Engineering Innovations Volume 5

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Volume 5 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

Full text available online at https://www.scientific.net/book/engineering-innovations-vol-5/978-3-0357-3878-0

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Table of Contents

Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

John K. Makunza

Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Yaou Wang, Chris Nault, Matthew Givens, Hai Tao Zhang, Ke Li

Dynamic Modeling and Analysis of Rotor-Bearing System with Localized Defect in Rotating Machinery

Supattarachai Sudsawat, Pirapat Arunyanart

IoT-Based Real Time pH Monitoring of University of Mindanao's Chemical Laboratory Wastewater

Egi Joe Fran Morales, Chosel P. Lawagon

Forward-Backward Propagation to Identify the Maximum Specific Growth Rates of a Bioreactor

Salima Borsali

Theoretical Study on CO2/SO2 Absorption Using N-Alkylethylenediaminium Protic lonic Liquid

Hui Huang, Er Hua

FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

Munkhtsetseg Sambuu, Nyamdulam Renten, Begzsuren Tumendemberel, Rene Tschaggelar, Anna Oleshkevich, Otgonchimeg Tuvdendor, Munkhjargal Sharav, Shilagardi Goolimensee

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-5/978-3-0357-3878-0

Engineering Innovations, ISSN: 2813-1002, Vol. 5, pp 1-13

doi: 10.4028/p-2b38z4

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

Submitted:
2022-04-28

Revised:
2023-02-01

Accepted:
2023-02-01

Online:
2023-04-19

Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

The original version of this paper is available on https://www.scientific.net/EI.5.1.pdf

Dr John K. Makunza

University of Dar es Salaam, Department of Structural and Construction Engineering, P. O. Box 35131, Dar es Salaam, Tanzania

E-Mail: makunzaj@gmail.com

Keywords: Columns, crushing, non-destructive tests, concrete quality, deflection, plumb, jacketing, strengthening

Abstract

Concrete structures may become obsolete and be unfit for use to the extent that they need special attendance of repair or strengthening so as to revive them. The weakness in the buildings may be caused by natural disasters like earthquake, floods, changes of use of the buildings in which higher loads more than the design loads, e.g. vertical extension loads, are applied. When the structures are defective they need to be strengthened in order to restore their original structural integrity. Sometimes, the defect or deterioration needs to be scientifically analysed first in order to know the root cause of the problem and apply the right corrective measures. A case study on an eleven stories damaged building in Dar es Salaam is presented, starting with an investigation on causes of sudden crushing of one column and a strengthening method which embraced a number of techniques such as Land Surveying and non-destructive tests in assessing the building structural integrity. The crushed column and other columns is the ground floor were all strengthened using jacketing method and provision of additional columns at the crushed column.

1.0 Introduction

The original version of this paper is available on https://www.scientific.net/EI.5.1.pdf

Structural defects in concrete structures may be caused by earthquake[1], overloading, structural damages, materials deterioration, apart from design and construction faults. However, if a structure is defective it needs repair and or strengthening. There exist various methods of strengthening defective concrete structures which include; use of bolted steel plates for strengthening columns and beams[2], jacketing of columns using a membrane of reinforced concrete[1, 3], using composites such as Glass Fibre and Polypropylene Fibre Based Engineered Cementitious Composites (GFPPECC)[4] and Post-Tensioned Metal Strapping (PTMS) or Side-Near Surface Mounted (NSM) techniques[5]. A number of strengthening techniques mentioned above from the authors were done in models in laboratories. This paper presents a method of strengthening a defective reinforced concrete column through a case study of a crushed column of an eleven stories building in Dar es Salaam Tanzania shown in Figure 1.

Figure 1: Crushed column

Figure 1. Figure 1: Crushed column

The strengthening technique included first a structural integrity investigation on the building in order to establish if all structural elements; beams, slabs, columns and foundation pads were still strong or not, land surveying work to check the verticality and displacement of the building, carbonation test to check if the concrete has been destroyed by carbonation effect. Also, the quality and amount of steel reinforcement were determined and structural analysis and design were carried out to verify the adequacy of steel reinforcement in the building. Finally, after getting the results, which showed that most of the building elements were still in good condition, the strengthening work was carried out for all columns from footing level to the first floor level by applying concrete jacketing technique, and additional of new columns.

1.1 The Problem

An eleven stories building located within the city of Dar es Salaam had one of its columns abruptly crushed at the head below floor slab beams with a loud noise like a small bomb in the year 2018. The concrete column head failed by crushing and the main steel reinforcement bend by bowing outwardly as depicted in Figure 1. It was not clear on the reason of the failure knowing that the building has been in service for more than 10 years. Due to this problem, the government of Tanzania ordered the residents of the building plus those in the adjacent neighborhood to be evacuated as a safety measure in case of any progressive failure. Following this concern, the Office of the District Commissioner of Ilala requested the University of Dar es Salaam, to carry out a thorough investigation on the structural strength of the building, and provide recommendations on the strength status and appropriate corrective measures[6].

1.2 Objectives

The objective of the assignment was to carry out structural investigation on the building in order to determine the existing structural strength, and recommend on the proper corrective measures to be applied. The specific objectives were therefore;

To determine the quality of concrete materials of the existing structural system;

To determine the load carrying capacity of the existing structural system

To determine the appropriate method of strengthening the crushed column

To ensure that the building at ground up to floor level is properly strengthened so that it is safe to the occupants. Under this retrofitting objective, the sub-objectives were

To supervise the excavation of bases and connections of the specified columns

To ensure good construction practices starting from reinforcement fixing, formwork, casting and curing of the concrete.

2.0 Materials and Methods

The original version of this paper is available on https://www.scientific.net/EI.5.1.pdf

The building was investigated in four main steps which included the following:
(a) Visual assessment
(b) Assessment of verticality of the building using Land Surveying techniques
(c) Structural investigation for the superstructure
(d) Assessment of the foundations

2.1 Visual Assessment

The eleven stories building was observed to be a framed structure with three rows of columns longitudinally by four rows transversally as seen in Figure 2. The floors were of solid concrete slabs of an average thickness of 160 mm spanning two way. The ground floor was used for parking and had no infill walls except at the concrete staircase and lift shaft. From an interview of one person who was there when the crushing of the column took place, it was noted that it suddenly and abruptly crushed emitting a loud noise. However, from an observation on the beams, slabs, walls in upper floors, and columns feet, nothing detrimental could be seen. There were neither cracks nor settlement observed at all, except at the crushed point as depicted in Figure 1.

Figure 2: Layout of columns and slab of the building

Figure 2. Figure 2: Layout of columns and slab of the building

2.2 Assessment of Verticality of the Building

Verticality of a building structure was measured in order to check if the structure was vertically plumb and exhibit safety behaviour or otherwise. The measurements were carried out using Land Surveying Equipment as detailed below:

2.2.1 Observation of Deviation Angles and Analysis for Verticality

Three points (P1, P2 and P3 ref. Figure 3) were established within the area along the front road opposite the building, and the instruments were centred and levelled at each point. Angles were observed for column CC at point E3 when the instrument was at P1, at point F3 with the instrument at P2 and lastly, at point F1 with instrument at P3. The columns E3, F3 and F1 are as shown in Figure 2. Angular measurements were taken using

Figure 3: Measurement of building verticality

Figure 3. Figure 3: Measurement of building verticality

a Total Station TS 01 on which the telescope pointed at first floor and zero degrees was set. Then, the angles were read along the column height at each Floor level, (ref. Figure 3)

The observed angles were recorded and the deviation angles were computed in order to assess if the building exhibit a safe behaviour or if there is any significant tilt. From the data analysis, observations made from Column (CC) at point E3 showed that the highest deviation angle was while the lowest deviation angle was . Also, for the Column at point F3, observation made showed that the highest deviation angle was and the lowest deviation angle was 26 ". And, for the Column at point F8, observation made yielded the highest deviation angle of and the lowest deviation angle was . The control limit in tilting is normally to . Taking . The obtained tilting of is equivalent to 60 mm for a 33.00 m height. Therefore, the tilting is within the allowable value. There is no any danger of toppling of the building.

2.2.2 Levelling of Beams to Determine Downward Displacement

Levelling was done for the beams meeting at column E3 (ref. Figure 4) in order to check the settlement of the building. Beams levels were taken in reference to an established Temporary Benchmark at site with the help of an inverted staff. Referring to Figure 4 which shows the arrangement of beams at first floor, levels were taken for the beams along line numbers L1 and L3, L9 and L7 to L24. The results obtained indicate that the differences of the connected nodes of L1L3 has a value of 0.012 m , L9-L24 has a value of 0.024 m and L7-L9 has a value of 0.042 m . In this regard, the beam along grid 3 at the crushed column (E3) was shortened by 42 mm . This displacement is within the deflection limits of 11500/250=46 mm.

Figure 4: Surveying work layout

Figure 4. Figure 4: Surveying work layout

2.2.3 Measurement of Column (E3) Displacement Due to Crushing of Concrete

Measurement of buckled bars was carried out along the bend bars and also the actual vertical height between the end points as depicted in Figure 5. The results obtained were as given in Table 1, where it can be seen that the bend covered a depth of 40 mm , while from surveying measurement, the total downward movement was observed to be 42 mm . These results together, show that there was a settlement of for column E3.

Table 1: Measurements for establishing the shortening of the column due to crushing

No. | L (mm) | h (mm) | Displacement Δ (mm)
1 | 650 | 610 | 40
2 | 660 | 620 | 40
3 | 540 | 510 | 30
4 | 780 | 750 | 30
5 | 650 | 610 | 40

2.2.4 Remarks on the Measurements

Based on the survey tests done to determine the tilting of the building, and downward displacement at the crushed column, the analysed data shows that the plumb of the building is within the allowable values, and the levelling data shows that the beams deflected down by 42 mm , while measurements along the bent bars gave an average of 40 mm downward displacement. This implies that the foundation moved downward by 2 mm only. In general, the downward displacement is still within the allowable values of not more than 20 mm .

Figure 5: Configuration of measurement of depth due to crushing

Figure 5. Figure 5: Configuration of measurement of depth due to crushing

2.3 Structural Investigation; Tests and Sampling for Laboratory Works

2.3.1 Drilling and Testing of Concrete Cores

For the purpose of assessing the compressive strength of concrete, twelve concrete core samples were drilled from the building at various locations; two from exposed foundations at C2 and C5 shown in Figure 2; two from floor, one from each of and floor slabs, and finally, two cores were sampled from the floor. The concrete cores were visually inspected and their structural features recorded as given in Table 2. The concrete core samples were resized in the laboratory before testing.

The concrete core samples were subsequently tested for compressive strength in accordance with BS 1881: Part 120: 1983[8]. In addition, strength tests were conducted by using the Rebound Hammer method to supplement values obtained from the tests of concrete cores. Reinforcement bars were detected by a Profometer equipment without exposing them. For the exposed steel in the crushed column head, reinforcement bars were viewed and found to be 12 ribbed bars of 25 mm diameter as a confirmation of the results from detecting meter.

Table 2: Summary of Inspection of drilled Core samples

Core No. | Extent of voids | Degree of compaction | Other comments
| Small | Medium | Large |  |
1 | Few | Few | none | Fair | Core cut on 1st Floor
2 | Few | Few | none | Fair | Core cut on 1st Floor
3 | Few | Few | none | Fair | Core cut on 3rd Floor
4 | Few | Few | none | Fair | Core cut on 4th Floor
5 | Few | Few | none | Fair | Core cut on 5th Floor
6 | Few | Few | none | Fair | Core cut on 6th Floor
7 | Few | Few | none | Fair | Core cut on 7th Floor
8 | Few | Few | none | Fair | Core cut on 8th Floor
9 | Few | Few | none | Fair | Core cut on 10th Floor
10 | Few | Few | none | Fair | Core cut on 10th Floor
11 | Few | Few | none | Fair | Core cut on base C2
12 | Few | Few | none | Fair | Core cut on base C5

The effectiveness of the method used to compact the concrete on site was judged based on the size and quantity of visible voids or pores on the surface of the concrete cores. The obtained strength of concrete cores in the bases was found to have an average value of , while for superstructure it varied from to . This implies that the grade of concrete for all the floors and footings was .

2.3.2 Rebound Hammer Test

Figure 6: Rebound Hammer Test on Columns and Beams

Figure 6. Figure 6: Rebound Hammer Test on Columns and Beams

The Rebound Hammer test is a non- destructive test, and it was undertaken in order to assess the strength of the existing concrete in the building. This test was performed on selected columns, beams and slabs at locations as shown in Figure 6. The Rebound Hammer test was done at positions in structural elements as shown in Figure 6. The average rebounds numbers were 15 per location or point. For the columns, from ground floor level to the eleventh floor, the obtained average concrete compressive strength was as summarized in Table 3.

Table 3: Compressive strength of Columns from Rebound Hammer Test

Column location | Estimated compressive strength (N/mm²) | Remarks
| GF | 1st FL | 4th FL | 6th FL | 8th FL | BGL |
Grid E3 | 19 | 27 | 31 | 32 | 29 |  | Crushed column
Grid E2 | 27 |  |  |  |  | 34 | At foot of exposed foundation
Grid C3 | 28 |  |  |  |  | 34 | At foot of exposed foundation

Key: GF = Ground floor storey, BGL = Below ground level
At ground floor storey, it can be seen that the concrete compressive strength for the defective column was being lower than concrete grade C20 which is the minimum grade for structural concrete. The other columns had strengths of not less than . It is most probable that column CC at point E3 (ref. Figure 2) crushed because it had less compressive strength of concrete up to bottom of floor beams. The compressive strength obtained from Rebound Hammer Test is in agreement with the results obtained from concrete cores.

2.3.3 Steel Detection

Steel detection was done using a Profometer steel detector equipment. The test was performed in order to find out the sizes and quantity of reinforcement in various structural elements. For beams, slabs and columns, the observed amount of steel was found to be sufficient even from analysis as shown in Figure 7.

Figure 7: Steel detection in slabs, beams and columns

Figure 7. Figure 7: Steel detection in slabs, beams and columns

2.3.4 Assessment of Chemical Attack in the Concrete

Phenolphthalein chemical solution was applied to the concrete in columns in order to assess as to whether the concrete has been affected by carbonation attack. As seen in Figure 8, the test results showed that the concrete wasn't attacked by chemicals and therefore the reinforcement in it can't corrode easily, and that the concrete is still safe without any agent of diminishing the strength thereof.

Figure 8: Carbonation test results - No carbonation attack

Figure 8. Figure 8: Carbonation test results - No carbonation attack

2.4 Concluding Remarks with Regard to Structural Investigation

With regard to the tests done on the strength of concrete, it was found that all concrete elements of the structure had a strength of not less than except the crushed column where it was which is very low. It is therefore established that the column crushed because of concrete weakness at the top of the column at floor level. The concrete at the point was observed to be rich in fines in comparison to others. Also the surveying measurements have shown that the building has not significantly tilted and that its vertical downward displacement is within the allowable values.

Figure 9: Columns strengthening

Figure 9. Figure 9: Columns strengthening

Due to the above results and concluding remarks, it was recommended that the columns be strengthened from foundation level to the first floor level by jacketing with 150 mm thick concrete of grade C30 (ratio 1:1.5:2 by volume) all around for those in grids C, E and F. Those in Grid A and B may remain without any treatment because of the masonry walls and Lift-shaft which helps the column system to be more stable. Reinforcement of 16 mm diameter 12 in number and links of 8 mm diameter spaced at were to be provided as shown in Figure 9.

2.5 The Retrofitting Works

The retrofitting and strengthening work involved: i) Site inspection, ii) Preparation of Method Statement and iii) Retrofitting work[9] as detailed below:

2.5.1 Site Inspection

A total of nine (9) columns at ground floor level including the crushed one were inspected visually to see if there was any more or new defects after being supported by steel H sections and steel props with threads for tightening or loosening. It was observed that there was no any new defect in the building after 1 year, implying that secure permanent support was needed to reinstate the building to its original functional purposes.

2.5.2 Preparation of Method Statement

The Method Statement was prepared by the contractor based on the report on the Structural Investigation prepared by BICO[6] after a thorough technical investigation on the building. The Method Statement was prepared in such a way that it showed sufficient details on the process of strengthening the 9 columns in grids (C) to (F) given in Figure 2. Structural drawings were prepared on how to anchor the columns reinforcement starter bars in the existing footings because during the structural investigation, the foundation was observed to be in good condition. For this reason, the jacketing of the columns had to start at the existing footings.

2.5.3 Instructions to the Contractor

For the purpose of ensuring that each stage of jacketing work of the columns is properly done, the consultant had to give instructions for excavation to expose each column footing, cleaning and anchoring the starter bars in the footing, ground beams, fixing shear keys and reinforcement with the links for the columns jacketing and formwork. The concrete materials and the grade of concrete were also specified as Grade C30 for all concrete works, and aggregates size not exceeding 20 mm , but preference to 12 mm aggregate size for enhancing better concrete matrix as the new concrete had a thickness of 150 mm all round the old columns. In each case, 12 anchor bars of 16 mm diameter were fixed in the footing in drilled holes of 200 mm deep and 16 mm diameter, cleaned and filled with epoxy material and then the starter bars were inserted in the holes and when the epoxy had hardened, high yield steel bars extending from the footing to the bottom of the floor slab were fixed including links of 10 mm diameter spaced at as shown in Figure 9 (improved from the recommended values). An overlap of at least 800 mm for the steel bars was to be observed.

(a) Excavation to expose the footing

The original version of this paper is available on https://www.scientific.net/EI.5.1.pdf

Each column footing was excavated from ground floor level by removing the paving bricks and breaking the oversite concrete of varying thickness from 100 to 150 mm . The excavated soil materials was heaped around the trench, then the top of the footing was cleaned and scarified to form key for bonding with the new concrete. Locations for the holes were marked, then holes of 16 mm diameter and 200 mm deep were drilled and cleaned again.

Figure 10: Excavation to expose the footing and fixing of starter bars

Figure 10. Figure 10: Excavation to expose the footing and fixing of starter bars

(b) Fixing of Reinforcement Bars

The original version of this paper is available on https://www.scientific.net/EI.5.1.pdf

From calculations in BICO report[9], the required amount of steel was 10Y16, but 12 Y 16 bars were provided to increase the safety margin. The starter bars of around 1000 mm long and 16 mm diameter were fixed in the epoxy filled holes as shown in Figure 10 above. The defective column as shown in Figure 11(a) was replaced by 3 columns bundled and attached to the old column in the directions of the three beams meeting at the said column. The concrete was tested to see that it conformed to grade C30.

Figure 11: (a) Column E3 (b) Columns along grid D reinforcement

Figure 11. Figure 11: (a) Column E3 (b) Columns along grid D reinforcement

In Figure 11(b), shown are the main reinforcement bars and links for jacketing of columns along grid D. Thereafter, the formwork from marine wood was fixed and then concrete was cast and properly vibrated.
With respect to Figure 12(a), after casting the jacketing concrete, after 7 days, the formwork was removed, then the columns were clothed with sacks sheets and continuously cured for 21 days. On finishing the curing, plastering was done to each of the columns as illustrated in Figure 12(b), and then cured for 7 days.

2.5.4 Analysis of Column E3 (Reviving from crushing)

The observed original size of columns was , with 12Y25 bars. However, after jacketing with 150 mm thick all round, the size changed to . Column CC at point E3, (ref. Figure 13) the crushed one, but now replaced by 3 columns and itself jacketed as well, was analyzed and redesigned to ascertain its adequacy. Reduction of live loads in each floor was done as per Table 2 of BS 6399 Part 1 (1996)[10]. From calculations[6], the imposed load constituted 33% of the total design load. Hence, the total axial design load was calculated in kN/floor. Bending moment in column E3 was also established in kNm . The slenderness ratio was found to be 4.5 , implying that it is a short column, and so no presence of an additional moment.

Figure 13: Column E3 layout

Figure 12. Figure 13: Column E3 layout

Thus, the load acting at the bottom of column CC (the crushed one) was analyzed and established in kilo Newton.

Since the compressive strength of concrete in the crushed column was , and the compressive strength for the new jacketing[11] concrete was not less than , an average of was taken into account, specific for the defective column. The design results, revealed that, for the new column size, the required amount of steel would be 10Y16. The added steel during jacketing was 12Y16, being higher than the required amount of steel.

The strength capacity of the strengthened columns was determined using the expression:

Where Strength of strengthened column
Strength of column before strengthening
Strength of concrete jacket membrane
Amplifying equation (1), get:

Where Characteristic strength of old concrete in N/mm²
Characteristic strength of membrane concrete in
width of old column
height of old column
centerline perimeter of the concrete jacket membrane
thickness of concrete jacket membrane
Characteristic strength of reinforcement steel in
Area of reinforcement steel in the old column
Area of reinforcement steel in the concrete jacket membrane

2.5.5 Completion of the Works

After all concrete works have reached 28 days age, the supports to the structural system, namely steel H-sections and other steel props were taken carefully as was indicated in Instructions No. 9 quoted as follows
"When removing the supports, start with the steel -Sections and at the end remove the steel props by unthreading slowly. This action will ensure smooth transfer of loads from the supports to the concrete columns safely''[9]. Figure 14 portrays the ground floor columns after removing all the temporary supports which were instituted when Column E3 crushed in March 2018.

Figure 14: Revived column E3 (in skimming process)

Figure 13. Figure 14: Revived column E3 (in skimming process)

3.0 Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.5.1.pdf

From the investigation results, it was observed that:
(1) The structural members such as columns, walls, beams and slabs didn't show any form of deterioration, exception is for column CC at point E3 (Figure 2) which crushed and the beams meeting at the column moved downward by . The crushing of the column at bottom of floor beams caused 40 mm downward movement, while settlement was 2 mm which is negligible.
(2) The strength of concrete in the columns is at least , with the exception of the crushed column CC which has a strength of , being below the minimum structural strength of , hence it contributed to the crushing problem.
(3) The crushed column had 12Y25, while due to its low strength of , it needed 14Y25 steel bars[6]. In addition, the verticality of the building was observed to be still good, posing no any danger of toppling or overturning.

With regard to the retrofitting work that has been carried out to the nine (9) concrete columns from footing level to the floor level, it was established that;
(1) The eight (8) columns in grids C , E and F, their sizes have been increased from to , i.e they have been jacketed with 150 mm thick reinforced concrete with new 12Y16 in each one of them.
(2) The compressive strength of concrete in the jacketed columns is not less than C25, i.e . The crushed column average strength has been taken as to cater for the weakness of the old column of at top although the new jacket concrete compressive strength is .
(3) Due to the strengthening of the columns from foundation level to the bottom of floor, the load carrying capacity of the columns has been increased to almost double of the previous capacity.
(4) The formerly crushed column has been strengthened by jacketing, and by adding three (3) columns, one in each of the three beams directions meeting at the point E3. The column is now more than 4 times stronger than before retrofitting.

4.0 Concluding Remarks

The original version of this paper is available on https://www.scientific.net/EI.5.1.pdf

From the investigation results and discussions made above, the following concluding remarks are deduced,

The concrete jacketing method is suitable if the columns are not completely crushed

The building is now sufficiently strong and all the tenants are back in the buildings and they will stay there safely without any structural problem on the structure. Further, it is suggested that frequent monitoring of the building be done for a period of five years to check and ascertain its performance.

During construction, it is important to make sure that professional practices are adhered.

It is recommended that use of self compacting concrete in jacketing be studied as it may be easier to work with and more effective.

References

The original version of this paper is available on https://www.scientific.net/EI.5.1.pdf

Su R.K.L., Cheng B., L. Wang, W.H. Siu, and Y. Zhu, "Use of Bolted Steel Plates for Strengthening Reinforced Concrete Beams and Columns", The IES Journal Part A: Civil and Structural Engineering, Vol. 4, No.2, May 2011, pp 55-68, Taylor & Francis Group, ISSN 1937-3260, DOI 10.1080/19373260.2011.567818.

Bassam A Tayeh, Mohammed Abu Naja, Samir Shihada and Mohammed Arafa, "Repairing and Strengthening of Damaged RC Columns Using Thin Concrete Jacketing", Advances in Civil Engineering, Vol. 2019, Article ID 2987412, 16 pages, Hindawi,

Kunio Fukuyama, Yasuo Higashibata and Yasuyoshi Miyauchi, "Studies on repair and strengthening methods of damaged reinforced concrete columns", Cement and Concrete Composites, Volume 22, Issue 1, 1 February 2000, Pages 81-88.

Bhuvaneshwari P and Saravana Raja Mohan K, "Strengthening of Fire-Damaged Reinforced Concrete Short Columns Using GFPPECC Composites", Arabian Journal of Science and Engineering (2020) 45:8619-8632, .

Thanongsak Imjai, Monthian Setkit, Reyes Garcia and Fabio Figueredo, "Strengthening of Damaged Low Strength Concrete Beams Using PTMS or NSM Techniques", Case Studies in Construction Materials 13 (2020), e00403,

Makunza JK and Kiwelu H, (2018), Investigation on an Eleven Stories Building on Plot No. 871 Mfaume Street, East Upanga in Ilala Municipality Dar Es Salaam, BICO Report, University of Dar es Salaam, May 2018.

Bungare S Taranath, Structural Analysis and Design of Tall Buildings, McGraw-Hill Book Company, ISBN 0-07-062878-5, New York, New Delhi, 1988.

BS 1881: Testing concrete -Part 120 (1983): Method for Determination of Compressive Strength of Concrete Cores, BSI, London.

Makunza JK (2019), Remedial Measures for the Eleven Stories Building on Plot No. 871 Mfaume Street, East Upanga in Ilala Municipality, Dar es Salaam City, BICO Report, University of Dar es Salaam, April 2019

BS 6399 Part 1:1996, Loading for Buildings, Code of Practice for Dead and Imposed Loads, BSI, London, 1996.

Saim Raza, Muhammed K.I. Khan, Scott J Menegon, Hign -Ho Tsang and John L. Wilson, "Strengthening and Repair of Reinforced Concrete Columns by Jacketing; State of the ArtReview", Sustainability MDPI, 2019, 11,3208; doi:10.3390/su11113208, /journal/sustainability.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-5/978-3-0357-3878-0

Engineering Innovations, ISSN: 2813-1002, Vol. 5, pp 15-20

doi: 10.4028/p-a43x74

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

Submitted:
2022-10-18

Accepted:
2022-11-08

Online:
2023-04-19

Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

Yaou Wang , Chris Nault , Matthew Givens , Haitao Zhang , Ke Li

Schlumberger, Houston TX, USA

ywang04@slb.com*, cnault@slb.com, hzhang25@slb.com, @slb.com

Keywords: BOP, Pipe Shearing, FEA, Simulation, Damage Model, Data Analysis.

Abstract

A Blowout Preventer (BOP) serves as a safety valve in the drilling process in the oil and gas industry. It will be closed if an influx of formation fluids occurs and threatens the rig. A Ram BOP is one type of widely used BOP. It is composed of two ram blades, which will move toward each other to shear the drilling pipe and to close the valve. To ensure the shearing process is completed on the rig, lab tests are often run to evaluate the BOP's capability and the required shearing pressure. Over the last decade, Finite element analysis (FEA) based simulation method has been set up to predict the shearing process. The simulation method still requires pipe damage parameters and requires lab test. This paper presents a test-free simulation method enabled by analyzing the ram BOP pipe shearing data, which significantly reduces the lead time and test costs.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

A BOP serves as a safety valve in the drilling process in the oil and gas industry [1]. It will be closed if an influx of formation fluids occurs [2]. This event, commonly referred to as a kick, threatens the rig. A Ram BOP, as shown in Fig. 1, is one type of widely used BOP, and acts as the last safety guard in the BOP stack. It is composed of two ram blades that will move toward each other to shear the drilling pipe and to close the valve.

Although the structure is known, the pipe shearing process is invisible since it is covered by the 'Black Box' - the BOP body. To ensure the shearing process can be completed on the rig, multiple lab test runs are required to evaluate the BOP's capability and the shearing pressure for the drill pipes given by the operators.

Efforts have been taken from around 10 years ago to develop a method to predict the shear pressure of Ram BOP. According to the report prepared for the US Minerals Management Services [3], analytical models were first presented, and then developments made to improve the accuracy of the analytical methods [4,5].

When developing accurate prediction methods, computer-aided numerical methods come into view, and the feasibility of computer-aided engineering (CAE) methods is checked. In 2014 [6] and 2015 [7], finite element analysis (FEA) was reported to model the Ram BOP shearing process. The Johnson-Cook damage model [8] was used to predict the pipe damage during the shearing process. For the application of the damage model, the prerequisite is the pipe material damage parameters. The damage parameters are not generic material property and require additional lab tests.

Recently, based on the big data accumulated through Cameron BOP shearing tests over the last decades, a test-free simulation method was developed. The method has been used to model the shearing process of Cameron Ram BOPs and displayed high accuracy. This paper presents this simulation method, which makes the simulation test free and significantly reduces the lead time and test costs.

Ram BOP Pipe Shearing Lab Test

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

Lab testing is the conventional and required method to check the capability of a BOP to ensure that the shearing process can be completed on the rig. For a given rig, operators will provide all types of pipes that will be used on the rig. Lab tests will then be run to evaluate the BOP's capability for each of the given drill pipes. Fig. 2 shows an example of the lab test setup. During the test, the curve of
shear pressure vs. time is collected and the pipe shearing cross section is examined. The max shear pressure is used as the key output to check BOP capability. If the shearing test fails or the max shear pressure exceeds the allowed magnitude, the ram BOP design needs to be modified and then all tests must be rerun. The process will be repeated until the proposed BOP design can pass all the tests, and the BOP is qualified for field use.

The test method is very costly. Shearing tests of each BOP design usually take several months' lead time and cost thousands of dollars. The costs will double if a failure occurs and replacement of the BOP design is required.

Figure 1. Ram BOP design.

Figure 1. Figure 1. Ram BOP design.

Figure 2. Figures of Ram BOP lab test setup, sheared pipe cross-section, and shearing pressure.

Figure 2. Figure 2. Figures of Ram BOP lab test setup, sheared pipe cross-section, and shearing pressure.

FEA Simulation

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

FEA-based simulation method has been developed in recent years to simulate the BOP shearing process. It provides designers with a 3D virtual shearing process tool that helps predict the shearing pressure.

Fig. 3(a) shows an example of the FEA model setup from a BOP computer-aided design (CAD), where the model consists of the BOP rams (blades) and a pipe. The pipe is supported on the bottom, and two rams (blades) will move toward each other to shear the pipe. When the pipe breaks, the highly deformed element will be deleted. The model records and outputs the pipe deformation, ram deformation, and shearing pressure during the whole shearing process, as illustrated in Fig. 3(b).

The traditional FEA simulation workflow is shown in Fig. 4(a). The engineer collects the information on the BOP design, pipe dimension, and material properties. The engineer then sets up the model by importing the geometry, meshing, assigning material properties, implementing a pipe damage model, applying boundary and loading conditions, running the simulation, and generating results.

Figure 3. Illustration of Ram BOP FEA model setup and simulation output.

Figure 3. Figure 3. Illustration of Ram BOP FEA model setup and simulation output.

Figure 4. Workflow for FEA simulation.

Figure 4. Figure 4. Workflow for FEA simulation.

Damage Model

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

The pipe material damage model is used to characterize the damage growth behavior of the pipe under shearing force. The damage model is critical to the accuracy of the shearing simulation result. There have been several damage models developed over the last decades for metals. For pipe shearing simulation, the Johnson-Cook damage model [8] has been widely used to describe the damage behavior of steel pipes. The model is shown as below:

where D1 to D5 are damage parameters and the parameters are conventionally obtained from a destructive lab test using the pipe material sample.

Test Free Simulation Method

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

The paper proposes a test free method, where the parameters D1 to D5 are derived from generic material properties as shown below:

where is elastic modulus, is Poisson's ratio, YS is yielding strength, UTS is tensile strength, is elongation, and RA is reduction in area. Array represents all the damage parameters D1 to D5. Function is the damage parameter function proposed in this paper. The damage parameter function is found through analyzing the correlation between the pipe generic properties and shear pressure in the Cameron BOP shearing test database.

Fig. 5(a) shows the structure of BOP shearing test data, where a large amount of tests (total number denoted by ) have been applied over the last decades. For each test, the pipe generic material property ( ) and the max shear pressure P are recorded.

Fig. 5(b) shows the process to generate the function from the test data. For a given (or trial) damage parameter function , the damage parameters are generated using the generic material property for each test , and FEA simulation is applied to generate the shear pressure prediction . Then the predictions of all cases ( ) are compared with the test recorded shear pressures ( , ). If the difference is larger than targeted error tolerance, then the function will be modified and the process will be repeated until the difference falls within the error tolerance.

Previous publications [7, 9, 10] of BOP shearing simulations have reported the errors in the range of to . This work chose the error tolerance as and successfully found a parameter function.

With this function, the test-free shearing simulation can then be applied following the workflow shown in Fig. 4(b). Compared with the traditional simulation workflow shown in Fig. 4(a), the test free method uses the pipe generic material property to generate the damage parameter and the damage model. This allows the analyst to run the simulation without the lab tests.

Figure 5. Illustrations of using shearing test data to develop the damage parameter function, .

Figure 5. Figure 5. Illustrations of using shearing test data to develop the damage parameter function, .

Validation Case Study

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

The method is validated by representative test cases of commonly used pipe types that cover the categories of tubing, casing, and tool joints. Table 1 lists the selective cases, which are one outside diameter (OD) pipe, one 6-5/8-in OD pipe, one 12.25-in OD casing, and one 8.5-in OD tool joint. Fig. 6 plots the comparison of the shear pressure between FEA prediction (y axis) and lab test measurement (x axis) for those cases. Proprietary test result units of measure have been removed from Fig. 6. However, the figure still provides a visual explanation of FEA vs. physical testing. A reference dash line is plotted to represent the case when prediction is the same as the measurement. The predicted shearing pressure agrees with the test data, and max error is .

In addition to the prediction on shear pressure, it is also found the sheared pipe cross section can be predicted using this method. Fig. 7 shows the comparison of pipe cross section for cases No. 1 to No. 3, and the prediction matches the test observations.

Table 1. Selective test cases of commonly used pipe types.

Pipe Type | Test Measurement | FEA Prediction
Tubing | ● | ●
Casing | ● | ●
Tool Joint | ● | ●

Table 1. Selective test cases of commonly used pipe types.

Case
No.
Pipe Dimension
Error in Max
Shear Pressure

1
OD 4.5 in, ID 3.8 in
-3.9%

2
OD 6.625 in, ID 5 in
2.0%

3
OD 12.25 in, ID 10 in
-9.7%

4
OD 8.5 in, ID 4.25 in
10.6%

Figure 6. Predicted max shear pressure vs. lab test.

Figure 7. Cross section of sheared pipe, simulation prediction vs. lab test.

Figure 6. Figure 7. Cross section of sheared pipe, simulation prediction vs. lab test.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

This paper presents the test-free simulation method for the ram BOP pipe shearing process. This method is developed based on big data accumulated through historical Cameron Ram BOP shearing tests over the last decades. It allows analysts to generate accurate prediction on the max shear pressure and pipe sheared cross section without the requirement on lab tests. This makes the simulation test free and significantly reduces the lead time and costs.

References

The original version of this paper is available on https://www.scientific.net/EI.5.15.pdf

API Standard 53: Blowout Equipment Systems for Drilling Wells. American Petroleum, 2012.

Abernethy, R.: Shear ram blowout preventer forces required, Case Study, Barringer & Associates Inc Florida, U.S., 2010.

Childs, G., Sattler, J. and Williamson, R.: Evaluation of Shear Ram Capabilities for the U.S. Minerals and Management Service, Requisition No: 3-4025-1001, West Engineering Service Inc., 2004, pp. 4-7, Texas, U.S.

Paslay, P.: Analysis of the BOP Cutting Process, Internal Report to BP, 2007.

Evangelatos, G.I., Pollock, T. and Payne, M.L.: Advanced Model for the Prediction of Tubular Shear Mechanics, SPE-170309-MS, 2014, pp1-18.

Tulimilli, B.R., Naik, P.N., Chakraborty, A., Sawant, S., Whooley, A. and Weiss, R.: Experimental and Numerical Investigation of BOP Shear Ram Performance, Proceedings of the ASME 33rd International Conference on Ocean, Offshore and Arctic Engineering, OMAE2014-24714, pp1-8.

Tekin, A., Choi, C., Altan, T. and Adin, H.: Estimation of shear force for blind shear ram blowout preventers, Research on Engineering Structures and Materials, Vol. 1 Iss. 1, 2015, pp39-51.

Johnson, G.R. and Cook, W.H.: Fracture Characterisics of Three Metals Subjected to Various Strains, Strain Rates, Temperatures and Pressures, Engineering Fracture Mechanics, Vol. 21(1), 1985, pp31-48.

Tulimilli, B.R., Naik, P.N., Chakraborty, A., Sawant, S. and Whooley, A.: Design Study of BOP Shear Rams based on Validated Simulation Model and Sensitivity Studies, Proceedings of the ASME 33rd International Conference on Ocean, Offshore and Arctic Engineering, OMAE2014-24305, pp1-9.

McCleney, A., Green, S.T. and Mueschke, N.J.: Modeling of Subsea BOP Shear and Sealing Ability Under Flowing Conditions, Offshore Technology Conference, OTC-28906-MS, 2018, pp1-17.

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Engineering Innovations, ISSN: 2813-1002, Vol. 5, pp 21-28

doi: 10.4028/p-kopelx

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

Submitted:
2023-01-03

Accepted:
2023-01-16

Online:
2023-04-19

Dynamic Modeling and Analysis of Rotor-Bearing System with Localized Defect in Rotating Machinery

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

Supattarachai Sudsawat , Pirapat Arunyanart

Department of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand

supattarachai.s@eng.kmutnb.ac.th, pirapat.a@eng.kmutnb.ac.th

Keywords: Machinery diagnostic, Bearing faults, Vibration analysis, Signal Analysis.

Abstract

The numerical model is developed to study the vibration response due to the localized defect of ball bearing in rotating machinery. In order to simulate the dynamic response, the equations of motions are developed based on the rotor-bearing system where two identical rotors mounted on symmetric flexible shaft and supported by ball bearings are considered in this model. The presence of defect is introduced on a bearing outer raceway and lubrication effect between bearing components is also included. The numerical results are obtained by applying Runge-Kutta method to solve governing equations of motions. It has been observed that the vibration spectrum of the ball pass frequency outer race and its harmonics for the defect bearing is relatively higher than the good one. Moreover, this dynamic model can effectively enhance the understanding of vibration responses for good and defective bearing.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

The rolling element bearing is a major component in rotating machinery. The continuous dynamic and excessive load in the rolling element bearing can cause a localized defect. The localized defect in bearing may be developed during manufacturing process or generated during machine operation. This defect may lead to the failure of other components or even rotor-bearing system. Generally, the rotor-bearing systems generate vibrations even in good condition through their dynamic behaviors and their component interactions. Thus, the dynamic model of rotorbearing system has an important role for condition monitoring and fault diagnosis of rotating machinery. Many researchers [1-10] have tried to develop the dynamic model to simulate vibration response with localized defect in bearing. For instance, Choudhury and Tandon [1] considered a local defect in a rotor-bearing by using three degrees of freedom in dynamic model. This research concentrated on an NJ 204 cylindrical roller bearing with a normal clearance test. The output frequency components in the empirical spectra are found to be the same as in the results from dynamic model. Patel et al. [2] used the Runge-Kutta method to solve coupled equations of motions of deep groove ball bearing based on single and multiple defects on the races. The results show healthy bearing behavior that presents frequencies with harmonic phenomena. Patil et al. [3] used a 6305 deep groove ball bearing to analyze a dynamic model for investigating a localized defect by considering the contact model between the ball and the races as a non-linear spring condition. The conclusion illustrated a mathematical model for the ball bearing vibration based on the defect situation that provided the vibrated amplitude levels for the outer race defect higher than the inner race defect. Liu et al. [4] researched a localized defect of ball bearing by using a piecewise response function. The results identify the relationship between the impulse response and the size of the local defect. Kankar et al. [5] applied the numerical integration technique Newmark- with the NewtonRaphson method to solve the nonlinear differential equations for ball bearing surface defects. The outcomes predict a discrete spectrum with specific frequencies of waviness on the inner and outer races of a ball bearing. Cui et al. [7] analyzed the local fault of rolling-element bearing with five degrees of freedom, apply Hertz contact methodology, and relevant bearing techniques of kinematics and dynamics to solve a nonlinear vibration model. The results show a similar mechanical behavior between experimental and simulation signals. Cui et al. [8] are concerned
about the outer race defect size that impacts vibration response signals. The results show that the width of the bearing fault is related to the vibration signals. Shah and Patel [10] investigated dry and lubricated deep groove ball bearings to predict dynamic models in healthy and defective bearing operations. The results show good relation between theoretical and experimental vibration behaviors. Zhang et al. [11] concerns three parameters (radial load, defect size, and rotation speed) for localized defects of rolling element bearing. The outcome provided the proposed model that matches the experimental data very well. Liu et al. [12] applied the mass distribution and energy method to analysis the rotor- bearing-housing defects. The results show that this method is efficient for structural optimization and fault detective of the rotor- bearing-housing system.

However, most researchers have developed models considering dynamic behavior of bearing solely or with a lumped mass of a shaft. Thus, the objective of this study is the development of the dynamic model of a rotor-bearing system having a localized defect in bearing element. The model in this work has considered a dynamic response of the whole flexible rotor-shaft system where a multimass rotor is divided into discrete mass stations. Then, the general equations of motions have been numerically computed using Runge-Kutta method in MATLAB. The vibration responses of defect free bearing and defect bearing have been compared and analyzed. The expected defect frequency and its harmonics have been noticed in the vibration spectrum of defect bearing.

Simulation Modeling

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

To study the vibration characteristic of rotating machinery due to the localized defect in rolling element bearing, the numerical model is developed by base on the bearing test rig, shown in Fig.1. The model consists of two identical rotors, a symmetric shaft, and two ball bearings. The rotor shaft is supported by two ball bearings where the left end is totally in good condition whereas the test bearing is placed on the right end. For bearing model, it is assumed that the inner race is tightly held to the shaft while outer race is rigidly fixed and stationary in bearing housing.

Fig. 1 Schematic diagram of the bearing test rig

Fig. 1. Schematic diagram of the bearing test rig

Numerical model of a rotor-bearing system

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

The equations of motions for a rotor-bearing system can be written in matrix form as

where is the mass matrix of shaft, is the shaft damping matrix, and is shaft stiffness matrix incorporating an average bearing principal stiffness term. is the generalized displacement vector of rotor and is the external force and mass imbalance excitation.

Nonlinear model of rolling element bearing

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

According to the Hertzian contact deformation theory [13], the non-linear relation loaddeformation is given by

where is the load-deflection factor or constant for Hertzian contact elastic deformation, is the radial deflection or contact deformation and n is the load-deflection exponent which equals to for ball bearing. The load-deflection factor depends on the contact geometry between ball and each raceway and the material elasticity. The total factor between two raceways is the sum of the approaches between the rolling elements and each raceway.

and are inner and outer raceways to ball contact stiffness, respectively.
The radial deflection of the ball takes into consideration the loading status is a function of the displacements of the inner race in the horizontal and vertical directions ( ), the angular positions of the rolling elements and the clearance is given by

The angular positions of the rolling element, , can be expressed as a function of cage angular speed , number of rolling elements , time period t , and the initial reference angular position .

The total nonlinear contact force be calculated from the sum of the contact restoring forces from each of the rolling elements. Thus, the contact forces exerted to flexible shaft in the and directions for a ball bearing with balls are written as:

The contact deformation occurs only when the ball is located in the loading zone, and thus contact force is produced. Therefore, a switch function is introduced as follows:

Outer race defect of rolling element bearing

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

When local defect occurs on the outer race of the faulty bearing, the defect can be defined as angular span of , and initial angular position of as shown in Fig.2(a). In addition, this angular position of defect does not change with respect to shaft rotation due to stationary outer race. In effect, the rectangular spall is modeled as fault on outer race surface with the width and the height of as shown in Fig.2(b). When the rolling element is in the site of defect, the additional deflection, , can be computed by using geometric relation.

The defect switch is defined the contact state of rolling elements over the defect as follows:

where the defect on the outer raceway is located between and which normally occurs in the load zone of the bearing. The deformation when the rolling element enters the defect zone is expressed as

The nonlinear contact force with the defect is calculated and substituted into dynamic equation of the nonlinear bearing contact force.

Fig. 2 Schematic diagram of ball bearing: (a) angular position of defect on outer race and (b) additional deflection due to defect on outer race

Fig. 2. Schematic diagram of ball bearing: (a) angular position of defect on outer race and (b) additional deflection due to defect on outer race

Simulation Setup

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

The vibration responses of rotor-bearing system are obtained by solving the governing equations of motions and bearing contact restoring forces iteratively for each time step. The planar displacements, velocities, and accelerations are computed by the Runge-Kutta numerical method. In this simulation, the governing equations of motions have been iteratively solved by using ODE 45 solver in MATLAB with the time step of .

Numerical Results

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

The schematic diagram of the bearing test rig shown in Fig. 1 is used for studying the vibration responses. In this study, the numerical model has been developed including a shaft, two identical rotors, and two ball bearings. The shaft has a length of 445 mm and a uniform cross section of diameter. Each rotor has a mass of 0.778 kg , a diameter of 160 mm , and a thickness of 15.88 mm . The bearing parameters are given in Table 1.

Table 1 Parameters of the ball bearing

Parameters | Units
Inner raceway diameter (di) | 31 [mm]
Outer raceway diameter (do) | 46.35 [mm]
Ball diameter | 7.9 [mm]
Contact angle (α) | 0° (assumed)
Number of balls | 9
Radial clearance (cr) | 10 [μm]

To identify fault effect, the numerical vibration responses are computed for the bearing with defect and without defect. The simulations are performed under the same condition where the shaft rotating speed is kept at . The vibration responses of the defect free bearing are shown in Fig.3(a) and 3(b) that the vibration response in both horizontal and vertical directions have periodic characteristics in the time domain.

Fig. 3 Vibration responses of numerical model with good bearing: (a) in horizontal direction ( x ) and (b) in vertical direction (y)

Fig. 3. Vibration responses of numerical model with good bearing: (a) in horizontal direction ( x ) and (b) in vertical direction (y)

Similarly, Fig. 4 shows the vibration responses (horizontal and vertical directions) for the test bearing with the outer race defect. Comparing Fig. 4 with Fig.3, the overall amplitudes of vibration responses in time domain for defect bearing are higher than those with healthy one.

Fig. 4 Vibration response of numerical model with defect bearing: (a) in horizontal direction (x) and (b) in vertical direction (y)

Fig. 4. Vibration response of numerical model with defect bearing: (a) in horizontal direction (x) and (b) in vertical direction (y)

Hence, the results in time domain can be identified between good and defect bearing from the amplification of vibration responses. However, in order to investigate the defect components, the vibration responses need to be transformed to frequency domain. The frequency spectra of vibration responses for good bearing and defect bearing are shown in Fig.5(a) and 5(b), respectively. The characteristic components of shaft rotating speed are clearly observed at 24.91 Hz (fs) in both Fig.5(a) and 5(b). Remarkably, the fault characteristic frequency can be calculated as 89.53 Hz which is same as ball pass frequency outer ( ). The spectra as shown in Fig. 5(b) are clearly visible at , and 448 Hz which are corresponding to ball pass frequency outer ( ) and its harmonics frequency ( . The amplitudes at these frequencies are remarkably increasing when the localized defect is included in the simulation model. The excitation force due to the change of contact deformation exerts to the shaft component when a ball bearing element rolls over the defect. Thus, these periodic impacts would enhance the amplitudes of vibration response for defect bearing.

Fig. 5 Frequency spectrum of vibration response: (a) good bearing and (b) defect bearing

Fig. 5. Frequency spectrum of vibration response: (a) good bearing and (b) defect bearing

Summary

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

This study develops the numerical simulation model which can be used to simulate the vibration responses of rotating machinery. The generalized equations of multimass rotor-bearing system including the effects of mass imbalance and nonlinear contact forces of bearings are generated. The nonlinear contact force of bearing is based on Hertzian contact deformation theory. The planar vibration responses have been obtained by solving the coupled equations of motions using the Runge-Kutta method in MATLAB. Then, the vibration results of good and defect bearing are compared. For the bearing without defect, the shaft rotating frequency ( ) along with other frequencies can be observed in vibration spectrum while it is noticed that the characteristic defect frequency ( ) with its harmonics is visible in the case of outer race defect. Theoretically, this frequency is widely used as defective indicator of outer race ball bearing in vibration analysis. From the results, the numerical model is able to simulate the vibration responses of rotating machinery incorporated with good and defect bearing. Moreover, the understanding of vibration responses and defect characteristic can help preventing the catastrophic breakdown of the rotating machinery.

References

The original version of this paper is available on https://www.scientific.net/EI.5.21.pdf

A. Choudhury, N. Tandon, Vibration response of rolling element bearings in a rotor bearing system to a local defect under radial load, J. Tribol. 128 (2006) 252-261.

V.N. Patel, N. Tandon, R.K. Pandey, A dynamic model for vibration studies of deep groove ball bearings considering single and multiple defects in races, J. Tribol. 132 (2010) 1-10.

M.S. Patil, J. Mathew, P.K. Rajendrakumar, S. Desai, A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing, Int. J. Mech. Sci. 52 (2010) 11931201.

J. Lui, Y. Shao, T. C. Lim, Vibration analysis of ball bearings with a localized defect applying piecewise response function, Mech. Mach. Theory 56 (2012) 156-169.

P.K. Kankar, S. C. Sharma, S. P. Harsha, Nonlinear vibration signature analysis of a high speed rotor bearing system due to race imperfection, J. Comput. Nonlinear Dyn. 7 (2012) 1-16.

F. Cong, J. Chen, G. Dong, M. Pecht, Vibration model of rolling element bearings in a rotorbearing system for fault diagnosis, J. Sound Vib. 332 (2013) 2081-2097.

L. Cui, X. Chen, S. Chen, Dynamics Modeling and analysis of local fault of rolling element bearing, Adv. Mech. Eng. 7 (2016) 1-10.

L. Cui, Y. Zhang, F. Zhang, J. Zhang, S. Lee, Vibration response mechanism of faulty outer race rolling element bearings for quantitative analysis, J. Sound Vib. 364 (2016) 67-76.

C. Mishra, A.K. Samantaray, G. Chakraborty, Ball bearing defect models: A study of simulated and experimental fault signatures, J. Sound Vib. 400 (2017) 86-112.

D.S. Shah, V.N. Patel, A dynamic model for vibration studies of dry and lubricated deep groove ball bearings considering local defects on races, J. Meas. 137 (2019) 535-555.

X. Zhang, C. Yan, Y. Liu, P. Yan, Y. Wang, L. Wu, Dynamic modeling and analysis of rolling bearing with compound fault on raceway and rolling element, Shock Vib. 2020 (2020) 1-16

Y. Liu, C. Yan, K. Wang, X. Gao, X. Zhang, L. Wu, Dynamic modeling of rotor-bearinghousing system with local defect on ball bearing using mass distribution and energy methods, Proc Inst Mech Eng K-J MUL. 235 (2021) 412-426.

T.A. Harris, M.N. Kotzalas, Rolling Bearing Analysis, fifth ed., CRC Press, Florida, 2006.

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Engineering Innovations, ISSN: 2813-1002, Vol. 5, pp 29-37

doi: 10.4028/p-zz302h

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

Submitted:
2022-09-29

Revised:
2022-12-19

Accepted:
2022-12-29

Online:
2023-04-19

IoT-Based Real Time pH Monitoring of University of Mindanao's Chemical Laboratory Wastewater

The original version of this paper is available on https://www.scientific.net/EI.5.29.pdf

Egi Joe Fran D. Morales and Chosel P. Lawagon

¹College of Engineering, University of Mindanao, Matina, Davao City, Philippines

Research and Publication Center, University of Mindanao, Matina, Davao City, Philippines

egimorales@umindanao.edu.ph, b*clawagon@umindanao.edu.ph

Keywords: pH, loT, buffer solution, solenoid motor

Abstract

Wastewater is a by-product of industrial and commercial facilities. If not treated, it could cause environmental and health problems. The proposed IoT-Based Real-Time pH Monitoring of wastewater can regulate the disposed waste through web browsers. The device has undergone several trials with wastewater from the chemical laboratory at different pH levels. It gave the signal to the solenoid motor to position either close when the pH level is more significant than pH 7.5 and less than pH 6.5 or open when the pH level is pH 6.5 to pH 7.5 . The output's accuracy was measured using buffer solutions at different temperatures. It showed that the value of the pH level varied in temperature with significantly small changes. The system monitoring in the web browser captured the data every minute and saved it in a database for data comparison purposes. The device conformed to the Department of Environmental Natural Resources - Environmental Management Bureau (DENR-EMB) in the Philippines' standard permissible pH level ( pH 6.0 to pH 9.0 ). Using an Arduino NANO microcontroller that served as a central processing unit and a Node MCU to connect the system to the internet, the equivalent output of an industrial pH meter was obtained, allowing the user to monitor the system at any time using a web browser.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.5.29.pdf

Wastewater is a costly pressing concern [1] that, if left untreated, poses environmental and health hazards, especially in developing countries [2], [3]. It is primarily generated from ordinary human activities like bathing, toilet flushing, laundry, and dishwashing, among others [4],[5]. Aside from domestic sources, it also includes industrial, commercial, agricultural, and academic activities [6],[7]. The common wastewaters generated from university chemical laboratories are acid and basic waste such as NaOH and KOH . The wastewater from the chemical laboratory was discharged from various laboratory activities, such as washing glassware and chemical waste from research and experimental educational activities. The discharged chemical liquid contains toxic chemicals, organic compounds, and heavy metals, which harm living organisms and the environment [8].

Every ecosystem relies on water in some regard, and when it is contaminated by sewage, toxic chemicals, or any number of other artificial forms of waste, the destruction of natural habitats and wildlife is imminent. Hence, several wastewater treatment practices, like physical water treatment [9], are used for screening, sedimentation, and skimming to remove solids. Biological water treatment [10] breaks down the organic matter in wastewater, such as soap, human waste, and food. In addition, using chemicals in water such as chlorine and oxidizing chemical commonly used to kill bacteria is one form of chemical water treatment [11]. These treatments require close supervision to prevent further environmental problems from persisting.

Although wastewater quality monitoring is required, some businesses need to apply it better because they need more technical resources and instruments [12]. Data on water quality and wastewater could be more reliable and organized. Due to increasing water quality concerns, monitoring water quality is likewise becoming a complex problem [13]. Consequently, a new paradigm is needed to manage water resources in the future. On the other hand, it's crucial to include cutting-edge technologies and new methods in conserving water resources. It enhances the performance of the network and processing facilities running within the region and resource
consumption. Parameters affecting water quality must be regularly checked every day. For instance, monitoring of industrial wastewater includes dissolved oxygen, temperature, pH , ammonia, nitrate, nitrite, and salinity [16] as well as specific parameters (COD or TOC, AOX, suspended solids), single substance parameters, and BOD 5, NH4-N, and total N (sum of Kjeldahl-N, NO 2N, and NO 3-N) (heavy metals, nitrogen compounds and other). Usually, the parameter must be continuously observed, i.e., more than once every day. pH must be checked even after wastewater treatment to ensure it is safe to be released back into the environment [17], [18].

InoLab® 7110 and 7310 gadgets can measure and monitor wastewater's pH [19]. It cannot be observed online and only record measurement data, such as the date, time, and pH level [20]. On the other hand, real-time monitoring of the Internet of things (IoT) consists of a network of physical objects outfitted with software, sensors, and network connectivity to gather and share data[21]. Chemical laboratories can track the toxic liquid discharges from their equipment online and in realtime by using IoT monitoring [22].

The University of Mindanao Chemical laboratory (UMCL) generated wastewater is indefinitely stored in containers due to the lack of a government-accredited third-party chemical waste disposal facility in the region [23]. However, most of the waste generated in UMCL is alkaline and acidic; hence typical neutralization [24] can be done before safe disposal. To ensure that treated wastewater adheres to Department of Environment and Natural Resources (DENR) standards ( to 8.5 from DAO No. 35) and is acceptable for safe discharge, the development of wastewater monitoring is necessary.

Hence, this study aims to monitor the quality of the treated wastewater online and assurance of water quality using sensors and microprocessors. Since wastewater monitoring is a system to ensure water quality, this can aid the university in guaranteeing clean and safe water for discharge, particularly for chemical laboratories and multi-test. Also, online monitoring allows the laboratory custodian to access and update anytime easily. Students are also safe from water contamination and risk of health problems.

The focus of this project is to design a system to monitor the quality of treated wastewater in terms of pH online in the University of Mindanao chemical laboratory only. The system will use a microprocessor and sensor to measure water quality. The microprocessor will send a signal through Wi-Fi and can be accessed online. The proposed method is limited only to android and laptops or computers.

Materials and Methods

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A. Materials

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The system's major components are the Node MCU (Express if Systems, 32 KiB instruction, 80 KiB user data, China), Arduino Nano (Arduino, eight analog inputs ports: A0~A7, China), Analog pH Sensor (DF Robot, Laboratory grade, pH 0~14, China), Analog Dissolved Oxygen (DF Robot, Galvanic Probe, , China), and Water Solenoid Valve (Ningbo Riyi Technology Co., Ltd, Actuating voltage: 12VDC, China).

An Arduino Nano serves as the system's central processing unit. All the system's components are controlled and processed by the Arduino nano. When connecting the system to a wireless network, the Node MCU comes in handy. It's also why the data in the system may be viewed through a web browser. The pH level of the wastewater influent and effluent is measured and monitored using an analog pH sensor/meter kit. An analog dissolved oxygen sensor/meter kit is used to measure and monitor the oxygen level in the wastewater influent and effluent. The water solenoid valve stops wastewater flow if the systems identify an undesirable pH level.

B. Methods

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The IoT-Based Real-Time pH Monitoring flow chart of the system is shown in Figure 1. As the device turns on, the system checks to see if any buttons have been touched. The system will look for a Wi-Fi signal if no buttons are pressed. When the system identifies a known Wi-Fi network, it retrieves a threshold, begins monitoring, and sends data to the website. If the system
is unfamiliar with the Wi-Fi signal, it will construct an Access Point (AP) and configure it through a web browser with an IP address of 192.168.4.1, after which it must be rebooted.
The system enters calibration mode if a button is pressed or can delete the previously saved data. Green, blue, and red are the three buttons. Each button has a distinct function. The green button activates calibration mode, the red button deletes the pH -calibrated value, and the blue button deletes the DO-calibrated value.
The system will enter calibration mode once the green button is pressed. "What button is pressed?" the system will question after it has entered calibration mode. The machine will exit calibration mode if it senses the green button. It will enter pH calibration mode if the blue button is pressed. If the red button is pressed, the system will enter Dissolved oxygen (DO) calibration mode and then restart the system.

Fig. 1 IoT based real-time pH monitoring flow chart.

Fig. 1. IoT based real-time pH monitoring flow chart.

Results and Discussion

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IoT-Based Real-Time pH Monitoring identifies the pH and DO levels and may be monitored online. IoT-Based Real-Time pH Monitoring detects pH and DO levels in analog sensors and saves the data online. The system's central processing unit, represented by the Arduino Nano in Figure 2, is responsible for processing sensor data before sending it to the Node MCU and LCD for monitoring.

IoT-Based Real-Time pH Monitoring, a functional block diagram of the system, is shown in Figure 3. The functions are shown above the blocks, and the required hardware is indicated inside the blocks. The purpose of this system is to monitor the pH level online and ensure effluent passes the environmental standard. The input command is wastewater. The pH and DO sensors convert the wastewater into a voltage.

Similarly, the feedback path's pH and DO sensor convert the threshold signal to a voltage. The Arduino nano boosts the difference between the input and output voltages. When the input and output match, the error will be zero, and the motor will not turn. Thus, the motor is driven only when the system detects if the feedback signal is within the threshold. The output of the Arduino nano always sends a threshold signal to Node MCU and LCD for monitoring.

Fig. 2 IoT based real-time pH monitoring physical diagram.

Fig. 2. IoT based real-time pH monitoring physical diagram.

Fig. 3 IoT based real-time pH monitoring functional block diagram.

Fig. 3. IoT based real-time pH monitoring functional block diagram.

To test the accuracy of the device, a buffer solution was used at ten different temperatures ( . At pH 4 and 7, the value varies in temperature (Table 1), which is the expected trend [25], implying the device's reliability.

Table 1 IoT based real-time pH monitoring values of pH buffer 4 and 7 at different temperature for testing the accuracy of the system.

°C | pH Reading
10 | 3.9 | 7.1
15 | 3.912 | 7.08
20 | 3.98 | 7.05
25 | 4.01 | 7.02
30 | 4.08 | 6.99
35 | 4.02 | 6.98
40 | 4.03 | 6.98
45 | 4.04 | 6.97
50 | 4.06 | 6.95
55 | 4.08 | 6.91

The system is set to pH 6.5 to pH 7.5 , as is the acceptable wastewater pH for disposal back to the environment [26]. At this threshold, wastewater disposal is allowed and adjusted to any desirable pH level.

If the wastewater's pH is greater than pH 7.5 , the status of the solenoid motor out of ten trials is close (see Table 2). It is because the value of a pH level greater than pH 7.5 is set to close the solenoid motor from the program. It signifies that wastewater won't be thrown away unless it meets the designated threshold. This mechanism will ensure that only acceptable pH values will be disposed of, adhering to environmental standards and regulations [26]. InoLab® 7110 and 7310 gadgets can be used to measure and monitor the pH of wastewater [19] and dispose of the effluent to sewage once treated. It can't monitor online and can only record measured data [20] of wastewater.

Table 2 IoT based real-time pH monitoring solenoid motor status with pH level greater than 7.5 of the system.

pH Level | Solenoid Motor Status
8.03 | Close
8.5 | Close
8.00 | Close
8.6 | Close
9.06 | Close
8.89 | Close
8.9 | Close
9.01 | Close
9.03 | Close
9.05 | Close

The solenoid motor is open in all ten experiments when the pH is between 6.5 and 7.5 (see Table 3). The solenoid motor's configuration indicates that wastewater will be ejected when the pH level in the program is between pH 6.5 and 7.5. The established pH threshold complies with environmental legislation and guidelines, enabling the laboratory to dispose of treated effluent properly.

Table 3 IoT based real-time pH monitoring solenoid motor status with pH Level ranging from 6.5 to 7.5 of the system.

pH Level | Solenoid Motor Status
8.03 | Close
8.5 | Close
8.00 | Close
8.6 | Close
9.06 | Close
8.89 | Close
8.9 | Close
9.01 | Close
9.03 | Close
9.05 | Close

When the pH is less than 6.5, the solenoid motor is closed (Table 4). If the pH level is less than 6.5, the solenoid motor will be shut, indicating that the wastewater will not be discarded. Wastewater's pH values are continuously monitored online, with the data gathered being saved in the database server.

Web browsers access the system's online monitoring (https://umindanaochemlab.com/ ). The online monitoring system dashboard (Figure 4) is on three levels. The blue line represents temperature, the green line represents pH , and the red line represents DO levels. The pH 's upper limit from the dashboard can also be modified. A portion of the system had wastewater treated before being disposed of away. With the help of this technology, it is possible to examine previously acquired data, ensure that wastewater disposal in drainage is done by environmental standards, and monitor it online [27].

Table 4 IoT based real-time pH monitoring solenoid motor status with pH Level Less than 6.5.

pH Level

Solenoid

Motor Status

6.48
Close

6.20
Close

6.19
Close

6.22
Close

6.17
Close

5.8
Close

5.78
Close

5.76
Close

575
Close

5.79
Close

Fig. 4 IoT based real-time pH monitoring dashboard.

Fig. 4 IoT based real-time pH monitoring dashboard.

Fig. 4. IoT based real-time pH monitoring dashboard.

The temperature, pH , and DO level data obtained from the device were reflected in the online monitoring dashboard (Figure 4). The data was collected online and entered into the Origin Pro application to see the results.

The pH meter reads pH 9.6 to pH 9.66 for the first 30 minutes, pH 7.04 to pH 7.07 for the second 30 minutes, and pH 4.92 to pH 5.21 for the final 30 minutes of testing (Figure 5). It has been noticed that depending on the pH sensor employed, the pH level takes some time to stabilize its value. But such a device's result is already acceptable given that only a shorter time ( .) is needed to stabilize compared to other studies [28].

Fig. 5 IoT based real-time pH monitoring pH level measured online.

Fig. 5. IoT based real-time pH monitoring pH level measured online.

For the first 30 minutes of testing, the temperature swings from 27.31 to 27.56 degrees Celsius (Figure 6a) before settling at 27.63 degrees Celsius for the remaining hour. Because the temperature of the wastewater has yet to reach room temperature, on the other hand, the dissolved oxygen shows that it fluctuates every time the pH level changes and stabilizes as time passes by (Figure 6b). The oxygen level of water rises every time there's a motion in the water [29] which is the case herein.

Fig. 6 IoT based real-time pH monitoring temperature (a) dissolved oxygen (b) level measured online.

Fig. 6. IoT based real-time pH monitoring temperature (a) dissolved oxygen (b) level measured online.

Based on the system's output, the system is flexible in terms of the pH level that is allowable to be disposed of and monitored online via a web browser (https://umindanaochemlab.com/login.php) and onsite. It is an advantage as other studies only monitor the pH level of the water [30].

Conclusion and Future Works

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The system was tested and operated with reliability up to out of ten trials in the given objectives, taking into consideration of the following conditions: disposal of effluent when the pH level is within the range of and holds the effluent that is greater than pH 7.5 and less than pH 6.5 . The designed system used Arduino nano to monitor the pH level and ensure the safe disposal of effluents in The University of Mindanao chemical laboratory. IoT Based Real-Time pH monitoring can be installed in the laboratory and accessed on a website. Personnel can register on the website to access the monitoring dashboard. Compared to inoLab , which manually monitors the pH level of effluent, this study uses Arduino nano for processing the data gathered from the sensor. It sends it
to node MCU and LCD screen for online monitoring and motor to dispose of effluent once the condition is true. The system developed is not limited to chemical laboratory applications but also any industry that may unintentionally dispose of harmful wastewater.

Furthermore, the dissolved oxygen for triggering solenoid DC motors and time delay detections from the system to online monitoring must be further explored. The time delay before it will be posted on the internet is 30 sec to 1 minute, depending on the speed of the internet. Finally, for ease of monitoring and better access, the creation of android and apple applications for better monitoring is recommended.

References

The original version of this paper is available on https://www.scientific.net/EI.5.29.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.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-5/978-3-0357-3878-0

Engineering Innovations, ISSN: 2813-1002, Vol. 5, pp 39-48

doi: 10.4028/p-si0156

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

Submitted:
2022-10-25

Revised:
2023-03-14

Accepted:
2023-03-17

Online:
2023-04-19

Forward-Backward Propagation to Identify the Maximum Specific Growth Rates of a Bioreactor

The original version of this paper is available on https://www.scientific.net/EI.5.39.pdf

BORSALI Salima ,

¹Automatic Laboratory, Department of Electrical Engineering University of Tlemcen, Algeria

borsali.salima@gmail.com

Keywords: Estimation, Bioreactor, Interval arithmetic method, Forward backward technique.

Abstract

In this article, we are interested in identifying the parameters of an aerobic bioprocess model used for wastewater treatment. In the field of biotechnology, various computer bugs caused by round- ing errors can induce an error interval that is too wide during data acquisition. For this reason, we are testing a new identification method using a set method based on interval arithmetic. The process studied is the chemical transformation of ammoniacal nitrogen which takes place in two stages: Re- action of nitrification-denitrification. The parameters chosen for the identification are the yields and the maximum growth rates. Initially, the study of observability by a differential algebraic method will simplify the study of the mathematical model. This nonlinear model is described by six differential equations. Subsequently, we apply a set method, in particular the propagation of constraints also called forwardbackward propagation, this technique allowed us to determine intervals containing the variable returns as well as the maximum specific growth rates defined from the Monod model which describes the operation of the bioreactor. This method also guarantees the result by rejecting all inconsistent values.

Introduction

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In order to estimate the parameters of a bioreactor, we are interested in this study at a new identification method. This choice is motivated for several reasons. The first is due to an important problem faced by the scientific community and which concerns the various computer bugs caused by rounding errors [1]. The other reasons for our motivation is to find solutions to the problems of convergence and of estimates caused by the errors of modeling and noises of measurements [2], therefore, one cannot rely only on conventional methods of identification. The set approach based on interval arithmetic is well suited in the biotechnology field because in these cases the error interval introduced during data acquisition is too wide as is also the case in biology, chemistry or medical sciences. In this context, a variable is represented by a set called the likelihood set or domain, supposed to contain the real value x . The objective is to identify the parameters of a bioreactor as part of an ammoniacal nitrogen nitrification process, chemical reaction observed during depollution of wastewater. The first models to describe such processes are those reported by the international Association on Water Pollution Re- search and Control(IAWPRC) [3]. One of them is the activated sludge model [4] using Monod kinetics. It is a complete model describe by many parameters making it very complex and therefore identifi- cation by statistical methods is difficult or even impossible. These models are also not suitable for online control. Other models can be considered for the online control of an activated sludge process, using models like ARMAX [5], fuzzy logic [6] and neural networks [7]. The last two methods allow easier handling of nonlinearities. The identification of the parameters of a bioreactor using genetic al- gorithm are cited in [8]. The set techniques used in this study are an approach known for many years. The bases of these methods were established by R. Moore [9], then Neumaier [10] and Hansen [11]. There are applications in many fields for estimating uncertain parameters. This is the case in robotics for the identification of dynamic parameters [12, 13]. Due to the difficulties of acquiring measurements, the use of interval arithmetic is not new in the field of biotechnology [14, 15]. First, we apply a differential algebraic method [16,17],[18], in order to study the observability and identifiability of the model. Indeed, models of biotechnological processes are often described in terms of differential
algebraic equations which therefore lend themselves well to this approach. The article is organized as follows, section 2 describes the essentials of the algebraic approach, and it will be followed in section 3 by the basic definitions of interval arithmetic as well as the principle of constraint propagation. In section 4, we present the bioreactor model whose dynamic parameters we want to identify. The results of the simulation and the discussion are given in section 5 followed by a conclusion.

The Algebraic Method

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The study of observability and identifiability problems using differential algebraic methods dates back to the early 1990s [19, 20],[21]. The principle of the method used in this article consists in verifying the observability of a latent variable compared, for example, to two variables and , if each com- ponent of is solution of an algebraic equation not differential with coefficients dependent on and and a finite number of their time derivatives . In fact, to verify the observability of a variable with respect to and of a system:

Then, for we compute a characteristic set of all differential polynomials we compute a characteristic set of all differential polynomials with respect to a classification whose order must be respected. This classification means that all the derivatives of v and w are less than x and all the derivatives of x are less than . This characteristic set will be represented by a set E of differential polynomials, each directed by one and only one of the variables. We conclude that x is observable with respect to v and w if and only if, each component of it leads to a differential polynomial in E. To do this, a REDUCE package called astb (Diop S, 2002) is used.

Constraint Propagation

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Interval arithmetic

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An interval, denoted by , is a bounded and connected set of , which is defined by:

The real numbers and are the lower and upper bounds of , respectively all intervals within R are denoted by , basic athematical operations are extended to intervals.
Let , then, we define.
Its lower bound:
Its upper bound:

Its Middle: mid
Its radius:
A box (or interval vector) is a compact of , defined by the Cartesian product of n intervals.Therefore,

Elementary mathematical operations are extended to intervals. The result of an operation between two intervals of finite terminals is an interval obtained by working only on their bonds.
Let and , then

In practice, we use less abstract equations:

The division is defined by:

In order to reduce an interval or block, bisection techniques are used. However, the result of operations between intervals is not minimal because of pessimism (Tarek Raissi, 2004) .Contraction techniques have been developed (Luc Jaulin, Michel Kieffer, Olivier Didrit, and Eric Walter, 2001), (Tarek Raissi, Nacim Ramdani, and Yves Candau ,2003) in order to reduce pavement without sometimes having to use bisections. So, a contractor is an operator who makes it possible to contract or reduce an initial search pavement without bisection and keeping all the solutions, i.e. eliminating inconsistent values. Constraint propagation on intervals allows on a predefined domain of variables, a considerable reduction in the size of a box without resorting to bisections. The contractor that we use in our study is based on the principle of projection constraints (Isabelle Braems, 2002).
Consider a set , let us note s containing the smallest pavement . s is called the contraction operator. This operator is able to replace a box by a box s with lower size while preserving the entire of solution set.
A contractor must therefore verify the following two properties (Fig1):

Fig. 1: Contraction of a set

Fig. 1. Contraction of a set

Application to the bioreactor

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In practice, modeling bacteria is difficult due to its living nature. Consequently, they are described by complex functions involving poorly known parameters. We focus on the interval approach, in order to represent the uncertainties that affect these parameters. For this application, a mathematical model is used to describe a biological wastewater treatment with activated-sludge process. Ammonia nitrogen is treated by nitrification-denitrification reaction, involving two populations of
autotrophic bacteria. Nitrification is the biological oxidation of ammoniacal nitrogen, it occurs through two steps: First, nitratation by ammonium-oxidizing bacteria, Nitrosomonas (AOB) and second, nitritation by nitride- oxidizing bacteria,Nitrobacter (NOB).

Fig. 2: The two stages of nitrification

Fig. 2. The two stages of nitrification

The state of this bioreactor is described by six variables: the bacterial concentrations, called biomass and denoted and , as well as the concentrations of substrate and is the concen- tration of the incoming substrate. The above model represents the dynamic evolution of these state variables. The model represents the dynamic evolution of these state variables.

with:

and are the biomass growth rates. They are modeled by Monod kinetics[26]. and are the maximum specific growth rates, and are the stoichiometric coefficients which represent the respective yields of biomasses and and are the half-saturation con- stants for the growth corresponding to the cell affinity for the substrate of each bacterial population. D is the dilution rate, such that , where is the input flow of the bioreactor and V its volume.

Results and Discussions

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The observability of the yields and of the system described by Equations (8) cited above is tested by calculating the characteristic set of the following set of differential polynomials. First, we simu- lated the model with parameters and measurements taken from the literature [27] over a period of 220 days. The yields and are obtained by the algebraic method [28]:

In order to illustrate the propagation of constraints, we construct the directed acyclic for and which are represented by Figure 3

Fig. 3: The directed acyclic graph (DAG) for and

Fig. 3. The directed acyclic graph (DAG) for and

We apply the forward-backward propagation after having developed the expressions (10) in primitive functions (see Appendix). For the simulations, we have chosen ;
and for the additive error due to disturbances and the modeling error, we chose . The principle of forward-backward propagation is to select the primitive constraints to be used for the contractions according to an optimal order, in the sense of the size of the domains obtained at the end.
All the intervals of are chosen and are initialized with
The intervals are obtained by simulation on Matlab, using the Intlab toolbox.

Fig. 4: Yield (right), (left)

Fig. 4. Yield (right), (left)

In Figure (4) are represented on the right the yield and on the left the yield , in blue framing obtained by propagation of constraints, in red star model a priori We obtain the intervals for the yields:

Such as:

We apply the same principle to determine interval for the maximum specific growth rates and . The directed acyclic graph (DAG) are represented by figure 4 In the same way, all the intervals of the primitives constraints are chosen and are initialized with [0, + [

Fig. 5: DAG for and

Fig. 5. DAG for and

In Figure (5) are represented on the right the the maximum specific growth rates and on the left the maximum specific growth rates , in blue framing obtained by propagation of constraints, in red star model a priori We obtain the intervals for the maximum specific growth rates

Fig. 6: The maximum specific growth rates and

Fig. 6. The maximum specific growth rates and

As a result, we have been able to associate with the yield parameters and , as well as with maximum specific growth rates and the smallest intervals contain the a priori values obtained by the propagation of constraints

Conclusion

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One of the difficulties in the study of biological or biotechnological systems is due to the experimental samples which can be counted in days. Therefore, the error interval may be too wide. The set methods based on interval arithmetic that we have already used are well suited in these areas. However, it has a drawback which concerns the problem of pessimism due to two phenomena
which can lead to an overestimation of the desired interval. There is a phenomenon of wrapping (wrapping effect) and of dependence or multi-occurrence because each variable is considered to be different. In this study, we tested this technique to identify the parameters of an aerobic bioprocess model for wastewater treatment. In order to avoid pessimism by studying a model with five equations, an algebraic method allowed us to determine simpler expressions for the yield and then to apply the principle of propagation constraints. The Forward-backward propagation technique allowed us to find a minimum interval for the return and as well as for the maximum unobservable specific growth rates and The advantage of this method is that it allows to obtain an interval containing the real value by rejecting the inconsistent values. We can extend our method to the identification of half-saturation constants for the growth and unobservable parameters

Appendix

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Fig. 7: Notations

Fig. 7. Notations

Forward propagation

Backward propagation

Fig. 8: Simulink

Fig. 8. Simulink

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Engineering Innovations, ISSN: 2813-1002, Vol. 5, pp 49-62

doi: 10.4028/p-7040un

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

Submitted:
2022-03-22

Revised:
2022-11-29

Accepted:
2022-12-06

Online:
2023-04-19

Theoretical Study on CO2/SO2 Absorption Using N-Alkylethylenediaminium Protic lonic Liquid

The original version of this paper is available on https://www.scientific.net/EI.5.49.pdf

Hui Huang , Er Hua

Chemical Science and Engineering College, China

Key Laboratory of Chemical Engineering and Technology, State Ethnic Affairs Commission, including country

Ningxia Key Laboratory of Solar Chemical Conversion Technology, North Minzu University, 204 Wenchang North Street, Xixia District, Yinchuan, Ningxia, 750021, China.

huanghui971217@163.com, huaer0101@hotmail.com

Abstract

The protic ionic liquids (PILs) comprising with N-2-ethylhexylethylenediaminium cation ( ) and bis(trifluoromethanesulfonate)imide anion (TFSA ) forming [HEtHex][TFSA] which has two amines in the polar group and available to absorbs acid gases such as and . In order to study the absorption mechanism of [HEtHex][TFSA], the stable configurations of [HEtHex][TFSA]- and [HEtHex][TFSA] are investigated using the density functional theory at the M06-2X/6-311G ( ) level, then, the interaction energy, molecular vibration frequency, second-order perturbation energy, electron density and Laplace value are calculated and analysed for the most stable configurations. The results show that type weak or medium hydrogen bonding are mainly formed between [ [TFSA] and molecules. The hydrogen bonding interaction is stronger for [HEtHex][TFSA]- comparing with and increases with increasing the number of molecules.

1. Introduction

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As one of the main energy sources of the present time, fossil fuels are necessary for the sustenance of human life. However, extensive usage of fossil fuels has adverse effects on the natural environment and ecological balance. The burning of fossil fuels, such as coal, oil, and natural gases, has caused a sharp increase in the amount of and in the atmosphere. , which is one of the most common greenhouse gases, has led to global warming and has produced a series of global climate issues today. As for , it is one of the main components of acid rain. Therefore, technology to capture and has become a global concern and has been attracting the attention of many researchers [1,2]. Traditional amine absorbents are volatile and not environmentally friendly. Ionic liquids (ILs), however, have many advantages [3-8] such as good thermal stability, low volatility, recyclability. This is especially true for ILs with amine groups, which have good prospects for development in the field of and absorption [9-11].

ILs are molten salts comprising organic or inorganic anions and organic cations with a melting point below [12]. They are divided into two categories such as protic ionic liquids (PILs) and aprotic ionic liquids (AILs)[13]. However, with IL research, the molecular composition of ILs has become more diverse. According to statistics, there are more than 600 cations and 150 anions available to design 1018 kinds of ILs [14]. At present, most studied ILs are AILs, and they occupy the majority of ionic liquid research. In contrast, there are few publications on PILs. PILs are easy to synthesise and have strong polarity, making them suitable for absorbing and dissolving high concentrations of and without causing environmental pollution. Especially, protic ionic liquids comprising chelate amines should be a greater green absorbent for the waste gases.

In this study, the absorption mechanism for and using [HEtHex][TFSA] type PIL (Fig. 1 and Scheme 1, which is experimentally synthesized and the most stable configuration of it was optimized at M06-2X/6-311G ( ) level in our lab [15-17]) are studied using density functional
theory (DFT) [18]. Interaction energy is calculated and molecular vibration frequency, second-order perturbation energy, electron density, and Laplace value are determined. [HEtHex][TFSA] is a nonvolatile PIL with two amine groups in the cationic part of the molecule. Compared with monoamine type PILs and other traditional amine absorbents, [HEtHex][TFSA] is expected as a green amine absorbent for absorbing industrial waste gases such as and .

Fig. 1 The three-dimensional structure of , and the bonds length is marked.

Fig. 1. The three-dimensional structure of , and the bonds length is marked.

Scheme 1 The synthesis of [HEtHex][TFSA].

Figure 2. Scheme 1 The synthesis of [HEtHex][TFSA].

2. Calculation method

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In this study, the configurations of [ HEtHex ][TFSA] and [ HEtHex ][TFSA]- are optimised using DFT with the M06-2X functional and 6-311G ( ) bases set. Hydrogen bonding is studied according to the results of the molecular vibration frequency, second-order perturbation energy [21], Laplace value [22], and the electron density [22] and the main charge distributions at the bond critical points (BCPs) of hydrogen bond. Moreover, reduced density gradient function analysis (GDR) [23] is utilised for analysing the strength of hydrogen bonding.

3. Results and discussion

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3.1 The electrostatic potential surface.

The electrostatic potential of the monomers of [ HEtHex ][TFSA][15,16], and is calculated by the DFT with M06-2X functional and 6-311G basis set and visualized with colors, Fig.2.

Fig. 2 The electrostatic potential surface distribution for the monomers of , and .

Fig. 2. The electrostatic potential surface distribution for the monomers of , and .

The blue region surrounding N-H is more positive, while the yellow with red region surrounding O is more negative. Therefore, intermolecular hydrogen bonding will mainly occur between in [HEtHex][TFSA] and O in or .

Fig. 3 Optimized configurations of , and bond length/nm. (with 1CO2), C4-C5 , C6-C7 and C8 (red is O atom, blue is N atom, charcoal grey is C atom, light grey is H atom, yellow is S atom, green is F atom).

Fig. 3. Optimized configurations of , and bond length/nm. (with 1CO2), C4-C5 , C6-C7 and C8 (red is O atom, blue is N atom, charcoal grey is C atom, light grey is H atom, yellow is S atom, green is F atom).

Fig. 4 Optimized configurations S1-S9 of [HEtHex][TFSA]- , and bond length/nm, S1-S3 (with 1SO2), S4-S6 (2SO2), S7-S8 (4SO2) and S9 . (red for O, blue N, larger grey C, smaller grey H , yellow S , green F ).

Fig. 4. Optimized configurations S1-S9 of [HEtHex][TFSA]- , and bond length/nm, S1-S3 (with 1SO2), S4-S6 (2SO2), S7-S8 (4SO2) and S9 . (red for O, blue N, larger grey C, smaller grey H , yellow S , green F ).

The configurations C1-C8 for [HEtHex][TFSA]- and S1-S9 for [HEtHex][TFSA]- are optimized using the DFT calculation with M06-2X/6-311G level, seen in Fig. 3 and 4. And the most stable configurations of C3, C4, C6, C8 for [HEtHex][TFSA]-1, 2, 3, 4CO2 and S1, S4, S7, S9 for [HEtHex][TFSA]-1,2,4,6SO2 are determined by the calculation of the intermolecular interaction energy using Eq.1, Eq.2, Eq.3 [24], the values are shown in Table1. The structure of [HEtHex][TFSA] with 7SO2 is designed and tried to optimized, but it was failed.

is the interaction energy between molecules [ HEtHex ][TFSA] and is the interaction energy calculated by ZPVE correction and is the interaction energy after the correction of ZPVE (zero-point vibration energy), and BSSE (basis set superposition error) [25]. Interaction energy increases with increasing number of molecules. The value of for [HEtHex][TFSA]- is more negative than for [HEtHex][TFSA]- for the same value of . It means that intermolecular interaction is stronger for [HEtHex][TFSA]- than [HEtHex][TFSA]- . This could be because of the fact that accepts protons more readily than .

Table 1 Interaction energies for [HEtHex][TFSA]- and [HEtHex][TFSA]- .

Configurations | n | ΔE₀ (kJ/mol)a | ΔE₀BSSE (kJ/mol)b
[HEtHex][TFSA] |  | −413.0 | −393.7
C1 |  | −33.2 | −27.0
C2 | 1CO₂ | −34.4 | −27.6
C3 |  | −41.2 | −32.1
C4 | 2CO₂ | −70.3 | −54.2
C5 | −68.3 | −52.5
C6 | 3CO₂ | −91.4 | −69.9
C7 | −91.5 | −64.0
C8 | 4CO₂ | −120.6 | −88.0
S1 |  | −89.8 | −65.6
S2 | 1SO₂ | −64.1 | −49.0
S3 | −66.4 | −50.4
S4 | 2SO₂ | −130.0 | −98.7
S5 | −119.6 | −79.7
S6 | −110.6 | −82.8
S7 | 4SO₂ | −250.6 | −185.8
S8 | −221.3 | −151.2
S9 | 6SO₂ | −388.8 | −296.6

a: : interaction energy calculated by ZPVE correction.
: The interaction energy calculated by ZPVE and BSSE correction.
3.2 Infrared spectroscopy analysis. Infrared spectroscopy analysis is an important approach to evaluate the formation of the hydrogen bonds between molecules[26]. The hydrogen bond is formed between PILs and ; further, the bond length of is in the range of 0.27 nm and can be classified as a weak or medium strength hydrogen bond. Table 2 summarizes that the vibration frequency of between the cation and anion of PILs undergoes a blue shift after combining with , and the change in its value increases with the number of gas molecules. This change in value is much greater for [HEtHex][TFSA]- than it is for [HEtHex][TFSA] . Meanwhile, for in bonding, the change in frequency and bond length is nearly zero for , while a greater red shift ( ) is observed for . This result implies that intermolecular hydrogen bonding between PILs and gas molecules is much stronger when binding with than it is with . In addition, when bonding with , the intermolecular interaction is small. This could be because accepts protons more easily than .

Table 2 The frequency and bond length changes for before and after the absorption of

Configurations | n | N—H Bond | v/cm⁻¹ | Δv/cm⁻¹ | r/nm | Δr/nm
[HEtHex][TFSA] |  | 31N—32H | 3461 |  | 0.1022 |
31N—33H | 2343 |  | 0.1088 |
31N—37H | 3461 |  | 0.1019 |
34N—35H | 3525 |  | 0.1017 |
C2 | 1CO₂ | 31N—33H | 2452 | -109 | 0.1080 | -0.0008
31N—32H | 3472 | -11 | 0.1022 | 0.0000
55O—32H |  |  | 0.2071 |
C4 | 2CO₂ | 31N—33H | 2729 | -386 | 0.1064 | -0.0024
31N—32H | 3473 | -13 | 0.1020 | -0.0002
31N—37H | 3474 | -13 | 0.1023 | 0.0003
55O—37H |  |  | 0.2052 |
58O—32H |  |  | 0.2148 |
C6 | 3CO₂ | 31N—33H | 2815 | -472 | 0.1059 | -0.0029
31N—32H | 3437 | +23 | 0.1021 | -0.0001
31N—37H | 3437 | +23 | 0.1022 | 0.0003
34N—35H | 3538 | -13 | 0.1017 | 0.0000
58O—32H |  |  | 0.2126 |
60O—35H |  |  | 0.2395 |
55O—37H |  |  | 0.2052 |
C8 | 4CO₂ | 31N—33H | 2739 | -396 | 0.1064 | -0.0024
31N—32H | 3485 | -25 | 0.1021 | -0.0001
31N—37H | 3485 | -25 | 0.1021 | 0.0002
34N—35H | 3531 | -7 | 0.1017 | 0.0000
58O—32H |  |  | 0.2104 |
63O—35H |  |  | 0.2305 |
55O—37H |  |  | 0.2140 |
S1 | 1SO₂ | 31N—33H | 3257 | -913 | 0.1036 | -0.0052
31N—37H | 3258 | +203 | 0.1027 | 0.0008
54O—37H |  |  | 0.1912 |
S6 | 2SO₂ | 31N—33H | 3369 | -1025 | 0.1030 | -0.0059
34N—35H | 3547 | -22 | 0.1017 | 0.0000
57O—33H |  |  | 0.1853 |
55O—35H |  |  | 0.2185 |
S8 | 4SO₂ | 31N—33H | 3383 | -1040 | 0.1029 | -0.0060
31N—32H | 3383 | +77 | 0.1029 | 0.0007
34N—35H | 3537 | -12 | 0.1018 | 0.0001
58O—33H |  |  | 0.1892 |
54O—32H |  |  | 0.1925 |
60O—32H |  |  | 0.2249 |
55O—35H |  |  | 0.2365 |
64O—35H |  |  | 0.2324 |

3.3 Natural population analysis. The charge distribution in is calculated by DFT with the M06-2X functional and 6-311G ( ) basis set, the data are listed in Table 3 and Table 4. Electron density decreases on the H atom of and increases on the hydrogen bond acceptor atom, O. For example, by surrounding the hydrogen bonding of in S1 with , the charge in 54 O becomes more negative (from -0.786 e to -0.957 e ) and the H atom becomes positive (from 0.415 to 0.455 e ). For the in C 1 with , the charge on 55 O becomes more negative, (from -0.518 e to -0.606 e ) and the H atom becomes positive (from 0.428 to 0.435 e ). This suggests that a stronger hydrogen bond is formed between [HEtHex][TFSA] and molecules. This result is consistent with the result of infrared spectroscopy analysis.

Table 3 Selected partial charges/e from NPA of [HEtHex][TFSA]- .

Configurations | n | Bonds | Atoms
|  |  | 31N | 34N | 32H | 35H | 37H | O
CO₂ |  | C–O |  |  |  |  |  | −0.518
[HEtHex][TFSA] |  | 31N–33H | −0.736 |  |  |  |  |
31N–32H |  |  | 0.428 |  |  |
34N–35H |  | −0.686 |  | 0.350 |  |
31N–37H |  |  |  |  | 0.415 |
C2 | 1CO₂ | 31N–32H···55O | −0.740 |  | 0.435 |  |  | −0.606
C4 | 2CO₂ | 31N–37H···55O | −0.740 |  |  |  | 0.440 | −0.601
31N–32H···58O | −0.740 |  | 0.429 |  |  | −0.601
C6 | 3CO₂ | 31N–37H···55O | −0.740 |  |  |  | 0.439 | −0.600
31N–32H···58O | −0.740 |  | 0.431 |  |  | −0.579
34N–35H···60O |  | −0.691 |  | 0.359 |  | −0.577
C8 | 4CO₂ | 31N–37H···55O | −0.738 |  |  |  | 0.435 | −0.595
31N–32H···58O | −0.738 |  | 0.433 |  |  | −0.592
34N–35H···63O |  | −0.700 |  | 0.361 |  | −0.568

Table 4 Selected partial charges/e by NPA of [HEtHex][TFSA]- .

Configurations | n | Bonds | Atoms
|  |  | 31N | 34N | 32H | 33H | 35H | 37H | O
SO₂ |  | S–O |  |  |  |  |  |  | −0.786
[HEtHex][TFSA] |  | 31N–33H | −0.736 |  |  | 0.489 |  |  |
31N–32H |  |  | 0.428 |  |  |  |
34N–35H |  | −0.686 |  |  | 0.350 |  |
31N–37H |  |  |  |  |  | 0.415 |
S1 | 1SO₂ | 31N–37H···54O | −0.735 |  |  |  |  | 0.455 | −0.957
S6 | 2SO₂ | 31N–33H···57O | −0.725 |  |  | 0.467 |  |  | −0.891
34N–35H···55O |  | −0.702 |  |  | 0.377 |  | −0.816
S8 | 4SO₂ | 31N–33H···58O | −0.743 |  |  | 0.477 |  |  | −0.868
31N–32H···60O | −0.743 |  | 0.471 |  |  |  | −0.821
31N–32H···54O | −0.743 |  | 0.471 |  |  |  | −0.883
34N–35H···64O |  | −0.696 |  |  | 0.376 |  | −0.810
S9 | 6SO₂ | 31N–37H···54O | −0.781 |  |  |  |  | 0.474 | −0.871
31N–37H···57O | −0.781 |  |  |  |  | 0.474 | −0.887
31N–32H···60O | −0.781 |  | 0.467 |  |  |  | −0.900
34N–35H···64O |  | −0.696 |  |  | 0.438 |  | −0.978
31N–32H···66O | −0.781 |  | 0.467 |  |  |  | −0.806
31N–33H···69O | −0.781 |  |  | 0.473 |  |  | −0.891

The second-order perturbation stabilization energy, , which is described by Eq. 4 as follows [21], corresponding to the LP donor acceptor interaction is calculated for the configurations of C2, C4, C6, C8 and S1, S6, S8, S9 with the strongest hydrogen bond, the data are listed in Table 5.

Among them, ( ) and ( ) represent the donor and acceptor orbitals, respectively, the is the donor orbital occupancy, and are the diagonal elements (orbital energies), and ( ) is the off-diagonal or coupling NBO Fock matrix element. As shown in Table and sum values of for are much larger than those for , which means that the hydrogen bonding between [HEtHex][TFSA] and is much stronger than that between [ HEtHex ][TFSA] and . For example, in the case with sum is 47 and the value is almost twice that of the case with . The strength of hydrogen bonding is strong for [HEtHex][TFSA]-SO2 and medium for [HEtHex][TFSA]-CO2, that are described in Table 7.

Meanwhile, the value for C 6 with is larger than that for C 8 with . At the same time, for [HEtHex][TFSA] with , the sum value reaches a maximum of . This means that one [HEtHex][TFSA] molecule becomes saturated when bonding with three molecules or six molecules.

Table 5 The second-order stabilization energies at BCP of [HEtHex][TFSA] .

Configurations | n | Charge transfer | E(2)a | E⁽²⁾sumb
C2 | 1CO₂ | LP(O55)→BD*(N31-H32)c | 16 | 17
C4 | 2CO₂ | LP(O55) →BD*(N31-H37) | 21 | 29
LP(O55) →BD*(N31-H37) | 19
C6 | 3CO₂ | LP(O58) →BD*(N31-H32) | 11 | 34
LP(O55) →BD*(N31-H37) | 10
C8 | 4CO₂ | LP(O58) →BD*(N31-H32) | 11 | 28
S1 | 1SO₂ | LP(O54) →BD*(N31-H37) | 29 | 29
S6 | 2SO₂ | LP(O55) →BD*(N34-H35) | 10 | 47
LP(O57→BD*(N31-H33) | 37
S8 | 4SO₂ | LP(O54) →BD*(N31-H32) | 31 | 108
LP(O58) →BD*(N31-H33) | 46
S9 | 6SO₂ | LP(O60) →BD*(N31-H32) | 14 | 135
LP(O54) →BD*(N31-H37) | 22
LP(O57) →BD*(N31-H37) | 17
LP(O60) →BD*(N31-H32) | 45
|  | LP(O60) →BD*(N34-H35) | 16 |
LP(O69) →BD*(N31-H33) | 15

a: are weak, medium and strong hydrogen bond, respectively.
values less than have been omitted.
is the sum of of the BCP in the configuration.
c: LP is lone pair, and BD * is bond donor.
3.5 The theory of the atoms in molecules. The topological properties of the electron density scalar field can be obtained from the theory of atoms in molecules and used to describe the characteristic of bonding between atoms. The electron density [22] ( , a.u.), Laplace value ( , a.u.) [22] for [HEtHex][TFSA]- are calculated by DFT theory with M06-2X/6-311G( ) and the data are listed in Table 6. The hydrogen bonding energy, is calculated using Eq. 5. stands for electron potential density at BCP.

at BCPs are positive for all of the configurations, values are in the range of . Among them, the maximum of the hydrogen bonding in is as high as 34 , while that in the case of is only . This means hydrogen bonding is stronger for [HEtHex][TFSA] than for [HEtHex][TFSA] . The value of is also in an agreement with the result of values. This could be because of the fact that accepts protons more readily than .

The of with are and with are , respectively. This means that intermolecular hydrogen bonding increases with an increasing number of molecules, which agrees with the result of the second-order perturbation energy study.

Meanwhile, the value of and is in the region of strong hydrogen bonding for [HEtHex][TFSA]- and medium for [HEtHex][TFSA]- , respectively as shown in Table 7 and Fig.5.

Table 6 The values of electron density, (a.u.), Laplacian of electron density, (a.u.), hydrogen bonding energy, at bond critical point of .

Configurations | n | BCPs | ∇²ρc | EHB | ρc | ρc(sum)a
C2 | 1CO₂ | 55O···32H–31N | 0.076 | 17 | 0.018 | 0.018
|  | 58O···32H–31N | 0.068 | 15 | 0.016 |
C4 | 2CO₂ | 55O···37H–31N | 0.077 | 17 | 0.018 | 0.034
|  | 57O···32H–31N | 0.074 | 16 | 0.017 |
C6 | 3CO₂ | 55O···37H–31N | 0.078 | 17 | 0.018 | 0.043
|  | 58O···32H–31N | 0.069 | 15 | 0.016 |
|  | 60O···35H–34N | 0.033 | 8 | 0.009 |
C8 | 4CO₂ | 58O···32H–31N | 0.068 | 15 | 0.016 | 0.044
|  | 55O···37H–31N | 0.071 | 16 | 0.016 |
|  | 63O···35H–34N | 0.045 | 11 | 0.012 |
S1 | 1SO₂ | 31N···37H–54O | 0.114 | 30 | 0.026 | 0.027
S6 | 2SO₂ | 31N···33H–57O | 0.121 | 34 | 0.029 | 0.045
|  | 34N···35H–55O | 0.060 | 15 | 0.016 |
|  | 34N···35H–64O | 0.041 | 10 | 0.012 |
S8 | 4SO₂ | 34N···35H–55O | 0.039 | 10 | 0.011 | 0.089
|  | 31N···32H–54O | 0.111 | 27 | 0.025 |
|  | 31N···32H–60O | 0.051 | 12 | 0.014 |
|  | 31N···33H–58O | 0.114 | 30 | 0.027 |
|  | 34N···35H–57O | 0.049 | 13 | 0.013 |
S9 | 6SO₂ | 31N···37H–57O | 0.075 | 17 | 0.017 | 0.101
|  | 34N···35H–60O | 0.060 | 15 | 0.016 |
|  | 31N···32H–60O | 0.106 | 27 | 0.025 |
|  | 31N···33H–69O | 0.050 | 12 | 0.013 |
|  | 31N···37H–54O | 0.066 | 15 | 0.016 |

a: is the sum of the values of the main hydrogen bonding in the configuration.

Table 7 Suggested strength for hydrogen bonds[27-29] and corresponding value calculated for [HEtHex][TFSA]- in this study, among them, and are corresponded to and , respectively.

Parameters | Weak Medium Strong | [HEtHex][TFSA]-nSO₂ | Strength | [HEtHex][TFSA]-nCO₂ | Strength
| Weak ionic dispersion | Ionic | Covalent |  |  |  |
H···Y (nm) | > 0.22 | 0.15–0.22 | 0.12–0.15 | 0.10–0.24 | Medium | 0.10–0.25 | Weak or Medium
X–H vs. H···Y | X–H << H···Y | X–H < H···Y | X–H ≈ H···Y | N–H < H···O | Medium | N–H < H···O | Medium
ρc (a.u.) | 0.02–0.002 | 0.02–0.05 | > 0.05 | 0.027–0.101 | Medium | 0.018–0.044 | Weak
E ⁽²⁾ (kJ·mol⁻¹) | < 29 | 29–150 | > 150 | 29-135 | Medium | 17-34 | Weak
EHB (kJ·mol⁻¹) | < 16 | 16–62 | 62–167 | 10-34 | Medium | 8–17 | Weak

Fig. 5 The second-order stabilization energies, , electron density, (a.u.), and the change in vibrational frequency, as a function of hydrogen bonding energy, for hydrogen bonds in and .

Fig. 5. The second-order stabilization energies, , electron density, (a.u.), and the change in vibrational frequency, as a function of hydrogen bonding energy, for hydrogen bonds in and .

3.6 The analysis of RDG. To describe the strength of hydrogen bonding between [HEtHex][TFSA] and , a scatter diagram and colour-filled reduced density gradient (RDG) isosurface map is drawn and shown in Fig. 6 and 7. The spurs on the left, in the middle, and on the right in Fig.6a and Fig.7a correspond to hydrogen bonding, van der Waal forces, and nonbonded overlap, respectively, and also correspond to the blue, green, and red regions in Fig.6b c and Fig.7b c, respectively. The denser spur on the left means stronger hydrogen bonding between [ [TFSA] and . In contrast, the density of the spur increases with an increasing number of molecules bonding with [HEtHex][TFSA], with a denser spur in the case of and a sparser spur for . This result, which shows that the absorption ability of [HEtHex][TFSA] is better for than , is consistent with the result of AIM and NBO analyses.

Fig. 6 The scatter diagram and color-filled RDG isosurface map of [HEtHex][TFSA]-

Fig. 6. The scatter diagram and color-filled RDG isosurface map of [HEtHex][TFSA]-

Fig. 7 The scatter diagram and color-filled RDG isosurface map of .

Fig. 7. The scatter diagram and color-filled RDG isosurface map of .

4. Conclusion

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The absorption of using the chelating PILs composed of N-2ethylhexylethylenediaminium cation and TFSA anion ([HEtHex][TFSA]) was invested at the electronic and molecular level using M06-2X/6-311G ( ) of DFT calculation. The interactions between [HEtHex][TFSA] and were determined and used for classification of
the H -bonds in the structure of . The total H -bond energy of [HEtHex][TFSA]- (or ) increased with the number of gas molecules. AIM analyses and NBO data showed that the H-bond energies for the interactions in the structures of PILs- are larger than those for PILs- . The PILs combined with have H -bond energies within the range of 10-34 kJ/mol, values within the range of bond lengths within the range of , and within the range of a.u. At the BCPs, in contrast, these values for PILs- are within the range of , and nm , respectively. These values enabled the classification of the H -bonds for as strong and those for PILs- as weak H-bonds. This could be because of the fact that accepts protons more readily than .

Supplementary Material

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See Supporting Information for the complete original vibrational frequency, charge distribution, second-order perturbation stabilization energy, and electron density data.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/EI.5.49.pdf

This work was financially supported by the Natural Science Foundation of the Ningxia Hui Autonomous Region (Project Number: 2021AAC03181), the Graduate Innovation Program of North Minzu University (Project Number: YCX21134) and the Scientific Research Foundation for Returned Scholars of Ministry of Education (Project Number: [2013] No. 693). We are grateful to Miss Zheng Liu for assisting in the synthesis of the PILs and investigating their properties in a previous study.

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The original version of this paper is available on https://www.scientific.net/EI.5.49.pdf

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-5/978-3-0357-3878-0

Engineering Innovations, ISSN: 2813-1002, Vol. 5, pp 63-70

doi: 10.4028/p-ntfupf

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

Submitted:
2022-02-11

Revised:
2022-12-02

Accepted:
2022-12-02

Online:
2023-04-19

FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

The original version of this paper is available on https://www.scientific.net/EI.5.63.pdf

Munkhtsetseg Sambuu , Nyamdulam Renten , Begzsuren Tumendemberel , Rene Tschaggelar , Anna Oleshkevich , Otgonchimeg Tuvdendorj , Munkhjargal Sharav , Shilagardi Goolimensee

¹Department of Physics, School of Arts and Sciences, Natural Sciences Division, National University of Mongolia, Ulaanbaatar, Mongolia

Department of Physics, German-Mongolian Institute for Resources and Technology, Ulaanbaatar, Mongolia

Department of Physical Chemistry, ETH, Zurich, Switzerland

Department of Physics, Belarusian State University, Minsk, Belarus

City Fuel Co.Ltd, Ulaanbaatar, Mongolia

munkhtsetseg_s@num.edu.mn, nyamdulam.r@gmail.com, tschaggelar@phys.chem.ethz.ch, Lapchuk@bsu.by, enkhee919@yahoo.com, munkhjargal@num.edu.mn, shilagardi@num.edu.mn

Keywords: Bituminous Coal, Enriched Coal, Dopant, FTIR Spectroscopy

Abstract

In the present work we pay an attention to the molecular structural changes between raw coal and its enriched, dense productions. Bituminous Ukhaa-Khudag coal in the form of an enriched, dense coal product is currently used as an energy source by Mongolian consumers for everyday need. There are three types of samples from one source: it is a raw coal (sample 1) and two of industrial products as an enriched, dopant (organic based) added coal (sample 2) and simply enriched coal (sample 3). The raw coal general characterization is given through the elemental analysis, thermogravimetry analysis, moreover functional groups were identified by Fourier Transform infrared spectroscopy. The differences in the FTIR spectra of the samples were done. Some details are as follows: hydroxyl group vibrations are existing, rather weak in agreement of TG result; vibrations of aliphatic group were rarely in the long wave near region, carbonate minerals in the region of 1300-900 are visible but overlapped, and an isolated out-of-plane deformational vibration (around ) or two-adjacent ( ) or three-adjacent (around ) of -hybrid bonded aromatic -groups were still presenting. After the processing hydroxyl group bands were affected in enriched coals, sample 2 and 3 . In the spectrum of the sample 2 the long wave bands were intensively blurred. Industry processing retains an amount of water in the productions. Water excess influenced absorbance bands were able to be removed via the drying processing, however, the affection to the spectral structure in sample 2 was unrecoverable.

Introduction

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Few years ago, the Mongolian Government constitutionally forbade a use of direct burning of raw coal in the country, particularly in the capital city Ulaanbaatar, where a population density exceeds over one million, to decrease facing problem related to the air pollution and public health damage. Raw coal use was substituted by enriched, dense coal products. Several industries throughout the country produce these types of products. As industry raw materials in common, the bituminous coals are used therefore, technically the coal deposits around the city Ulaanbaatar can not be potential suppliers to them.

By the geographical map of coal-bearing basins and deposits [1], the Ukhaa-Khudag deposit is located in the South Gobi basin in southern Mongolia. The deposit is currently under mining as an open-pit. Despite its remote location 600 km south of Mongolia's capital Ulaanbaatar Ukhaa-Khudag coal was chosen to be processed throughout the industries for its good general chemical and physical properties such as elemental content, coal rank, porosity index etc. The mineable reserves of the Ukhaa-Khudag coal is estimated approximately 300 million tonnes.
Enriching processing of the coal is as following: the raw coal was first water washed and released from the water by the gas pressure in the factory. It is an intermediate production. Thus the intermediate product was ground up to size of 0.5 to 0.7 mm and even less. During grinding certain physical indices (eg, coal moisture content) was controlled to optimize further processing. A dopant, a typical and commercially relevant organic based product, was added. The dopant was chosen as an indicator for changes in the coal structure to minimize coal smoke during the burning. Unnecessary substances, such as heavy elements were separated from this intermediate product and dopant mixture via placing it in a magnetic field, afterwards an enriched product (it is a clean mixture) was compressed into a shape and supplied into the public market.
Such enriched production has been in use of direct burning for years in the capital city Ulaanbaatar, Mongolia.
Currently there is no substitutional variant of this production, except raw coal, moreover enriched coal manufacturing increases by several hundred thousand tonnes annually for the domestic demand.
However, scientific evidences in quality and quantification of these enriched coal are rare in publishings. Recently used method of using corn starch to suppress coal dust in coal production [2] or to prepare high performance of porous carbon [3] showed promising results of quality improvement.
Fourier Transform infrared spectroscopy is widely used to structurally reveal coal characterization [4, 5, 6], since it can provide crucial information on the molecular structure of organic and inorganic components of coal. The present paper is about earning some information of the structural differences between raw coal and enriched ones (intermediate and final productions) to show qualitatively the coal structural improvement.

Experimental

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Coal. The Ukhaa-Khudag raw coal was taken from the one borehole of the basin and labelled as "sample 1". Enriched, dopant added and compressed coal sample, labelled "sample 2" and enriched, power energy coal sample, labelled "sample 3" are from the factory located near Ulaanbaatar.
The sample preparation for the analysis was according to the standard procedures of the coal chemistry: A piece of coal was crushed initially and then milled to a size of 0.05 mm or less in an agate mortar in an argon gas flow glove box. At last, the milled sample was kept in a Schlenk flask under argon gas.
Dopant. As a dopant, soluble corn starch ( ), an organic polymer, a polysaccharide comprising of glucose monomers joined in linkages was chosen. The dopant sample is a finely dispersed, homogeneous powder.
Elemental analysis. Quantification of the elements and S in the raw coal sample were done with instruments of LECO at the Micro Laboratory for Organic Chemistry (ETH, Zurich). After processing the combustion products - carbon ( ), hydrogen ( ), sulfur ( ) and oxygen ( ) were analyzed quantitatively by infrared spectroscopy. Nitrogen ( ) was determined by a thermal conductivity detector. These variables are measured in weight percent (wt. %) and calculated in the air-dried (ad) base (see table 1).
Thermogravimetry analysis ( ) was done on a Mettler Toledo TGA/SDTA851 thermobalance. The thermogravimetry scan was carried out from 30 to temperature interval in a flow of nitrogen gas of high purity nitrogen ( ). Fixed carbon (determined in the presence of air at ) and ash content (the residual weight loss) are not measured in this method for some technical reasons.

Table 1: Elemental analysis result of Ukhaa-Khudag raw coal from LECO

C,ad | H,ad | O,ad | N,ad | S,ad
81.77 | 5.26 | 4.76 | 2.71 | 0.31

Inintially, the sample mass was 4.47 mg as measured on the Mettler Toledo AE 260 micro balance. Then, the sample was placed into the open alumni pan ( 70 uL ).
TG measurement is started with a cooled furnace and a cleaned pan. The pan is cleaned in a programmable furnace (Fisher Scientific) at for 60 min . The total time of being in a furnace is approximately 18 min for one sample.
Determination of moisture content. Moisture held within the coal itself is known as inherent moisture and is analyzed. Moisture may occur in four possible forms within coal: surface, hydroscopic, decomposition and mineral moisture. The sample is heated up to at a rate of and is maintained to be at every for 1 min . The weight loss is due to the water.
Determination of the volatile matter. Volatile components in coal are a mixture of short and long chain hydrocarbons, aromatic hydrocarbons and some sulfur. The sample is further heated up to at and is maintained at every for 1 min . The weight loss is due to volatile matter and total weight loss is due to coal volatile components.
The result from the TG method is a temperature-weight curve. The typical resulting curve is shown on figure 1. Two stages of weight are shown in figure 1 where a component loss is indicated. A calculation of the total weight loss values obtained from TG experiment are shown in table 2. The TG results in the table refer to a sample ground up to , the standard size for the proximate analysis. Two stages of pyrolysis of coal are found in two main temperature regions. The first with a

Fig. 1: TG curve for the Ukhaa-Khudag coal at a heating rate of in the nitrogen atmosphere.

Fig. 1. TG curve for the Ukhaa-Khudag coal at a heating rate of in the nitrogen atmosphere.

small weight loss before is to moisture elimination [7], it is somehow overlapped with a higher weight loss in the temperature range of , due to devolatilisation which results mainly from the decomposition of the organic coal substances and the mineral matter (water of hydration, carbon dioxide and sulphur compounds of the latter) [8]. Devolatilisation extends up to and here corresponds to a secondary degasification or active decomposition in the result of an evolution of various hydrocarbon gases [9].

Table 2: Proximate analysis results of the studied coal Ukhaa-Khudag, its production and the material that used for the coal enrichment

Element content, wt.% |  |  |  |
C,ad | H,ad | O,ad | N,ad | S,ad
81.77 | 5.26 | 4.76 | 2.71 | 0.31

Note: the results are taken from the State inspector assessment numbered 02-04-407 286 authorized by City Inspection Service
*the results are taken from the product supplied organization

Experimental setup for spectroscopy. The functional groups of the prepared samples were detected by the In situ Fourier Transform Infrared (FTIR) Nicolet iS5 FTIR Spectrometer at the Laser II laboratory, NUM, in absorption mode. Samples were well ground using a mortar for the infrared measurement. Thus, every sample was scanned in the range from 400 to . The beam splitter was Ge.

Experimental Results and Discussion

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Analytical data is provided by FTIR spectroscopy measurement. Absorption bands are spread in the short-wave ( ) and long-wave ( ) ranges of the FTIR spectrum (see. fig 2). In table 3 the characteristic organic absorptions and their assignments to the original coal are shown. In the spectrum of the raw coal, "sample 1", rarely visible weak peaks around 3684 and 3617

Fig. 2: The FTIR spectra of the samples. Ukhaa-Khudag raw coal labelled as "sample 1", enriched, dopant added and compressed coal sample, labelled "sample 2" and enriched, power energy coal sample, labelled "sample 3". The spectra of the samples 2 and 3 are magnified by 50 and 2 times, corresponding their weak intensity compared to the raw coal spectrum.

Fig. 2. The FTIR spectra of the samples. Ukhaa-Khudag raw coal labelled as "sample 1", enriched, dopant added and compressed coal sample, labelled "sample 2" and enriched, power energy coal sample, labelled "sample 3". The spectra of the samples 2 and 3 are magnified by 50 and 2 times, corresponding their weak intensity compared to the raw coal spectrum.

are altered by nonbonded hydroxyl group, stretch, like phenols and alcohol [10]. The absorption at is associated to a vibration in a hydrogen-bonded dimer in alcohol like

Hydroxyl groups vibrations are intensively significant weak in sample 2.
In the region of the group stretching vibration there are observed two

Table 3: Absorptions in the coal samples (Sample 1 - raw coal of Ukhaa-Khudag, Sample 2 - enriched, dopant added and compressed Ukhaa-Khudag coal, Sample 3 - enriched, power energy Ukhaa-Khudag coal) under the current study (sh - shoulder peak)

Bands, cm⁻¹ | Assignment
Sample 1 | Sample 2 | Sample 3 |
| 3869 | 3800 |
3684, 3617 sh | 3661, 3528 | 3645 | aliphatic O - H stretching
3000, 2960 sh, | 3036, 2913 | 2926, 2850 | aliphatic C - H stretching
1598 | 1674 | 1599 | aromatic ring stretch,high conjugated hydrogen bonded C = O
1437 | 1541 | 1440 | bending of CH₂ and CH₃ groups
1163 | - | 1165 | alkyl, ketones, group = C - O stretching
1084, 1032 | - | 1078, 1029 | mineral oxide stretch (Si - O or Al - O)
930 | - | 950 | isolated aromatic C - H,
797, 693 | - | 800, 740 | 2 or 3 neighbouring C - H bending

intense absorption bands with maximum at 2922 and . Bands at 2922 and 2845 are from the stretching (symmetrical and asymmetrical) vibrations of aliphatic group and relatively. Since a concentration of hydrocarbon is proportional to the integral intensity of the stretching band of group [11], in these coals predominantly in a state of is appropriate.
A broad band in the region of gives important information about the functional groups in the coal structure. Because of the overlap in this frequency range, interpretations are always ambiguous.
A peak centered near indicates a carbonyl group in an aromatic ring characterized by carbon or carbonyl absorption from conjugated hydrogen bonded carbonyl groups, or due to a donor and an acceptor interaction between aromatic carbon macromolecules.
The band around is assigned to the bending of and groups [12, 13]. The intensity of this band correlates preferentially with an intensity of bands from carbonate minerals in the region of . Despite the peak at that contributes to the presence of minerals [10], the identification of minerals such as clay in the current broad region remains very difficult because of their complicated and non-constant composition and also because band positions can vary highly due to isomorphous replacements.
Bands in the wide interval of indicate mineral components in raw coal. The presence of the bands of 1163, 1084 and 1032 is followed a vibration near that is resulting from the quartz existency [10].
The prominent bands in the short-wave zone are three bands at 930,797 and from an isolated out-of-plane deformational vibration (around ) or two-adjacent ( ) or threeadjacent (around ) of - hybrid bonded aromatic -groups.
Generally, the mineral fraction of the coal is a poly-component system provided that one mineral is not predominant and a complexity of the overlapping may preclude a straightforward interpretation. Thus, in according to the literature [14, 15] the various spectral bands at 1032, 797 and and lower are compatible with the vibration of minerals in different types.
Regarding the spectrum of "sample 2", the enriched, dopant added coal is firstly, a low intense and contains very few vibration bands. Vibrations in the short wave zone (that is in wave number) were obscured. Vibrations from carbonyl and carboxyl groups at 1674 and are barely observed. These frequencies tend to red shift compared to the corresponding peaks in the raw coal spectrum. Bands around are broader and blue shifted and relate to 3528 and that means the processing of the enrichment is followed a weak interaction hydrogen bonds among

Fig. 3: FTIR spectra a) sample 2, as received and dried b) sample 3, as received and dried. Samples were dried at for 24 hrs for the removal of water content.

Fig. 3. FTIR spectra a) sample 2, as received and dried b) sample 3, as received and dried. Samples were dried at for 24 hrs for the removal of water content.

phenolic and carboxyl groups. Water is difficult to be totally removed, therefore, a vibration around is in place. Mineral vibration bands were well eliminated by the washing and, magnetic cleaning process of the raw coal. The removal of various minerals is enhanced by adding a dopant to the coal (sample 2).
For the enriched, power energy coal "sample 3", a background broad band in the area of 4000 to 2000 is clearly observed. TG data shows an extremely low moisture content (fig. 1 and table 2) in the raw coal that is in good agreement with the corresponding frequency bands on the infrared spectrum. This suggests the containment of water in the sample 3 is higher than its raw coal. Relatively, a weak vibration around and a broad band at are observed in sample 3.
Quantitatively the same number of the vibration bands in the long wave region as in its raw coal has recorded in sample 3. It might be that a broad background band overlapped on the vibrations in the short wave area in sample 2 and 3 after the coal enriching processing is blurring the actual absorbance vibrations in this area. In other hand, this is the evidence of water containment due to the sample preparation regime in which the imperfection of water removal processing. The moist condition in coal processing allows bonds to form solutes. Water contains both hydrogen donor groups interacting with (halogenated compounds) and non-bonded electron pairs such as ethers, aldehydes and tertiary amines. The position of the band (around ) shows a vibration of stretching like

Another broad band in this area at in the sample 3 can be associated to normal "polymeric" OH stretching. These hydroxyl compounds frequencies are stemming from the processing of the coal. Base on above, we removed excess water containment via drying sample 2 and 3 in an oven at for 24 hrs in the presence of air and measured FTIR spectra (fig.3). In both samples water excess background spectra clearly removed as shown in figures 3a and 3b. In details, several absorption bands of stretching vibrations such as groups ( ), polymeric (ethers) ( ) were extracted in samples. Therefore, we see the actual functional groups existed in the samples. The absorption at in dried samples (particularly, in dried sample 3) is evident of stability of self associated and hydrogen bonds. Dried sample 2 still preserves absorptions around 3400 , moreover, the carboxyl (in the ) and mineral fragments in the long wave number ( ) now possibly are observable.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.5.63.pdf

Structural characterization of enriched coal products, prepared from the well carbonized Ukhaa-Khudag coal, was made in the field of infrared spectroscopy. Carbon content in the raw coal was estimated by elemental analysis as high as . In this analysis the content of hydrogen, oxygen, nitrogen and sulphur was determined as well. TG analysis reveals low content of moisture ( ) and volatile matter ( ) in the raw coal.
The registered vibration bands in the infrared spectrum of the raw coal were well related to the elemental and TG analysis results.
Enriched and dopant added "sample 2" exhibits an essential change in the infrared spectrum, the main vibrations from the "sample 1" tend to blurring. The coal enriching processing has resulted the blurring of hydroxyl and neighbouring aliphatic bands in the short wave and oxygen contained carboxyl and neighbouring aliphatic vibration in the long wave region. In sample 2 we see long wave vibrations after the drying process that demonstrates structural affections by doping processing.
The infrared spectrum of the "sample 3 " contains most of the peaks as in "sample 1". The processing induced broad band near that is assigned to the normal "polymeric" OH stretching. Moreover, hydroxyl group peak around in the spectra of "sample 2 and 3 " is caused by the water existence during the coal enriching processing. Imperfection of industry processing leaves a certain content of water in the structure of the coal that gives a change in, particularly, spectrum integral intensity to decrease. Water excess removal shows the stability of self associated and hydrogen bonds in the samples.
The results present a significance to the diminishing heavy volatile parts in the form of smoke or dust under the direct burning use of coal via removing functional groups in the structure.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/EI.5.63.pdf

The research has received a support from Foundation of Science and Technology, Ministry of Education and Science, Mongolia, under grant agreement 3254.

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The original version of this paper is available on https://www.scientific.net/EI.5.63.pdf

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Keywords Index

B

Bearing Faults Dynamic Modeling and Analysis of Rotor-Bearing System with Localized Defect in Rotating Machinery

Bioreactor Forward-Backward Propagation to Identify the Maximum Specific Growth Rates of a Bioreactor

Bituminous Coal FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

Bop Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Buffer Solution IoT-Based Real Time pH Monitoring of University of Mindanao's Chemical Laboratory Wastewater

C

Columns Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

Concrete Quality Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

Crushing Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

D

Damage Model Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Data Analysis Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Deflection Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

Dopant FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

E

Enriched Coal FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

Estimation Forward-Backward Propagation to Identify the Maximum Specific Growth Rates of a Bioreactor

F

Fea Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Forward Backward Technique Forward-Backward Propagation to Identify the Maximum Specific Growth Rates of a Bioreactor

Ftir Spectroscopy FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

I

Interval Arithmetic Method Forward-Backward Propagation to Identify the Maximum Specific Growth Rates of a Bioreactor

J

Jacketing Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

L

Lot IoT-Based Real Time pH Monitoring of University of Mindanao's Chemical Laboratory Wastewater

M

Machinery Diagnostic Dynamic Modeling and Analysis of Rotor-Bearing System with Localized Defect in Rotating Machinery

N

Non-destructive Tests Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

P

Ph IoT-Based Real Time pH Monitoring of University of Mindanao's Chemical Laboratory Wastewater

Pipe Shearing Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Plumb Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

S

Signal Analysis Dynamic Modeling and Analysis of Rotor-Bearing System with Localized Defect in Rotating Machinery

Simulation Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Solenoid Motor IoT-Based Real Time pH Monitoring of University of Mindanao's Chemical Laboratory Wastewater

Strengthening Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

V

Vibration Analysis Dynamic Modeling and Analysis of Rotor-Bearing System with Localized Defect in Rotating Machinery

Authors Index

A

Arunyanart, P. Dynamic Modeling and Analysis of Rotor-Bearing System with Localized Defect in Rotating Machinery

B

Borsali, S. Forward-Backward Propagation to Identify the Maximum Specific Growth Rates of a Bioreactor

G

Givens, M. Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Goolimensee, S. FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

H

Hua, E. Theoretical Study on CO2/SO2 Absorption Using N-Alkylethylenediaminium Protic lonic Liquid

Huang, H. Theoretical Study on CO2/SO2 Absorption Using N-Alkylethylenediaminium Protic lonic Liquid

L

Lawagon, C. IoT-Based Real Time pH Monitoring of University of Mindanao's Chemical Laboratory Wastewater

Li, K. Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

M

Makunza, J. Strengthening of a Damaged Concrete Structure Case Study of an Eleven Stories Building in Dar es Salaam

Morales, E. IoT-Based Real Time pH Monitoring of University of Mindanao's Chemical Laboratory Wastewater

N

Nault, C. Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

O

Oleshkevich, A. FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

R

Renten, N. FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

S

Sambuu, M. FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

Sharav, M. FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

Sudsawat, S. Dynamic Modeling and Analysis of Rotor-Bearing System with Localized Defect in Rotating Machinery

T

Tschaggelar, R. FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

Tumendemberel, B. FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

Tuvdendor, O. FTIR Study of Enriched Bituminous Ukhaa-Khudag Coal

W

Wang, Y. Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data

Z

Zhang, H. Test-Free Simulation Method for Blowout Preventer Pipe Shearing Powered by Data