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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

Calculations were done for the same data as calculations for the active system presented in the previous section.
The FPGA module enabled safe and quick implementation of the control algorithms, while the RT processor managed data exchange with external devices such as memory modules and the operator’s console.
Fig. 7 The electric part of the laboratory workstation The measurement system was adjusted to convert data, sample by sample, with the maximal sampling frequency allowed for the measurement card and transducer.
To avoid problems related to synchronization of measurement data and limitation of maximal sampling frequency, an additional measurement subsystem was implemented.
The applied solution enabled hardware synchronization of data recorded in packets.

In the context of industrial noise reduction, data-driven models are able to analyze large amounts of data, which include gear modification parameters or even manufacturing parameters and their associated noise levels.
The first step in the workflow was to collect data.
The raw data set was divided into training and test sets.
Using synthetic data, the models predicted noise levels with similar accuracy as they did on real data. 4.
Future research directions: • More data: The accuracy could be further improved by incorporating larger data sets

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.

In order to promote emissions reductions, carbon emissions reductions shall be discounted.
Selected a representative and large power plant as a case, all the latest data are from the enterprise’s official website.
The main data are shown in Table 1.
Table 1 Data Descriptions of Selected Power Plant Variables/Parameters Value Variables/Parameters Value Reduction period (year) 5 Project’s life cycle (year) 20 Predicted emissions in 5 years (104ton) 450.09 Reduction emissions discountrate (%) 8 Permits distribution in 5 years (104ton) 546.38 Annual return ratio on retudtion investment (ton CO2-e/Yuan) 0.0038 Permits price (RMB Yuan) 25.68 Limits of (104Yuan) 5067.89~ (1) Considered that China's economic and social development plan is usually adjusted for every five years, the reduction period is also assumed as 5 years.
According to the above decision model and related data, the optimal range of emission reduction investment is 5067.89 to (unit: 104Yuan, see Table 1).

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.

Lastly, after reaction rate and time of reaction has been determined, the reduction process can be calculated based on following equation: Roρc=[1-(1-R')1/3]=Kt (15) Where R’ = fractional reaction (instantaneous weight of pellet/ initial weight of pellet)sphere Ro = initial radius of the reacting ρc = molar density t = time K = constant Mo = initial weight of pellet (Fe2O3) R’ = (Mo-Mt)/ (Mo-Mf) Mt = weight of pellet at time t (Fe2O3 + Fe) Mf = final weight of pellet (Fe) Results and Discussions Four sets of independent experimental data were generated with different experimental conditions which were employed for different furnace temperatures and porosity based on previous work of Tan, 2012[6].
The set of data combinations with temperature of 700°C and the porosity of 20%, temperature of 700°C and porosity of 40%, temperature of 800°C and porosity of 20%, and temperature of 800°C and porosity of 40% had been tested.
Reaction rate increases rapidly as reduction proceed.
However, the model used does not fit ideally with our experimental data.
Thus, it is obvious that non-isothermal is inclined better to experimental data compared to isothermal predicted transport limited reaction rate for hydrogen reduction of ferric oxide kinetic modeling and obeys the theoretical framework of ore reduction.

The new algorithm has been tested based on the Chang’E Data in the Matlab environment.
Their mission is to collect many different types of data at different times and even from the different viewpoints.
Results have been put together and compared with the original data (Figure 4).
Results of the new algorithm for the No.2 area And after these results have been put together and compared with the original data (Figure 6).
[3] LIU han, JIANG HongKun, TIAN XiaoLin, Xu AoAo, A New Fast Auto-Extraction Algorithm of Lunar Craters Based on the Chang’E Data, DEStech Publications, E113

The reported mechanism does not provide information for the reduction kinetics; however, it gives certain suggestions how reduction might occur in complex ore minerals.
After reduction the crucible was cooled down with a furnace to room temperature.
The captured micrographs were analysed using ImageJ 1.8.0_60 software enabling to obtain the data about quantity, size and distribution of the metal particles, Fig. 4.
The data was sorted and entered in RStudio 1.0.143 program, where the normal distribution and homogeneity of variance of the metal particles were estimated.
Vinters, Gaseous Reduction of Iron Oxides: Part III.

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