Dissertation > Economic > Economic planning and management > Management of National Economy > Energy Management

Energy forecasting and energy optimization techniques in the metallurgical enterprises

Author ZhaoYing
Tutor ZhangDeJiang
School Changchun University of
Course Measuring Technology and Instruments
Keywords Adaptive Genetic Algorithms Support Vector Optimization Submerged arc furnace
CLC F206
Type Master's thesis
Year 2010
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Metallurgical industry in recent years, rapid development, due to the production of high energy consumption characteristics of the industry, it has become a modern industrial enterprise in the energy-hungry. Sinosteel Jilin Ferroalloy Company the domestic metallurgical industries of the key enterprises, is the country's largest and most varied ferroalloy production and supply base. Ferroalloy production process, the main energy-consuming equipment submerged arc furnace. Submerged arc furnace is a complex system, its smelting process variability, nonlinearity and strong coupling characteristics of three-phase voltage, three-phase arc current set point will impact the length of the arc. Therefore, a reasonable choice of submerged arc furnace supply strategy can effectively reduce consumption, electrode wear and refractory erosion, reduce the refining cycle, thereby reducing smelting costs and improve productivity. Conventional supply strategies are often set to determine the operating current, submerged arc furnace smelting process, smelting cycle and ferroalloy per ton power consumption and arc power is closely related. Therefore, the reasonable supply strategy formulation is not only the current choice. Access on the basis of the relevant literature, Sinosteel Jilin Ferroalloy smelting tungsten-iron alloy production process as the background for different working characteristics of the smelting process to capitalize on the favorable heating conditions: First, the submerged arc furnace production process analysis, support vector machine, the establishment of a submerged arc furnace furnace conditions determine the model gives the melting of four stages of the smelting process, and in accordance with the conditions of the furnace energy estimated; and then based on the energy balance equation, the melting of energy input to the optimization model optimization index for the type of furnace conditions, according to the energy input, the use of genetic algorithms to the current, voltage and reactance selected to give a reasonable supply strategy, thus achieving optimization of the submerged arc furnace input energy. Tungsten-iron alloy furnace production of Jilin Ferroalloy Co., Ltd. 404 # 40 furnace experimental data were compared, the results demonstrate the effectiveness of the proposed method. Submerged arc furnace smelting process input energy optimization techniques to enhance the overall degree of automation of the submerged arc furnace smelting process, improve the energy information management level and achieved remarkable economic and social benefits. More importantly, through this research project, exploration and accumulated some experience, provides a practical, industrial energy efficiency is worth learning method for modeling and optimization of complex industrial process control.

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