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The bioreactor systems are operated under increasingly stringent conditions through the reduction in the hydraulic residence time.
Biological sulfate reduction represents an option for ARD remediation.
Thereafter, the matrix-attached cells were detached in seven sequential steps comprising vigorous agitation for 2 minutes in reactor feed containing 0.4% (v/v) Tween® 20, demonstrated previously to remove the dominant fraction of the microbial community (data not shown).
The SEM micrographs of the colonised carbon microfibres and polyurethane foam corroborate the cell count data (Fig. 1) which indicates more colonisation of the polyurethane foam compared to the carbon fibres.
Marais, S.T.L Harrison, Biomass retention and recycling to enhance sulphate reduction kinetics.

Processing methods of the line points cloud data 2.1 Preprocessing technology Generally, the data processing work can be divided into these methods, such as filtering of point cloud data, data multiple visual alignment, feature extraction, and data segmentation.
And the key pre-processing technologies of line point cloud data mainly include multi-view split, noise removal, data reduction and gridding.
Noise reduction processing can make data smooth, reduce the deviation of the model.
The main operation processes is as following: data package, interception of feature section, sheet data import to CAD, extract data as demand.
Summary Against the features of line point cloud data, establish the appropriate track model, by filtering of point cloud data, data multiple visual alignment, feature extraction, and data segmentation.

Development of a baseline methodology The methodology includes two parts which are the power supply part calculation of the CO2 emission reduction and the heating part calculation of the CO2 emission reduction. 1.
According to the data of the project, CO2 emission reduction can be calculated as Table 2.
Table 1 Parameters of IGCC power plant Parameter Unit Value Installed capacity MW 250 Power generation efficiency % 48 Running time h 5000 Power generation efficiency of the baseline % 35 Power used in site rate % 3 Desulfurization efficiency % 99 Dedust efficiency % 100 Bituminous coal calorific value GJ/Kg 0.02717 Thermoelectrical ratio [4] % 35 Table 2 Annual emission reductions (unit :) CO2 emissions Baseline emissions Project emissions Leakage Emission reductions Power part 1,083,429 860,269 0 223,160 Heating part 240,875 0 0 240,875 Total 1,324,304 860,269 0 464,035 Environmental benefit and economic benefit According to the 250 MW IGCC power plant data, it can be calculated that IGCC power plant can save 227,763 tons of coal per year [5], and at the same time, the following pollutants (Table 3) can be reduced annually.
Table 3 Pollutants reduction annually (unit: t) Pollutant Baseline emissions Project emissions Leak Emission reductions SO2 634.1 25.5 0 608.6 NOx 4143.7 2496.1 0 1647.6 Smoke 190.3 0 0 190.3 According to the project, it can be calculated for project CO2 emission reductions (CERs) income under the condition of internal rate of return (IRR), detailed data shows in Table 4.
By using the methodology developed, the IGCC emission reductions are calculated.

Combining rough set attribute reduction with SVMR theory, using rough sets as a front-end processor and attribute reduction to eliminate redundant attributes, this paper will train and test obtained data by SVR method.
The major steps are: (1) Data preprocessing Screening, filling historical data and eliminating one of the singular data (2) Selecting a set of similar days According to the type of forecasting day, under the premise of properties of season, using similar day theory to select similar days which relational degree as sample set of rough set attribute reduction.
Therefore, this set of data selected as attribute values of the train and test sets of SVMR.
According to historical data of PV plant, this method is able to forecast output directly, and it can avoid specific modeling of inverter model for PV systems, collecting and converting process of illumination data.
On the premise of completeness of information, it can provide streamlined modeling data for subsequent data fitting that using rough set theory to do discretization and attribute reduction analysis for decision table which consists of a variety of influential factors and output.

So it can destruct the marine environmental factor data in a fast and effective way, and make the data be unrecovered, thereby prevent data leaks, ensure data security.
l Chemical corrosion damage method: using the chemical reagent, will drive disc magnetic medium corrosion damage, so as to avoid the data reduction.
l Data overwriting method: also known as data wipe division [7].
Data delete method can't completely clear data, use some special software recovery tools can make the data reduction; Strong degaussing method, burning high temperature destroyed method, physical damage method, chemical corrosion damage method these several ways need professional destroyed equipment or chemical reagent, and require manual intervention to remove storage media data destruction from buoys, it is unrealistic for marine environmental monitoring buoy data storage module.
Data overwriting Data overwrite method is based on the U.S.

Therefore, an advanced data analysis and processing tool (i.e. efficiency data mining technology) is introduced for analyzing and mining large amounts of data monitored with the smart industrial park system.
Data service In the smart industrial park system, the data in all automation systems is extracted, cleaned, transformed, and synchronized through the data integration, and also the data is converted into a unified model format and stored in the database.
From the data, knowledge is mainly extracted by data mining algorithm.
Preprocessing data Data preprocessing can be divided into data integration and data cleaning.
The grey prediction model and data smoothing processing are studied in depth, and also the processing correction methods of electricity data missing and abnormal data are proposed.

Iron reduction rate of Fe, Cu and Ni can be elevated to above 90% by smelting reduction, thus the comprehensive utilization of valuable metals can come true.
In this study, results of the “Double slag” smelting under the condition of smelting reduction have been analyzed from a thermodynamic point of view, and experimental verification of the possibility of “Double slag” smelting under the condition of smelting reduction also has been carried out. 1 Thermodynamic calculation According to the preliminary work the optimum conditions of the “Double slag” smelting reduction can be determined as follows: the slag basicity is 1.1, the adding ratio of JISCO slag is 10%.
Substituting the above data into Eq. 8, the content of manganese in liquid iron can be obtained as follows: . 3 Experimental study on smelting reduction In order to verify the results of thermodynamic calculations, smelting reduction experiment of the “Double slags” was carried out in the electric arc furnace with lining of L3.
Table 4 Tapping output and reduction rate of “Double slag” smelting Test number D1 D2 D3 Tapping amount/ kg 5.6 5.4 5.4 Reduction rate/ ％ 94.9 91.5 91.5 Table 5 Hot metal composition of “Double slag” smelting/ % Test result C Si Mn P S Ni Cu Co D1-T 3.84 0.17 0.21 0.022 0.034 0.85 0.420 0.001 It can be seen from Table 4 and Table 5, in the smelting conditions identified in this process, the iron reduction rate and hot metal chemical composition of double-slag smelting is relatively stable, the iron reduction rates were maintained above 91%, the reduction rate of Ni, Cu in hot metal is above the level of 95%.Compared with the calculational results in Section 2, the content of Si, Ni and S in the experimental results is close to the calculated results, while there are some differences between Mn content and the calculated results, which are mainly attributed to the following reasons, first, the test temperature in the bath is not equal to 1600℃, second, approximate calculation method
Conclusion (1) Test conditions are shown as follows: the weight of Jinchuan slag was 13.5kg, and the weight of JISCO slag was 1.5kg, the composition of the slag has the basicity of CaO : SiO2 = 1.1, the use level of reducing agent H4 was 1.8kg, smelting current strength was 1900A, smelting time was 20min, during the smelting reduction process, reduction rate of iron remained above 91%, the reduction rate of Ni, Cu in hot metal was above 95%; (2) Through thermodynamic calculation, it is found that the content of each element in molten iron of the calculation results is close to that of the experimetal results, thus the purpose of guiding experimental procedure has been achieved; (3) Low alloy iron produced by “Double slag” melting reduction can be smelted into high value-added spring steel.

Automatic control technology in power generation especially in energy conservation and emission reduction is briefly introduced in the paper.
So control sulfur dioxide emission caused by the process of coal consume is key point of energy conservation and emission reduction.
Workshop process flow of smoke desulphurization Control system of wet limestone smoke desulphurization has such functions: data acquisition and storage, auto protection, automatically control and adjustment, production management.
The function of data acquisition and storage mainly include data acquisition, screen display, data process, online performance calculation, event recording and tracking.
The function of automatically protection means that PLC and host computer based on comprehensive analysis to acquired data to decide if there some accidents, then automatically alarm and take action to prevent the accident occur or extend in order to protect people and equipment.

Reduction in area is a measure to determine formability on cold heading.
Reduction in area is affected by second phases and inclusions.
The test data of conventional ferrite+pearlite steel [6], tempered martensitic steel [6] and bainitic steel [7] are also plotted in Fig. 3 for the purpose of comparison.
This indicates that reduction in area determines the formability of screw heads.
[6] NIMS data base, Materials Information Technology Station, National Institute for Materials Science, Tsukuba, Japan

Substantial increase in the number of data dimensions has brought unprecedented difficulties to the cluster; therefore, before using FCM algorithm, the original sample data reduction has very important significance.
However, dimensionality reduction by manifold can find low dimensional embedding hidden in high dimensional data.
This paper uses manifold dimensionality reduction algorithm for high dimensional data, existing methods use dimensionality reduction for feature vector firstly, further FCM training, this type methods of dimensionality reduction do not use the data correlation .
FCM algorithm base on L-Isomap dimensionality reduction Isometric mapping algorithm built on the basis of MDS[2],Use local neighborhood distance calculate approximate manifold geodesic distance of data points, Complete data reduction through the establishment reciprocity between geodesic distance of the original data and spatial distance of dimensionality reduction data.
Assumedis data set,N is the number of samples, C the number of data set is divided into .