基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )的高爐噴煤優(yōu)化

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基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )的高爐噴煤優(yōu)化
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作者單位:(1.內蒙古科技大學(xué) 信息工程學(xué)院,內蒙古 包頭 014010;2.內蒙古包頭鋼聯(lián)有限責任公司,內蒙古 包頭 014010)
作者簡(jiǎn)介:崔桂梅(1963),女,河北保定人,博士,碩士生導師,主要從事控制理論等方向的研究。[FQ)]
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基金項目:國家自然科學(xué)基金項目(61164018)。

Optimization of Pulverized Coal Injection in Blast Furnace Based on BP Neural Network Optimized By Genetic Algorithm

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(1. College of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China ;2. Inner Mongolia Baotou Steel Union Limited Liability Company, Baotou 014010, China)

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高爐煉鐵是一個(gè)復雜的多變量系統,而現行的操作制度是基于爐長(cháng)經(jīng)驗的參數設置模式,導致能源尤其是煤粉的消耗常常處于“盲目”狀態(tài)；文章綜合煉鐵工藝理論和高爐專(zhuān)家經(jīng)驗,針對白云鄂博礦石冶煉的特殊性,采用篩選出的優(yōu)化數據,利用遺傳算法所固有的全局搜索性能優(yōu)化BP神經(jīng)網(wǎng)絡(luò )模型的權值和閾值,分別建立了基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )的高爐噴煤量?jì)?yōu)化預測模型以及工藝指標(鐵水含量及入爐焦比)預測模型；優(yōu)化數據的利用使得上述模型可以根據高爐當前爐況輸出噴煤量的最佳優(yōu)化設定值,并預測出相對應的工藝指標變化趨勢；實(shí)際應用表明,本方法能夠給現場(chǎng)操作人員提供操作指導,實(shí)現高爐穩定順行、提高經(jīng)濟效益的目的。

Abstract:

Blast Furnace iron making process is a complex multi-variable system, due to the operational parameters are set by BF masters with their experience, thus making the consumption of resources are often in a "blind" state. According to the analysis of mechanism of blast furnace and experiences of the blast furnace experts, considering the speciality of ore smelting in Bayan Obo, the coal injection quantity prediction model and process indicators ( content in molten iron and coke ratio) prediction model based on the BP neural network which weights and thresholds has been optimized through the genetic algorithm were established by using optimizing data. So the optimization setting value of coal injection quantity and the changing trend of process indicators can be given according these models. The results of practical application showed that, this method can provide decision-making guidance for operators and achieve the goals of stable operation and increase benefit in the process of BF iron making

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崔桂梅,高翠玲,侯佳,陳智輝,馬祥.基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )的高爐噴煤優(yōu)化計算機測量與控制[J].,2015,23(5):1568-1570, 1574.

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在線(xiàn)發(fā)布日期: 2015-07-31
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