. 8.

Fig. 8 The effect of the stripping solution acidity on the manganese back-extraction rate

Fig. 8. The effect of the stripping solution acidity on the manganese back-extraction rate

It can be seen from Fig. 8, that with increasing sulfuric acid concentrations in the back-extraction solution, the manganese back-extraction rate exhibited an upward trend. Because the concentration of manganese ions in the low-concentration manganese sulfate solution treated using the extraction-back-extraction process had reached the needs of the electrolytic manganese, it could be sent to the electrolytic process as the new electrolytic solution, its pH remaining within the range of 6.5-7. Consequently, while ensuring different acid concentrations and back-extraction rates of manganese, a lower sulfuric acid concentration should be used for back-extraction as much as possible. In this experiment, the optimum acidity of the back-extraction process was .

Effect of Back-Extraction Temperature on Manganese Back-Extraction Rate

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The experimental conditions were as follows: the volume fraction of P204 in the loaded organic phase was , the back-extraction phase ratio ( ) was , the sulfuric acid concentration of the back-extraction solution was , and the mixing time of the back-extraction was 3 min . We examined the variation of the manganese back-extraction rate under different back-extraction temperatures. The test results are shown in Fig. 9.

Fig. 9 The effect of temperature on the manganese back-extraction rate

Fig. 9. The effect of temperature on the manganese back-extraction rate

It can be seen from Fig. 9, that with increasing back-extraction temperatures, the manganese backextraction rate did not change significantly, so the temperature of back-extraction process could be selected to be .

Effect of Back-Extraction Mixing Time on Manganese Back-Extraction Rate

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The experimental conditions were as follows: the volume fraction of P204 in the loaded organic phase was , the back-extraction phase ratio was , the sulfuric acid concentration of the back-extraction solution was , and the back-extraction temperature was . We investigated the variation of the manganese back-extraction rate under different back-extraction mixing time. The results are shown in Fig. 10.

Fig. 10 The effect of mixing time on the manganese back-extraction rate

Fig. 10. The effect of mixing time on the manganese back-extraction rate

It can be seen from Fig. 10, that with the extension of the back-extraction mixing time, the manganese back-extraction rate increased. However, once the mixing time exceeded 3 min , there was no obvious change in the manganese back-extraction rate. This indicated that the back-extraction process was essentially balanced when the back-extraction mixing time was 3 min . To ensure the maximal manganese back-extraction rate, the optimal back-extraction mixing time was set to be 3 .

Effect of Back-Extraction Phase Ratio on Manganese Back-Extraction Rate and Manganese Ion Concentration in Back-Extraction Solution

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The experimental conditions were as follows: the volume fraction of P204 in the loaded organic phase was , the back-extraction mixing time was 3 min , the concentration of sulfuric acid in the back-extraction solution was , and the back-extraction temperature was . We examined the changes in the manganese back-extraction rate and the concentration of manganese ions in the back-extraction solution under different back-extraction phase ratios. The test results are shown in Fig. 11.

Fig. 11 The effect of phase ratio on the manganese back-extraction rate

Fig. 11. The effect of phase ratio on the manganese back-extraction rate

It can be seen from Fig. 11, that with increasing back-extraction phase ratio ( ), the manganese back-extraction rate exhibited a decreasing trend. However, the concentration of manganese ion in the back-extraction solution increased with increasing back-extraction phase ratio. Since the concentration of manganese ions in the new electrolytic solution in industrial production is required to be above , to ensure a higher concentration of manganese ions and manganese backextraction rate, the best back-extraction phase ratio ( ) was considered to be between 2:1 and 3:1.

Determination of Back-Extraction Series

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If there were still large volumes of unstripped manganese ions in the loaded organic phase after first stage back-extraction, it would be essential to conduct multi-stage back-extraction to achieve the maximum back-extraction of manganese. In this experiment, the number of back-extraction stages was selected to be 1,2 and 3 , respectively. The other experimental conditions were as follows: the volume fraction of P204 in the loaded organic phase was , the back-extraction phase ratio (O/A) was , the back-extraction mixing time was 3 min , the sulfuric acid concentration of the backextraction solution was , and the back-extraction temperature was . The test results are shown in Table 2.

Table 2 The relationship of stripping stages and the manganese stripping rate.

Reverse extraction series | The concentration of manganese in the loaded organic phase (g/L) | Back-extraction rate of manganese (%)
1 | 0.57 | 96.9
2 | trace amounts | 100
3 | trace amounts | 100

It can be seen from Table 2, that through the optimal selection of a series of factors such as the concentration of sulfuric acid in the back-extraction solution, the contrast of the back-extraction solution, the mixing time and the temperature of the back-extraction process, the manganese recovery rate could reach when the back-extraction stage was level 1, the maximum recovery of having been achieved. Increasing back-extraction series would undoubtedly increase the cost of industrial production, so the back-extraction stage was selected to be level 1[23].

Determination of Back-Extraction Solution Composition

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Under optimized extraction conditions-that is, with the volume fraction of P204 in the loaded organic phase being , the back-extraction phase ratio ( ) being , the mixing time of the back-extraction being 3 min , the sulfuric acid concentration of the back-extraction solution being , the temperature of the back-extraction being , and the back-extraction stage being level 1-the chemical composition of the manganese sulfate back-extraction solution was obtained. The results are shown in Table 3.

Table 3 The chemical composition of the back-extraction solution.

Component | Mn | Fe | Al | Ca | Mg | Co | Ni | Cu
Content(g/L) | 42.16 | 0.0003 | 0.00012 | 0.03 | 0.088 | 0.00014 | 0.00027 | 0.00005

reached the manganese ion concentration range required by electrolytic manganese, and that the content of all impurities was lower than that of the extract solution, realizing the aim of deep impurity removal. Consequently, the solution could be sent to the electrolytic process for preparation of the electrolyte.

Conclusions

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(1) We analyzed the basic experimental principles of extraction, back-extraction and saponification.
(2) The optimized extraction conditions of manganese from a manganese sulfate purification solution containing a manganese ion concentration of under pressure were as follows: the saponification rate of P 204 was , the extraction phase ratio ( ) was , the concentration of P 204 was , the extraction equilibrium pH was approximately 5.0 , the extraction temperature was , the extraction equilibrium time was 5 min , and the extraction stage was level 1 . Under these conditions, the extraction rate of manganese reached more than , the concentration of manganese ions in the raffinate being less than .
(3) To obtain higher manganese back-extraction rates and reach the required concentration range of manganese ions in electrolytic manganese, various factors affecting the manganese back-extraction rate were investigated. The optimum technological conditions for the back-extraction process were as follows: The back-extraction acidity was , the back-extraction temperature was , the back-extraction mixing time was 3 min , the back-extraction phase ratio was , the backextraction stage was level 1. The back-extraction process was conducted under these optimized conditions, achieving a manganese back-extraction rate of , the concentration of manganese ions in the back-extraction solution being (the concentration of other impurities reaching the requirements of electrolytic manganese).

Acknowledgments

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This work was supported by the National Nature Science Foundation of China under Grant No. 51764007, and the National Key Laboratory Open Fund Project under Grant No. yy20160013.

Conflict of interest

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We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-chemistry-vol-1/978-3-0364-1292-4

Engineering Chemistry, ISSN: 2813-6535, Vol. 1, pp 57-66

doi: 10.4028/p-507e3a

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-12-29

Accepted:
2023-01-13

Online:
2023-03-30

Carbonaceous Catalyst Activated Persulfate for Degradation of Antibiotic Pollutants in Water

The original version of this paper is available on https://www.scientific.net/EC.1.57.pdf

Chenkai Zhong

School of Eco-Environment, Hebei University, Baoding, China.

zck873251403@mail.hbu.edu.cn

Keywords: carbon materials; persulfate; antibiotics; mechanism.

Abstract

In recent years, the frequent use of antibiotics has led to the continuous release of antibiotics into the water environment, which not only poses a potential threat to public health, but also contributes to the generation and spread of antibiotic resistance. In addition, due to the high environmental persistence and low biodegradability of antibiotics, it is difficult to be effectively degraded by traditional water treatment processes. Therefore, it is urgent to develop clean and efficient treatment technologies. Advanced oxidation processes (AOPs), which can effectively remove refractory organic pollutants from water, has become a promising water treatment technology. In this regard, persulfate(PS)-based AOPs(PS-AOPs) has attracted extensive attention of researchers. In this system, PS can be activated by energy and catalysts to produce highly oxidizing active species, and achieve efficient degradation of antibiotics. Due to its rich surface functional groups, high specific surface area and high adsorption properties, researches on the activation of PS by carbonaceous materials have been reported continuously. In this paper, the research progress of carbon nanotubes, graphene, biological carbon, active carbon and hetero-atom doped carbon materials as catalysts to activate PS and degrade antibiotics is reviewed. In addition, the structure and properties of different carbon materials and the activation mechanism of free radical and non-free radical mediated by carbon materials were introduced, and the effects of PS dosage, catalyst dosage, temperature and pH on the degradation of antibiotics were discussed. Finally, this paper points out the important development direction in the future, that is, the development of environmental protection, high efficiency, low cost carbon materials and further research on the actual wastewater treatment performance.

Introduction

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In practice, antibiotics are widely used in medical and health care, aquaculture, livestock disease treatment and other fields,[1-3] making outstanding contributions to human health and animal disease prevention and control. However, after antibiotics are ingested in humans or animals, only a relatively small amount is absorbed and utilized, while the unused portion is excreted through urine and faeces and into various environmental media.[4,5] The accumulation of antibiotics will bring a series of hidden dangers to the ecosystem and natural environment. Moreover, antibiotics are persistent and difficult to degrade, which will not only make water pollution more serious, but also cause a series of intractable environmental problems (such as the emergence of drug resistance). More seriously, the presence of antibiotics in water will facilitate the proliferation of resistant bacteria, posing a threat to human health and the effectiveness of antibiotic drugs.[6] Therefore, it is necessary to take measures to efficiently remove antibiotics from water.

At present, there are many ways to remove antibiotics, such as adsorption, biological treatment, flocculation and AOPs. The adsorption method can only transfer antibiotics between liquid and solid phase without actual degradation.[5] Because antibiotics have bacteriostatic or bactericidal effects, the removal of antibiotics by biological treatment is not ideal.[7] Although the traditional flocculation method can effectively remove suspended particulate matter and natural organic matter (NOMs), the traditional method has poor effect on the removal of trace antibiotics.[8] AOPs overcomes the shortcomings of the above methods and mainly activates peroxides through energy and catalysts to generate active species with strong oxidability, including hydroxyl radical ( ⋅ OH) and sulfate
radical(SO4•-), so as to achieve efficient degradation of antibiotics in water.[9, 10] AOPs based on hydrogen peroxide (H2O2), especially Fenton technology, have been widely used in water treatment. However, Fenton system requires high operating conditions (highly acidic environment) and its cycle stability is also poor. In addition, the continuous replenishment of reactive reagents such as H 2 O 2 and ferric salt leads to the generation of a large amount of iron sludge, which increases the cost of subsequent treatment.[11-14] Compared with H 2 O 2 system, AOPs based on PS has become a more promising water treatment technology, mainly due to: (1) PS is solid at room temperature, which is convenient for transportation and storage; (2) SO4 produced by PS activation has higher redox potential and longer half-life period than ; (3) has better selectivity than and is more inclined to attack electron rich organic pollutants; (4) can play an oxidation role in a wide pH range (2.0-8.0) [15-18]

PS can be activated by energy (ultrasonic, ultraviolet, electrical and thermal energy, etc.) or catalysts (transition metal base materials and carbon materials, etc.) [19, 20] Although heat energy can effectively activate PS, continuous energy input significantly increases the treatment cost, which is not suitable for large-scale water treatment projects. In the ultrasonic activation system, the efficiency of ultrasonic activation is low because of the relatively small range of ultrasonic action and uneven energy distribution. The efficiency of UV activated PS is high, but the ability of UV /PS system to treat actual wastewater is limited because the penetration of UV light in water is greatly affected by water quality. The electrochemical activation reaction mainly occurred on the electrode surface, and the energy consumption and processing time increased due to the limitation of mass transfer. In contrast, catalyst-based PS activation is considered to be a promising AOPs. In recent years, carbon materials have been frequently used to activate PS due to its rich surface functional groups, high specific surface area and high adsorption performance. [2-4, 21] For example, Chen et al studied the degradation of tetracycline (TC), oxytetracycline (OTC) and doxycycline (DTC) by activated persulfate (PDS) with carbon black (CB), and the removal rates of these antibiotics were and , respectively, within 40 min ; [22] Peng et al. found that activated PMS could completely degrade sulfamethoxazole (SMX) in 20 min when they studied biochar composite activated by persulfate (PMS). [23]

In recent years, the study of structure-activity relationship of catalysts in PS-AOPs system activated by carbon materials has attracted much attention. In addition, different structures of carbon materials may play a key role in the activation mechanism. Therefore, the review of past studies is helpful to better understand the nature of activated PS by carbon materials. In this paper, the structure and properties of different carbon materials and their free radical/non-free radical activation pathways were systematically described, and the effects of various environmental factors on the degradation of antibiotics by carbon materials/PS system were discussed, in order to provide scientific basis for the development of this technology in the field of degradation of antibiotics.

Application of Different Carbon Materials in PS Activation

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Carbon Nanotubes. Carbon nanotubes (CNTs) is a typical one-dimensional nano material, which is usually a hollow tube composed of single-layer or multi-layer graphite sheets, which is often used as an efficient adsorbent due to large surface area and abundant pore structure.[24-26] In addition, the lateral wall electron transfer rate of CNTs is high and the mass transfer resistance is low, which is conducive to electron transfer. The oxygen-containing functional groups on the surface of CNTs can promote the electron transfer of organic pollutants to PS, thus improving the activation performance. [27, 28] Surface defects of CNTs can also be important active sites for adsorption and catalysis of PS, improving the catalytic activity of CNTs. [29]

CNTs can be divided into single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs). When the target pollutant has a high degree of desaturation, it can provide more delocalized electrons and generate conjugation with the delocalized electrons of SWCNTs, increasing their adsorption performance. The delocalized electrons of SWCNTs can also be transferred to the PMS, facilitating the cleavage of bonds to form and , thus
accelerating the degradation of pollutants. [30] For example, Duan et al. found that lone pair electrons on could effectively enhance the bond on the surface of SWCNTs, improve the catalytic activity of SWCNTs, and increase the degradation rate of phenol in the study of n-doped phenol oxidation of SWCNTs. [31] Yu et al. also found a coiled carbon framework with highly delocalized electron systems on the outer surface of SWCNTs, which favored electron enrichment or vacancy defects and increased electron transfer rates during PDS activation. [24] MWCNTs, which are made of many graphene sheets rolled into a cylindrical shape, have high stability, high mechanical strength and excellent electrical conductivity, and can be used as catalyst carriers. [26, 32, 33] Feng et al. found that polyhydroquinone coated magnetite/multi-walled carbon nanotubes (Fe3O4/MWCNTs/PHQ) could effectively activate PDS and degrade flumequine (FLU), in which MWCNTs could not only serve as an excellent carrier of electron transfer, but also directly activate PDS to produce active species. [34] Nie et al. found that the application of MWCNTs as the cathode could effectively activate PDS and remove of acyclovir within 240 min .[35]
Graphene. Graphene is a new kind of two-dimensional nanomaterial, arranged in hexagonal lattice, which can be separated from the surface of graphite. Its surface contains rich functional groups including hydroxyl ( -OH ), carbonyl ( ) and epoxy ( ). [36, 37] Graphene has the potential to be used as a catalyst or carrier because of its excellent electrical conductivity, chemical stability and high adsorption properties. [38, 39] However, the surface of the original graphene is hydrophobic and tends to agglomerate, and the zero band gap and high chemical potential in its structure make it exhibit catalytic inertia. Studies have been carried out to improve the catalytic activity of graphene by reducing the oxygen content of graphene, grinding or heat treatment, adjusting the structure and surface properties of graphene.[39-42] At the same time, the change of the oxidation degree of graphene can also strengthen its catalytic activity, such as the preparation of reduced graphene oxide (RGO) and graphene oxide (GO). The activation efficiency of the edge of the carbon atom is not limited by the shape of the carbon atom sp2, which mainly depends on the edge of the carbon atom; In addition, organic pollutant molecules with conjugated structure can produce stacking interaction with RGO, so as to improve the surface activity of RGO and accelerate the degradation process of organic matter through the special functional groups of organic matter. [38, has a large number of functional groups ( -OH , carboxyl ( -COOH ), , etc.) on its surface, which increases its hydrophobicity, enables it to interact strongly with organic pollutants in aqueous solution, and promotes the removal of organic pollutants. [44]
Biochar. Biochar (BC) is a low-cost, accessible and green carbon material, which can be made from various biomass raw materials [37], such as straw [45], sludge [13], algae [46] and wood [47]. The preparation method of BC is relatively simple and it can be obtained through various thermochemical processes [13, 23, 46]. The catalytic activity of the prepared BC can be further improved by certain means. [20,48] Modification of BC form is a common measure and can be achieved by heat treatment or acid-base treatment. Heat treatment can adjust the carbon content, pH value and Zeta potential of BC. Moreover, higher pyrolysis temperature is beneficial to increase the surface area and porosity of BC. For example, Zou et al. investigated the relationship between different pyrolysis temperatures and BC structure and catalytic performance using Spirulina platensis as a precursor. With the increase of pyrolysis temperature, the crystallinity, the amount of sp2 hybrid carbon, pore size and conductivity of the material increased, and the catalytic activity also increased gradually. [46] Acid treatment can significantly reduce the mesoporous surface area and total volume of BC, and introduce oxygen-containing groups on the surface of BC. These oxygen-containing groups can be used as active sites for activating PS and increase the catalytic activity of BC. [49] Alkali treatment can introduce -OH on the surface of BC , and reduce the polarity by forming a positive surface charge, so as to improve its catalytic activity. [20]
Activated carbon. Activated carbon, (AC), with high specific surface area, good porosity and abundant surface functional groups, is the ideal choice as PS activator. [11, 12, 50, 51] AC comes from a wide range of sources and can be prepared from a variety of biomass waste, coal-based, plasticbased and asphalt-based precursors. [37] AC has many functional structures, including sp2 hybrid
carbon, delocalized electron, oxygen-containing functional groups and so on. [37] For example, Guo et al. found that in-situ S-doped AC(SDAC) can increase the number of sp2 hybrid carbon and defect sites, and increase the content of , thus improving the catalytic activity of SDAC to PDS. [52] According to the aggregation form, AC can be further divided into powdered activated carbon (PAC) and granular activated carbon (GAC). [53] PAC's powdery structure gives it a higher specific surface area and better adsorption performance, but it is less reusable and difficult to recycle.[37] In this regard, GACS are generally well reusable [11]. Forouzesh et al. reported synergistic effects of oxidation and adsorption on the removal of metronidazole (MTZ) in GAC/PDS systems. [54]
Heteroatom doped carbon materials. In recent years, heteroatomic doping, as an important means to enhance the properties of carbon materials, has attracted more and more attention. Elements commonly used for doping mainly include non-metallic elements , and P and metal elements Cr , Co , Fe and so on. [50,55,56] It is found that hetero-atom doped carbon materials can: 1) improve the defect degree of carbon materials [55], 2) increase catalytic activity through conjugation and activation of sp2 carbon grids [57], 3) accelerate electron transfer between pollutant and PS [58], 4) enhance surface hydrophilicity. [59] Zhong et al. found that the activation efficiency of BC to PDS could be improved by doping BC with N atoms, thus increasing the removal rate of TC. [58] The doping of N atom can also reduce the content of BC surface, improve the graphitized structure of BC surface, and introduce more active centers. [58] Huang et al. prepared S-doped AC sample (SAC) with a planetary ball mill at 450 rmp for 24 h . In a certain range, the content on SAC surface was positively correlated with the amount of S doping. High content could provide more active sites for PMS, thus improving the catalytic performance of SAC. [60] Generally, the catalytic activity increases with the increase of heteroatom doping, but excessive doping may bring adverse effects. [37] Liu et al. found that the removal rate of 4-nitrophenol (PNP) was improved after PS was activated by ordered mesoporous carbon (S-OMC) doped with appropriate amount of sulfur. When S is excessive, the removal rate of PNP begins to decline, which is due to the serious collapse of the pore structure of carbon material, which reduces the effective exposure of active sites. [61]

In metal element doping, Fe , Ni , Cu and Co are also widely used in PS activation. [2, 62-64] Co doped carbon materials can enhance the dispersion of metal sites, improve the catalytic activity and cyclic stability of carbon materials, and attract much attention in the activation of PS. For example, Han et al. found that when Co doping pomelo peel carbon carbon ( ) activated PMS and degraded TC, as the dose of Co-PPCC increased, the contact frequency of TC molecule and active site of PPCC increased, which promoted the generation of and thus improved the removal rate of TC. [65] Li et al used and urea as N precursors to doped three-dimensional graphene-loaded to activate PMS for degradation of benzotriazole (BTA). [66] Using thiourea and ferric chloride hexahydrate as the precursor system, Wang et al. obtained Fe-S co-doped graphite phase carbon nitride (FeOy/(S-g-C3N4), which could effectively change the charge density and distribution on the surface of g-C3N4 and improve the activation efficiency of PMS. [67]

Progress in Activation of Carbon Materials

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In the activated PS system of carbon materials, the activation modes of PS include non-free radical and free radical path. The radical pathway usually involves , and superoxide anion (O2•-). In PS activation systems, is usually derived from the transformation of SO4•- (Eqs. 1 and 2) [68], which can effectively remove antibiotics mainly through hydrogen capture reaction or addition reaction on aromatic ring. [22, 40, 61] However, due to its low selectivity, NOMs and halogen ions widely existing in actual water bodies are easy to compete with target pollutants and consume ⋅ OH, which not only leads to lower utilization rate of oxidants, but also may produce toxic halogen by-products. SO4•- has a higher REDOX potential than • OH and is more selective, tending to attack electron-rich sites. Many studies have shown that is suitable and mineralized at pH 2-10, and is the main active species under acidic and neutral conditions. Although the activity of is low in alkaline environment, it can react with to transform into , and continue to
degrade antibiotics efficiently (Eq. 3). [22, 43] O2•- has low oxidation capacity, which however can react with water or to produce singlet oxygen ( 1 O 2 ) with higher oxidation capacity (Eqs. 4 and 5), accelerating the degradation of pollutants. [10, 22, 69] Non-radical pathways mainly include 1 O 2 and direct electron transfer. [13] In general, 1 O 2 can be generated by two intermediates, SO5•- and O2•- (Eqs. 5 and 6), or by decomposition of a composite of carbon material and PS [70]. Compared with and has higher selectivity and moderate oxidation capacity, and tends to attack antibiotics containing benzene ring. [70] The direct electron transfer process can occur in carbon materials with low electrochemical impedance and good electrical conductivity, which can not only transfer electrons directly, but also produce some active substances as dielectric to activate PS. In addition, the modification of carbon material can reduce its surface resistivity, improve its surface defect structure and improve electron transfer efficiency. [37, 61] The effect and mechanism of oxidative degradation of antibiotics by activated PS of different carbon materials are shown in Table 1.

Table 1. Effect of different carbon materials activated PS oxidative degradation of antibiotics.

Carbon materials | Substrate | Carbon material dose (g/L) | Type and dose of oxidant | pH | Reaction rate constant (min−1) | Free radical/non-free radical paths | Literature
GAC | MTZ | 5 | PDS, 58mM | 3.9 | 0.0006 | SO4•− | [11]
AC | SMX | 0.1 | PDS, 0.5mM | 7.2 | 0.12 | SO4•−, | [12]
GAC | SMX | − | PDS, 5mM | − | − | •OH, SO4•− | [1]
BC | SMX | 0.1 | PDS, 0.5mM | 7.2 | 0.048 | SO4•− | [12]
BC | SMX | 0.09 | PDS, 0.25g/L | − | 0.03 | •OH ,SO4•− | [71]
BC | SMX | 0.1 | PDS, 0.5g/L | 5.6 | 0.0239 | •OH, SO4•−, 1O2, Direct electron transfer | [72]
Ni@NPG | sulfachloropyridazine (SCP) | 0.2 | PMS, 2g/L | − | 0.4577−0.4623 | •OH, SO4•−, 1O2, O2•− | [73]
EGB | SMX | 0.05 | PDS, 4mM | 3.2−9.2 | 0.0201 | •OH, SO4•−, 1O2, O2•− | [74]
MWCNTs | Acyclovir | − | PDS, 11.1mM | 2−10 | − | Direct electron transfer | [75]
CNTs | ATC | 0.1 | PDS, 0.21mM | 3−7 | 0.153 | Direct electron transfer | [76]

Note: - Indicates unknown.

Factors Affecting the Degradation of Antibiotics by Activated Persulfate from Carbon Materials

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PS Dosing Quantity. The dosage of PS is an important factor affecting the degradation effect of antibiotics. A large number of research results show that when other conditions remain unchanged, the increase of PS concentration in a certain concentration range can improve the yield of active species and then improve the degradation efficiency of antibiotics. [17, 43, 55, 57] For example, Kang et al. found that increasing the concentration of PDS produced more • OH and SO4•- and accelerated the removal of SCP. [77] Huang et al. found that as the concentration of PDS increases, the amount of SO4•- and 1 O 2 increases, and the degradation rate of TC increases [78]. However, when the PS concentration is excessive, increasing the PS concentration will not further improve the degradation efficiency, and even weaken the degradation ability of the system. [13, 43, 57, 79] This could be for several reasons: 1) Excessive PS will directly consume ROS [13]. For example, when the concentration of PS in the system is too high, PS ions react with SO4•- to generate S2O8•-(Eq. 7)
with lower activity. 2) Although excessive PS can increase the ROS production rate, the selfquenching reaction of ROS can also be accelerated, thus reducing the degradation rate of antibiotics. [43, 57, 79] Through chemical experiment and cost analysis, the optimum PS dosage can be determined.

Catalyst Dosage. The amount of catalyst is another important factor affecting the degradation effect of antibiotics. The increase of catalyst dosage can usually provide more active centers, increase the PS activation efficiency, and then generate more reactive oxygen species, and accelerate the removal rate of antibiotics. [43, 80] For example, Magioglou et al. found that as the concentration of BC increased, the system produced more • OH , , thus improving the removal efficiency of SMX [48]. Yin et al. also found that when the concentration of sludge-derived biochar (SDBC) increased from to , the 1 O 2 yield of the system increased and the degradation rate of SMX increased from to [13]. The removal rate of antibiotics is usually positively correlated with the dosage of catalyst, but excessive catalyst may also weaken the degradation performance of the system. For example, Kakavandia et al. found that when the dosage of AC exceeded , the degradation rate and degradation rate of TC decreased, which may be because the main active species under ultrasonic irradiation is 1 O 2 . However, the high dose of AC catalyst inhibited the production of 1 O 2 [81].
Catalyst Dosage. From the perspective of kinetics and thermodynamics, the synergistic effect between temperature and carbon material is helpful to improve the degradation rate and efficiency of antibiotics. [17, 37, 52, 80] For example, Yu et al. reported that when the temperature increased from to , the removal rate of sulfadiazine (SDZ) increased from to [17], for the following reasons: 1) The increase of temperature will increase the proportion of active groups, and then increase the collision probability between antibiotics and active groups [17, 82]; 2) With the increase of temperature, the surface area and volume of micropores increase, which is conducive to enhancing the chemisorption of carbon materials to antibiotics and PS [37, 52]; 3) High annealing temperature can transform sp3 bond into sp2 bond through graphitization, thus increasing electron transfer rate between antibiotics and PS. [37, 82] However, too high temperature will reduce the efficiency of carbon material to degrade antibiotics by affecting the functional groups, graphite structure and porous structure of carbon materials. For example, Kang et al reported that when studying the adsorption capacity and catalytic oxidation capacity of Ni@NCNTs to sulfachlorpyrazine (SCP) at different temperatures, SEM images show that high annealing temperature will lead to deactivation of carbon nanotubes, decrease of 102, decrease of SCP removal efficiency, and produce a large number of by-products in the oxidation process [57]. Guo et al. found that excessive temperature would lead to the collapse of carbon skeleton, which would increase the pore size of carbon material, thus increasing the total pore volume (Vp). However, high temperature will lead to a decrease in the number of oxygen-containing functional groups, resulting in a decrease in the catalytic efficiency of PS [52]。
pH. The pH value of solution affects the dissociation morphology of antibiotics, surface charge of carbon materials and reactive oxygen species. First, the degree of antibiotic dissociation is closely related to the pH of the solution. For example, SDZ dissociation constant pKa 1 is 1.5 . When pKa1, SDZ mainly exists in the form of neutral (SDZ0) and anion (SDZ-) in solution. While when exists in the form of cation (SDZ+) [83]. Secondly, when the pH of the solution is higher than the isoelectric point of carbon material and antibiotics, the surfaces of both are negatively charged, resulting in electrostatic repulsion, which is not conducive to adsorption. [17,37,43,48, 55, 58] In addition, Fe doped carbon materials are also prone to iron hydroxide precipitation under alkaline conditions, reducing the degradation efficiency of antibiotics. [55, 84,] Third, the pH of the solution changes the composition of free radical species. Under acidic conditions, the composition is mainly SO4•-, and H+ may interact with SO4•- and • OH [37, 43]; Under neutral alkaline conditions, and can coexist [37]. Under strong alkaline conditions, tends to be converted to , which will further react with to form water [85, 86]. Jiang et al. activated PDS with
mesoporous carbon-loaded nano-iron ( ), and both and were abundant when . [85] In addition, Yao et al. found that the adsorption capacity of AC to p-chloroaniline (PCA) significantly decreased at pH 3 , which inhibited the direct electron transfer of PCA to , thus reducing the removal rate of PCA. [51]

Conclusion and Outlook

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In recent years, carbon-based materials have been widely used as PS activator to treat antibiotic wastewater due to their low cost, high efficiency and environmental protection. In this paper, the research progress on the degradation of antibiotic pollutants by activated PS with carbon materials is reviewed, including the structure and properties of different carbon materials, activation progress, reaction mechanism, effect and influencing factors of different carbon materials. Although some achievements have been made in AOPs based on activated PS by carbon materials, there are still problems to be solved: On the one hand, due to the adsorption of degradation products and masking of active sites, the problem of poor cyclic stability of carbon materials is widespread. The development of environmentally friendly, efficient and low-cost carbon materials is still one of the key tasks. On the other hand, the current research is mainly focused on the laboratory phase, where experiments are usually carried out using simulated wastewater of a single composition. The concentration of antibiotics in actual wastewater is not fixed, and the composition is complex, which can easily affect the catalytic reaction. Therefore, more attention should be paid to the effect of coexisting components in actual water on antibiotic degradation in future studies.

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Keywords Index

A

Alumina Membrane Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

B

Biodiesel Purification Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

Bioethanol Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition

Bioethanol; Syngas Fermentation; Trace Metal; Growth Medium; Clostridium Ljungdahlii Enhance the Growth of Clostridium ljungdahlii Microbial Cells by Modifying the Medium Composition and Trace Metals

C

Carbon Materials; Persulfate; Antibiotics; Mechanism Carbonaceous Catalyst Activated Persulfate for Degradation of Antibiotic Pollutants in Water

Ceramic Membrane Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

Colloidal Cluster Peak Wavelength Mapping of Triangular Reflection in Spherical Colloidal Cluster with Icosahedral Symmetry

D

Data Driven Soft Sensors Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

Dimetil Eter Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

F

Fermentation Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition

G

Glycerol Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

I

Icosahedral Symmetry Peak Wavelength Mapping of Triangular Reflection in Spherical Colloidal Cluster with Icosahedral Symmetry

L

Lignocellulosic Biomass Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition

M

Manganese Ion Extraction Technology for Manganese Sulfate Solution Purification

Manganese Sulfate Extraction Technology for Manganese Sulfate Solution Purification

Microfiltration Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

P

Partial Least Squares Regression Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

Principal Component Analysis Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

R

Reactive Distillation Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

Removal Technology Extraction Technology for Manganese Sulfate Solution Purification

S

Structural Color Peak Wavelength Mapping of Triangular Reflection in Spherical Colloidal Cluster with Icosahedral Symmetry

Syngas Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition

Authors Index

B

Bisowarno, B. Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

I

Istiqomah, N. Enhance the Growth of Clostridium ljungdahlii Microbial Cells by Modifying the Medium Composition and Trace Metals

J

Jin, H. Extraction Technology for Manganese Sulfate Solution Purification

K

Kresnowati, M. Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition; Enhance the Growth of Clostridium ljungdahlii Microbial Cells by Modifying the Medium Composition and Trace Metals

Krista, G. Enhance the Growth of Clostridium ljungdahlii Microbial Cells by Modifying the Medium Composition and Trace Metals

Kusumocahyo, S. Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

L

Li, C. Extraction Technology for Manganese Sulfate Solution Purification

M

Maharani, N. Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

Mukti, R. Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition; Enhance the Growth of Clostridium ljungdahlii Microbial Cells by Modifying the Medium Composition and Trace Metals

O

Ohnuki, R. Peak Wavelength Mapping of Triangular Reflection in Spherical Colloidal Cluster with Icosahedral Symmetry

R

Ramadhany, P. Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

S

Setiadi, T. Challenges in Syngas Fermentation for Bioethanol Production: Syngas Composition; Enhance the Growth of Clostridium ljungdahlii Microbial Cells by Modifying the Medium Composition and Trace Metals

T

Takeoka, Y. Peak Wavelength Mapping of Triangular Reflection in Spherical Colloidal Cluster with Icosahedral Symmetry

W

Wibowo, T. Determination of Data Driven Soft Sensors in Dimethyl Ether Production by Reactive Distillation Column

X

Xie, H. Extraction Technology for Manganese Sulfate Solution Purification

Y

Yoshioka, S. Peak Wavelength Mapping of Triangular Reflection in Spherical Colloidal Cluster with Icosahedral Symmetry

Yusri, S. Microfiltration Performance of -Alumina Membrane for Removal of Glycerol from Biodiesel

Z

Zhong, C. Carbonaceous Catalyst Activated Persulfate for Degradation of Antibiotic Pollutants in Water