The Optimization Of Electric Arc Furnace Input Energy Based On The Multi-scale Forecast

In recent years the rapid development of modern metallurgy, it has becamed energy consumption in a large industrial enterprises, Production of high energy consumption due to its characteristics, more and more attention and pressing needs of energy management and optimization. Sinosteel Jilin Ferroalloy Corporation is the metallurgical enterprises of the key enterprises, it is the largest and most varied of ferroalloy production supply base. In the ferroalloy production process, the main energy equipment-electric arc furnace.EAF is a complex system, its smelting process has time-varying, nonlinear and strong coupling features, three-phase voltage, phase current set-point difference would have an impact on the arc length. Therefore, a reasonable choice of electric arc furnace power supply strategy can effectively reduce energy consumption, electrode consumption, and refractory erosion, reduced refining cycle, thereby reducing the refining costs, increase production economic indicators.For arc furnace smelting iron alloy is an extremely complex physical and chemical reaction process, the information accurately and effectively predict energy consumption related to the safe and economic operation of electric arc furnace, This paper production process of electric arc furnace based on the analysis, a least squares support vector machine based (LS-SVM) multi-scale energy consumption prediction model is presented. First of all sequences of wavelet decomposition of energy consumption, by the specified scale approximation coefficients and wavelet coefficients of the relevant scale, then use the LS-SVM to predict points of multi-scale combination forecasting coefficients, and finally obtained by wavelet reconstruction of the corresponding arc Furnace energy consumption forecasts. Depending on the furnace conditions and optimization of energy input indicators, the melt is given the power of the four stages of policy, thus achieving the optimization of electric arc furnace input energy. Jilin Ferroalloy electric arc furnace with power consumption forecast the company conducted a simulation test data, the results show that the proposed method has higher accuracy and real-time.Manganese silicon alloy is produced using a slag smelting, materials ratio on the smelting process have a great impact, This article from the initial ratio of raw materials:the manganese ore, manganese-rich slag, coke, lime, silica, dolomite (or limestone), the ratio of fluorite, etc., to the smelting process is a balanced three-phase current, click the Insert depth Is appropriate, even if the flame furnace mouth, drop charge is balanced, the product meets requirements and slag composition and stable, The economic and technical indicators such as furnace condition is good observation, to determine, on the ratio of raw materials and control strategies to adjust, with particular attention to carbon content, slag basicity, the furnace temperature control, and to accurately determine the operating furnace conditions And timely manner to ensure the smooth progress of the smelting process.As used in the EAF process optimization and material input energy optimization techniques improve the overall smelting degree of automation, improved energy information management, saving energy, has achieved remarkable economic and social benefits to the actual production Has some significance.