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The kinetics showed the AO7 reduction rate can be greatly improved by the addition of sulfate and RF, thus it is possible to speed up the start-up of AO7 reduction system under appropriate condition.
For this reason, oxidation following reduction of the –N=N– is favored for the degradation of azo dye, with reduction being the rate-limiting step of the overall process.
The balance between AO7 and SA (data not shown) indicating that the reduction plays a major role in the AO7 decolourising process.
Data shown in Fig. 2 indicated that the absence and presence of RF made no big difference, while the existence of sulfate can greatly influence the variation of the electron donor.
The kinetics of AO7 reduction.

Some data about TPA-80, including 8-cage continuous and 24-cage reduction mills, have been published earlier [13].
Statistical Data Processing To improve the technological rolling modes to reduce the thick ends length, further analysis and processing of factual data on the pipes wall thickness ends was performed.
Thick ends sizes after reduction mill were defined based on factual data.
The shape and size of thinned ends were determined from factual data.
Bibliographic data: 2003-03-04

It is proved that the application of fuzzy control in vehicle emission reduction is feasible through the processing and analysis of test data.
It does not need to the data processing of blur and eliminate blur in this study, because there are only 13 conditions measured in tests.
Put the original test data into formula (1) ~ formula (7), the urea solution needed theoretically to completely eliminate NOx under ESC conditions are obtained as shown in table 3.
Through look-up table, called the corresponding output values while in different conditions, measured the NOx values processed, the data are as shown in table 6.
The application of fuzzy control in vehicle emission reduction is feasible 2.

These methods deal with the whole data set rather than selected some samples randomly and aim to dig correlation among data rather than causality, thus they can be believed taking philosophy of big data analytics.
Big data analytics not only emphasis the huge volume of data but also imply that the collected data set covers almost the whole population.
On the other hand, big data analytics abandon the exact formulation of causality and forecasting with the correlation among data.
Big data analytics employ different philosophy with methods of the existing data mining to deal with data and has been applied to many areas successfully.
The huge volume and complexity of collected data from thermal power units strongly motivate us to mine them by employing idea of big data analytics.

Every original variable is conveyed by k factors (f1, f2, f3,……,fk) of the linear combination: (1) xi means the index data what measured in practice.
The method of data processing such as the influencing factor of energy-saving and emission-reduction can be referenced by literature [7], the energy efficiency x1 can be calculated by formula 3, as is shown below: (3) The rest data of this paper obtained from Jiangxi Statistical Yearbook 2008-2011, the unit of energy consumption was transformed to standard million tons of coal both in 2007 and 2008.
We need not to deal with positive indexes which are used in data analysis.
Data standardization is used to comparing variables and eliminating the influence which caused by difference of observation dimension and the order of magnitude.
Eigenvalue, contribution rate of eigenvalue and cumulative contribution rate can be obtained through data statistics from 2007 to 2010 by using SPSS software analysis, as is shown below: Table 1.

It provides basis and reference for the optimization of drag reduction for the vans.
The pressure and velocity analysis Body surface pressure is an important data to characterize the performance of the car.
Taking the van without a dome as a comparative object, by analyzing the data of the measured wind resistance, the changes of wind resistance before and after the installation of different types of domes may come to a conclusion.
To minimize the test error of wind resistance on van model, for the same model, continuous test three times to take the average, and take it as the data of wind resistance this type of van model suffers.
When the wind speed is 40m/s, the maximum drag reduction rate reaches to 16.9737%.

The model fitted quite well with the collected data, suggesting that the model was applicable.
So it is greatly necessary to develop new technologies for the reduction and disposal of waste sludge.
Data processing and model evaluation Nonlinear fitting analysis and significance test were done using SPSS17.0, Origin 7.0 and 1st Opt software.
Fig.3 The uptake and release curve for TOC: a, the uptake curve; b, the release curve Fig.4 The uptake and release curve for TN: a, the uptake curve; b, the release curve Fig.5 The uptake and release curve for TP: a, the uptake curve; b, the release curve Non-linear fitting method and related equations (3) and (4) were applied to the experimental data.
Based on data given above, the specific release rate of pollutants by Tubificidae in dry weight could be calculated.

To make up for a lack of past studies, based on the autoptical data in Dali river, a tributary river that has the most dams and reservoirs in He-Long region, analysis and studies were carried out on sediment reduction effects by soil retaining dams with different allocation proportion in this paper.
Sediment reduction by different types of dams is given in Table 1 [9].
In August 1994, the sediment transport was 100 million ton annually because of heavy rainstorm, on August 10, 1994, the daily sediment transport was 33.8 million ton, the annual sediment transport modulus is 25700t/km2; the annual flood sediment transport would be 118.7 million ton by restoring sediment reduction with slope surface measures, accounting for 48.1% and 57.1% of the annual sediment transport of 208 million ton by Wuding river that year, being the maximum value in the observed data series.
The maximum sediment reduction capability (sediment reduction capability) of different types of soil retaining dams correlates closely with flood season rainfall.
Within the sediment reduction capability of soil retaining dam, the sediment reduction increase with more rainfall and more incoming sediment in the flood season, having the feature of “the more incoming sediment is, the more sediment reduction will be”.

The paper proposed a new evaluation indicator for reduction effect and introduced the formula of reduction rate.
The new reduction rate formula solved the problem.
Our experiment data come all from the real-time data of a large steel company.
[6] Boley D, Cao D W,Training support vector machine using adaptive clustering, Proceedings of International Conference on Data Mining, Florida, 2004,pp. 235-242
[7] Yu H, Yang J, Han J W, Making SVMs scalable to large data sets using hierarchical cluster indexing, Data Mining and Knowledge Discovery. 11(2005) 295-321

To eliminate the redundancies, noises, incompletion, and inconsistencies in the data set of sluice monitoring, a method of data preprocessing to implement data mining is proposed by integrating the data preparation process in data mining and data warehouse [4].
Data preprocessing Before starting data mining, data preprocessing should be performed to eliminate or reduce the the redundant, noise, incomplete or inconsistent data.
Processing Data mart Monitoring DB Hydrological, meteorological DB Other data Cleaning Integration Transformation Reduction Data Warehouse Data sets Extraction Loading Input Output Fig. 1 The process of data preprocessing Beginning from selecting the data sources, the operation of data preprocessing searchs the existing rows (records) and columns (attributes) related to the data mining tasks, then performs data cleaning, integration, transformation, and reduction based on the result data sets after extraction, and in the last the process is finished when the data processed are stored in the specified data mining sets.
The data extraction and loading are the operations that must be implemented whatever the data are from any kinds of sources, but it is not necessary for data cleaning, integration, transformation, and reduction in the processing stages.
Only when the data preprocessing is finished can we get clear data to perform data mining operations.