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Key words: BrO3-; Titanium Dioxide; Photocatalytic Reduction Kinetics.
Their experimental data showed that the kinetic curve of the bromate photocatalytic removal at pH 5.0 was very different from that at pH 7.0 and the bromate removal was greatly promoted at pH 5.0.
To verify the effect on bromate reduction at low pH, the bromate reduction was investigated under dark condition at pH 3.0.
However, in fact, they still play a certain roles in promoting bromate reduction.
In the existence of Fe3+, the bromate reduction is easier than other cations.

As 2 waste-water treatment stations operation results have shown, during the passed 6 years, annual sludge reduction ratios (per tons product) reach 43.64% and 50.29% respectively, realizing 105.6 thousands tons of sludge reduction effect and 68.04 millions CNY economic benefits.
The results show that sludge pellet reduction, reduction swelling index, compressive strength and leaching toxicity to meet the requirements, but its softening melting property is poor.
Therefore, the sludge pellet and sinter ore, lump ore are mixed by the ratio of 10:85:5, and its softening melting property is 1508 ℃ with the melting interval is 111 ℃, the maximum differential pressure is 3103Pa, meeting the requirements of blast furnace. 3)Sludge amount reduction effect During Sept., 2010~Feb,2014, sludge amount reduction technical transformation project have been carried out with 57,576 tons sludge reduction achievement(based on the basic data that per ton steel produces 50.18 kg mixed sludge, theoretical sludge amount is 114,487 tons,while actual sludge amount is 56,911 tons), reduction ratio reaches 50.29%,and annual sludge reduces 16,450 tons.
Based on the basic data that per ton steel produces 17.49 kg mixed sludge, during Sept., 2011~Feb,2014, theoretical sludge amount is 93,594 tons,while actual sludge amount is 60,503 tons, sludge reduction ratio reaches 35.36%,and annual sludge reduces 13,000 tons. 2)Recovery of waste sulfuric acid : During Sept., 2011~ Feb,2014, 5224 m3 waste sulfuric acid has been taken out from wastewater to produce polymeric ferric sulfate (water purifying agent), obtaining multiple benefits, such as wastewater neutralization agent saving, sludge amount reduction, acid raw material purchase cost saving,etc. multiple benefits. 4.
During the passed 6 years(A Plant, 2.5 years; B Plant, 3.5 years), annual sludge reduction ratios (per tons product) reach 43.64% and 50.29% respectively, realizing 105.6 thousands tons of sludge amount reduction and 68.04 millions CNY economic benefits.

Some researches, chiefly focuses on potential and ways of energy conservation and emissions reduction, are mostly based on energy conservation and less on emissions reduction.
Water Pollution Emission Reduction Measures.
This study was to obtain the potimal emision reduction scheme based on a selected typical enterprise parameters to verify the feasibility of the established model, the specific data were shown in Table 1 to Table 3.
Table 1 Data for pollution reduction potential model of refining and chemical enterprises Pollutant Pollutant source Industrial emissions standards(mg/L) Wastewater quantity(t/h) Pollutant concentration (mg/L) COD Catalytic reforming unit(CRU) 200 1060 194 Catalytic cracing unit(CCU) 200 1960 289 Delay catalytic unit 200 3012 289 Hydrofining unit — 0 0 Oxidized asphalt plant — 0 0 Acrylonitrile unit — 0 0 Furfural treatment 200 2960 222 Ethylene unit 200 1160 300 Propylene unit 200 960 661 Pressure-relief devices 200 1160 451 Sulfur recovery unit — 0 0 NH3-N Catalytic cracing unit 25 1010 32.6 Furfural treatment 25 2167 13.01 Sulfur recovery unit (containing sewage stripping) 25 1651 44.7 Table 2 Pollution control equipment parameters for refining and chemical industrial Pollutants Pollution control measures Removal rate (%) Unit processing costs (104 t/104 yuan) Buiding area (m³) COD SBR 90 4000 500 MycelxTM 85 1000 400 NH3-N SBR 75 2000 500 MycelxTM 85 4000 700 Table 3 Existing
In discussion, the following may be drawn based on the optimization model calculation results of this study: (1) For the maximum pollution emission reductions of oil refining chemical enterprise, the maximum emission reductions of COD for refining and chemical enterprises was 6481.50 tons, calculated by means of computer, remarkably improved 29.63% compared with the target reductions (5000 tons); Similarly, the maximum emission reductions of NH3-N was 549.51 tons, improved 9.90% compared with the target reductions (500 tons), which has excellently finished the pollution emission reduction task during "the 12th Five-Year Plan".

The soft-reduction segments with remote roll gap adjustment functions (the key devices for implementation of soft-reduction action).
The servo hydraulic control system with high control accuracy for soft-reduction (the hydraulic drive system for implementing action of soft-reduction).
The soft-reduction basic automatic control system with high efficiency in the implementation (The electrical control system for implementing action of soft-reduction).
The L2 model database has been debugged, we have confirmed the database structure and the variable set, we have also defined the relationship of data communications between L1 basic automation program and L2 process control model and improved the database functions to test and verify the normalcy and stability of L2 model database.
As Fig.1 shows, it is the macroscopic comparison of the JB45 and JB50 steel grade produced without soft-reduction and with soft-reduction, and the details lie in Table1.

Weight-reduction Optimization Design For Milling Planer Bed Based on Finite Element Analysis Dong Sun1, a, Xudong Yang1, b 1 School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou, China apilliar3660@163.com, byxd_gzu@163.com Keywords: Milling planer bed, Weight-reduction, FEA, Static stiffness, Dynamic characteristics Abstract.
Three-dimensional modeling method is used to calculate the exact weight of the bed and then finite element analysis is used to research the static and dynamic characteristics before and after weight-reduction.
Table 1 The preceding 10 orders natural frequencies Order 1 2 3 4 5 6 7 8 9 10 Frequency 300.1 304.0 311.9 322.6 327.5 328.7 331.9 334.4 339.2 339.5 a) b) c) Fig.4 The vibration modes a-The first vibration mode, b-The second vibration mode, c-The ninth vibration mode Weight-reduction Optimization Design Considering the variety of economic factors of the company, to minimize the rectification of the existing wood patterns, installation foundations and the corresponding drawings, the size of the outer contour remains the same in the weight-reduction optimization design and only the wall thickness is thinned.
The comparison data is shown in Table 2.
Table 2 The comparison data of milling planer and planer milling machine (Length: mm) Product Name Driving Motor for Worktable (KW) Driving Motor for Milling Head (KW) Center Distance of two guides Load on Guide Surfaces (Kg/m) Thickness of Guide Surfaces Wall Thickness Thickness and Type of Reinforcing Plates Height of Bed Reference Machine 132 15 1300 3200 40 25 20, well-shaped 770 Milling Planer 15 7.5 900 2500 50 30 25, herringbone 700 The above data shows that the workload of the milling planer is much smaller than the reference object, structure and size that meet the reference machine can meet the milling planer theoretically.

The thickness reduction was measured by optical 3D scanning method and the influence of the feed, workpiece geometry and planar anisotropy of the blank on the wall thickness reduction was studied.
Based on the results it is determined that the highest reduction of wall thickness is observed in the conical part of the experimental sample.
For the experimental measurement of shape accuracy a non-contact data capture method was used.
Control factors and levels Parameter Sign Level 1 Level 2 Level 3 Feed ratio (mm/rev) Workpiece geometry (-) f pm 1 (1) radius R10 1,5 (2) conical area 2 (3) cylindrical area Rolling direction of the sheet (deg) rd 0 45 90 Analysis of data Using Minitab 16 software, ANOVA (Analysis of Variance) was performed to determine which parameter and two-way interactions significantly affect the performance characteristics.
Analysis of variance (S/N data) Source Sum of squares DoF Mean square p-value F- ratio f 0.066052 2 0.033026 0.000 236.21* pm 0.525874 2 0.262937 0.000 1880.61* rd 0.001030 2 0.000515 0.074 3.68 f*rd 0.001170 4 0.000293 0.174 2.09 f*pm 0.042593 4 0.010648 0.000 76.16* pm*rd 0.001015 4 0.000254 0.219 1.81 Other/Error 0.001119 8 0.000140 Total 0.638852 26 Tabulated F-ratio at 95% confidence level: F (0.05; 2.8) = 4.46; F (0.05; 4.8) = 3.84 * significant parameter or significant two-way interaction Fig. 3 Relation between response (reduction ratio t0/t1) and variables (feed ratio, place of measure) Conclusions Based on the experimental investigation and data analysis using ANOVA has demonstrated that the workpiece shape is very significant factor which intensively influence the wall thickness reduction of spun part.

NO reduction efficiency depends strongly on the redox process of iron/iron oxides.
Fig 2 NO reduction efficiency by iron Fig 3 Durable reaction of NO reduction by CO/CH4 in simulated flue gas at 1050 ˚C A durable test of NO reduction by iron in N2 atmosphere was conducted at 800˚C since the NO reduction efficiency was almost 100% at 800˚C (refer to Fig 2).
This showed that CO was not good reductant for NO reduction by iron in durable application.
NO reduction quickly increased to over 98% as CH4 was fed.
NO is chemically reduced to N2 by iron according to the following reaction: Fe + NO®FexOy + N2 According to the thermodynamic calculations using data from the JANAF tables (1985), it is possible for metallic iron to be completely oxidized to Fe2O3 when NO volume fraction is less than 0.05% at 700-900 ˚C.

According to the engineering geological and geophysical data, field outcrop on UDL Archeozoic Fuping group put a shop group (Ar3f ) biotite plagioclase gneiss, from top to bottom has weathered, strong weathering, weathering in three kinds of soil properties.
The toe of slope changes first; with the strength reduction, slope displacement develops gradually, eventually leading to the failure.
Extracte every reduction coefficient F, using software z-soil to calculate, we can achieve the distribution of plastic zone of the slope to observe the plastic zone’ development.
With the strength reduction factor F increasing gradually, plastic zone located at the toe of the increases gradually, and slowly extent to the top.
When the reduction coefficient F=1.0, plastic zone is produced at the slope toe.

According to the statistical data, the disposal cost of excess sludge was very high and held the 25~65％ of the total operating cost in the STPs in the developed countries while the disposal facilities of sludge held the 60~70％ of the total construction cost [1].
Fig. 1 Schematic diagram of process for the reduction of activated sludge by ozone.
Hajsardar et al. [24] studied the reduction efficiency of sludge in a SBR process by the ozonation.
Table 1 The reduction efficiencies of excess sludge by the ozonation under the different conditions.
Choi, Ozonation of wastewater sludge for reduction and recycling, Water Sci.

The required input data are rolling speed, roll radius, furnace temperature, initial and final height of the specimen, and specimen width.
Experimental Data used in validating the new hot rolling simulation was obtained through preliminary metallographic, hot torsion tests, and hot rolling experiments performed on the as-received wrought AISI316 (with Nb, V and Ti inclusions) in the temperature range (600-1200) OC and strain rate range of (3.6X10-3 - 1.4) s-1.
Program Validation The hot rolling experiment performed on AISI316 provided a data base for assessment of the validity of the simulated model.
Fig.2: Effect of Reduction on Yield Stress distribution at Low and High Strain Rates 50 100 150 200 250 300 350 0 0.94 1.94 2.94 3.94 4.94 5.94 6.94 7.96 8.96 9.96 10.96 11.96 12.96 13.96 14.96 15.96 Specimen Height, mm Yield Stress for Load Calculation H37, Strain Rate = 0.08, Reduction = 6.27% H39, Strain Rate = 0.08, Reduction = 14.54% H41, Strain Rate = 0.09, Reduction = 19.35% H43, Strain Rate = 0.09, Reduction = 22.77% H38, Strain Rate = 1.00, Reduction = 6.67% H40, Strain Rate = 1.17, Reduction = 15.21% H42, Strain Rate = 1.28, Reduction = 20.15% H44, Strain Rate = 1.37, Reduction = 24.43% Fig. 3, revealed a uniform pattern of rolling load distribution with specimen through-thickness from the rolling surfaces.
Also, the ratio of experimental to estimated roll load and torque were higher at lower reduction than at higher reduction.