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Increase of reduction time and temperature led to total iron reduction in the whole ore lump.
Reduction of lump ore by graphite at 1200 ºC for 240 min reduction time.
According to the data obtained using the X-ray phase analysis, after reduction experiments the ore lumps contained metallic iron (with manganese impurity according to the data from electron-probe test), silicate with (Mg1.145Fe0.855)SiO4 composition and small amount of FeO.
Reduction of iron from sideroplesite at 1200 ºC for 60 min reduction time: (a) – by graphite; (b) mixed with coal.
According to the data obtained by using the synchronous thermal analysis device STA449C Jupiter, the process of dissociation developed at 350…700 °C in 2 stages [10].

The Forecasting Model Based on Data Mining Technology The data mining also called the knowledge discovered in the database is to discover the effective, novel, having the latent value knowledge from the mass data.
T0 (m)), then each historical water pollution density data all all regarded as a vector.
Forecasted Result Analysis The following data come from Shayin River.
The data include sides. one is the water level,water flow,density of COD of upstream of the river.
Rough Set Spatial Data Modeling for Data Mining.

The influences of reaction temperature and time on the reduction ratio of magnesia were studied.
The reduction ratio of magnesia increases with the increase in the reaction temperature and time.
The wMg is the weight fraction of magnesium in the briquette before thermal reduction.
Data were collected over the 2θ range of 10-90º.
Specially, the reduction ratio of magnesia can be up to 73% after 1 hrs aluminothermic reduction at 1200°C.

The growth rate of GDP has no direct impact on the realization of emission reduction targets.The higher reduction benchmarkdoes not restrict the successof emission reduction targets.
Table 1 Decoupling status identification table DE ΔCO2 ΔGDP Decoupling Status DE<0 <0 >0 Strong decoupling >0 <0 Strong negative decoupling 0≤DE<0.8 ≥0 >0 Weak decoupling ≤0 <0 Weak negative decoupling 0.8≤DE<1.2 >0 >0 Expansive coupling <0 <0 Recessive coupling 1.2≤DE >0 >0 Expansive negative decoupling <0 <0 Recessive decoupling Data Sources and Processing GDP data is from China Statistical Yearbook.
In order to guarantee the comparability of GDP, as well as analysis of carbon emission reduction targets for 2020, the GDP of every province is calculated based on 2005 constant price.The energy consumption data is from China Energy Statistical Yearbook.
The reduction targets could be achieved in regions with lower reduction benchmark, by relying on significant technic breakthroughs.
Higher reduction benchmark will not restrict the reduction targets to be achieved.

Implementation of ERP system involves changes in management idea, business process reengineering, data integration, computer hardware and software, software vendors, consulting firms, etc., and are considered as a typical of complex system project.
Rough set has been already applied in many areas such as data mining, artificial intelligence, control and decision-making, pattern recognition and fault diagnosis, medical diagnosis; and also achieved encouraging results.
For attribute, (3) is used to measure the importance of attribute to the equivalence relation and . is positively related with the attribute ; Attribute Reduction Algorithm Decision-making table is developed in accordance with the data from the company implemented ERP.
When rough set is used for attribute reduction and rule extraction, only discretized data can be dealt with.
Pawlak，Rough set theory and its applications to data analysis[J].

On account of severe water pollution condition, this paper combines structure emissions reduction, engineering emissions reduction and management emissions reduction (namely SEM emissions reduction), builds an optimization model for total amount control of regional water pollution, and puts the above model into practice to validate its validity and reliability.
Constraint of Emissions Reduction Goals for Regional Water Pollutants.
Constraints of Industrial Emissions Reduction for Regional Water Pollutants.
The data about water pollutants increments in different industry are shown in Table 1.
Table 1 Increasing amount of each water pollutant of different industries in 2015 (unit: 104 tons) Pollutants category Industry categories To 2015 Industry Urban Agriculture COD 9.27 14.21 11.13 34.6 NH3-N 1.43 1.55 0.28 3.26 Combining the 12th Five-year environmental protection plan for total amount control of regional water pollutants in the case province, as Table 2, using the main control pollution data in 2010 as the base data of environmental emissions, the reduction of COD and NH3-N should be achieved 44.3 (104 tons) and 11.05 (104 tons) by calculation.

The base data of power plants are shown in table 1.
Tab.1 Base data of power plants Power Plant Net coal consumption rate/(g/kWh) SO2 mg/m3 NOx mg/m3 EAF Net loss/% Water consumption t/h Power plant 1 340 390 380 0.90 6.7 800 Power plant 2 330 380 350 0.91 6.9 820 Power plant 3 310 61 310 0.92 7.1 200 Power plant 4 320 55 120 0.91 7.2 80 Power plant 5 303 65 250 0.90 7.3 350 The indexes of energy-saving and emission reduction and the comprehensive evaluation indexes are shown in table 2.
In order to compare the effect of energy-saving and emission reduction, the basic data of power plant 3, 4, 5 should be calculated.
Tab.3 Calculation data of power plants Plant Coal consumption t/h Flue gas/(m3/h) SO2 kg/h NOx kg/h Water consumption t/h Order Power plant 3 186 1116000 68 245.5 300 2 Power plant 4 192 1152000 57.6 138.2 80 1 Power plant 5 181.8 1090800 81.8 272.7 350 3 According to the table 3, although the coal consumption of power plant 5 is low, compared to power plant 4, the SO2 and NOx emission per hour is higher.
According to the comparison of the computational data, the proposed evaluation method of this paper can fully reflect the situation of energy-saving and emission reduction.

The knowledge discovery process has the following steps: data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, knowledge representation.
The data mining module according to the model library system model, the data warehouse multidimensional data sets for data mining, in order to obtain the required Knowledge.
On physical education comprehensive quality index by, through data mining based on rough set attribute reduction algorithm.
The C2 attribute significance degree is 0, so do not select C2 joined the reduction set, i.e. the last attribute reduction set is Red = { Ratio of height and weight, Fitness function } Insect.
Advanced Scout: Data Mining and Knowledge Discovery in NBA Data.

And then, the actual reduction ratios were optimized incorporation to the relative reduction ratios of the sub-regions and the total emission reduction target of the province by means of GA, a new optimizing method [9].
The basic idea of TOPSIS-GA are 1) to obtain the relative reduction ratios of the sub-regions by TOPSIS, 2) to optimize the actual reduction ratios of the sub-regions by GA, and 3) to calculate the reduction responsibilities of the sub-regions based on the actual reduction ratios and base-year emissions.
Specific steps are as follows: 1) Construct data matrix , , (1) where, m is the number of sub-regions, n is the number of indexes, is the original value, is the normalized value, ,, is the weighted value, , is the weight of index j, determined by entropy-weighting method as Eq. 2
The data used in this article was collected by ‘Shandong Statistic Year Book 2011’ (Table 2).
The reduction ratios of Dezhou, Liaocheng, Binzhou, Rizhao and Zaozhuang are higher than the province’s average reduction ratio (12.9%).

From these data the fraction reacted after 15, 30, 45, 60 minutes were calculated.
Data of ‘Factor’ value for Chromite Pellets Pellet (Chromite) Factor CR/99/1 0.2017 CR/89/10C/1 0.2648 CR/89/10CC/1 0.2778 CG/99/1 0.1683 CG/89/10C/1 0.2348 CG/89/10CC/1 0.2478 CB/99/1 0.2097 CB/89/10C/1 0.2721 CB/89/10CC/1 0.2851 The wt. loss during reduction of composite chromite pellet (Chromite + Coal/Charcoal) is due to the loss of O2 associated with Cr2O3 and FeO and also because of the loss of the carbon in it.
Hence, the product gas could be safely assumed to be CO and the fraction reacted be calculated as follows: = f* x 16/28 The data for fraction reacted (f) against time (t) are tabulated in Table III.
The data fit on linear plot passing through the origin for first order reaction.
Ray Reduction of I.