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Sintering Burdening Optimization methods and industrial applications

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Tutor: CaoWeiHua WangChunSheng
School: Central South University
Course: Control Science and Engineering
Keywords: Sintering Burdening,Satisfactory Optimization,Particle swarm optimization,Multi-
CLC: TF046.4
Type: Master's thesis
Year:  2011
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Sintered ore is the main raw material for blast furnace ironmaking , the sinter will directly affect the quality of the blast furnace conditions and steel production . The sintering ingredients is the first step of the iron and steel smelting production process and the batching effect a direct impact on the quality of the sinter . Blending and sintering process vulnerable to unstable sources of iron ore , the physical properties and chemical properties of the same and sintered ore quality testing lag caused by the complex process of sintering ingredients . Sintering Burdening program seeking reasonable , and how to take advantage of great significance . This paper focuses on Sintering Burdening Optimization models and ingredients optimization methods to carry out the research , the proposed optimization method based on the satisfaction Sintering Burdening Optimization Theory , Sintering Burdening Optimization blending and sintering of multi-objective satisfaction optimization model , design and development . The main research work mainly in the following aspects . Sintering Burdening process characteristics based on Sintering Burdening Optimization analysis of impact , the establishment of the iron grade , alkalinity and cost optimization objectives , chemical composition , alkalinity and inventory Sintering Burdening multi-objective optimization constraints model. Second, the shortcomings of traditional optimization method of sintering ingredients and Sintering Burdening Optimization Model there is a multi- target , multi- constraint conditions and nonlinear problems , a Sintering Burdening Optimization Based on satisfactory optimization theory . Satisfactory optimization ideas , establish blending and sintering of multi-objective satisfaction optimization model , and constructs the function of sintering satisfaction ingredients , blending and sintering multi-objective optimization problem into a for solving satisfaction largest single objective optimization problem . Finally , for sintering ingredients model characteristics and particle swarm algorithm for solving optimization problems shortcomings , improved particle swarm algorithm is given a method for solving the blending and sintering multi-objective satisfaction optimization problem . The experimental simulation verify the effectiveness of the proposed modeling methods and optimization methods . Meanwhile, in order to verify its practical application , sintering plant for a large - scale iron and steel enterprises , development Sintering Burdening Optimization and Decision Support Systems . The results of the practical application of the system to achieve the sintered batching process optimization , information management and decision support , can effectively improve the quality of the sinter improve computational efficiency and reduce production costs .
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