化工学报
化工學報
화공학보
JOURNAL OF CHEMICAL INDUSY AND ENGINEERING (CHINA)
2015年
1期
206-214
,共9页
高炉%一氧化碳%建模%支持向量机%预测%自适应粒子群
高爐%一氧化碳%建模%支持嚮量機%預測%自適應粒子群
고로%일양화탄%건모%지지향량궤%예측%자괄응입자군
blast furnace%carbon monoxide%model%support vector regression%prediction%adaptive particle swarm optimization
高炉冶炼是一个具有非线性、大时滞、大噪声、分布参数等特征的高度复杂生产过程。针对目前高炉现场以焦比为能耗评价指标却无法提供实时指导的问题，研究以一氧化碳利用率为能耗评价指标，提出一种基于改进支持向量机的高炉一氧化碳利用率预测方法。首先分析高炉炼铁过程机理，结合互信息法得出影响一氧化碳利用率的相关操作因素。然后鉴于生产数据含噪高的特点，采用小波去噪方法去除数据噪声干扰，并且利用灰色相对关联度分析方法对操作参数进行时序配准，消除时滞影响，建立高炉一氧化碳利用率预测模型。在建模过程中，将自适应粒子群与支持向量机回归方法相结合，以克服模型参数选择的随机性，提高了模型预测精度。现场实际数据的预测结果表明所提出方法的有效性，能够实时精确地预测高炉一氧化碳利用率，为后续高炉的优化操作和节能减排提供了及时有效的决策支持。
高爐冶煉是一箇具有非線性、大時滯、大譟聲、分佈參數等特徵的高度複雜生產過程。針對目前高爐現場以焦比為能耗評價指標卻無法提供實時指導的問題，研究以一氧化碳利用率為能耗評價指標，提齣一種基于改進支持嚮量機的高爐一氧化碳利用率預測方法。首先分析高爐煉鐵過程機理，結閤互信息法得齣影響一氧化碳利用率的相關操作因素。然後鑒于生產數據含譟高的特點，採用小波去譟方法去除數據譟聲榦擾，併且利用灰色相對關聯度分析方法對操作參數進行時序配準，消除時滯影響，建立高爐一氧化碳利用率預測模型。在建模過程中，將自適應粒子群與支持嚮量機迴歸方法相結閤，以剋服模型參數選擇的隨機性，提高瞭模型預測精度。現場實際數據的預測結果錶明所提齣方法的有效性，能夠實時精確地預測高爐一氧化碳利用率，為後續高爐的優化操作和節能減排提供瞭及時有效的決策支持。
고로야련시일개구유비선성、대시체、대조성、분포삼수등특정적고도복잡생산과정。침대목전고로현장이초비위능모평개지표각무법제공실시지도적문제，연구이일양화탄이용솔위능모평개지표，제출일충기우개진지지향량궤적고로일양화탄이용솔예측방법。수선분석고로련철과정궤리，결합호신식법득출영향일양화탄이용솔적상관조작인소。연후감우생산수거함조고적특점，채용소파거조방법거제수거조성간우，병차이용회색상대관련도분석방법대조작삼수진행시서배준，소제시체영향，건립고로일양화탄이용솔예측모형。재건모과정중，장자괄응입자군여지지향량궤회귀방법상결합，이극복모형삼수선택적수궤성，제고료모형예측정도。현장실제수거적예측결과표명소제출방법적유효성，능구실시정학지예측고로일양화탄이용솔，위후속고로적우화조작화절능감배제공료급시유효적결책지지。
Blast furnace is a highly complex production process with the characteristics of nonlinearity, large delay, big noise, parameters distribution, and so on. According to the problem that coke ratio is used as energy consumption evaluation index but can’t provide real-time guidance in the blast furnace field, carbon monoxide utilization ratio is studied as the energy consumption index and a prediction method for it based on improved support vector regression(SVR) is proposed. Firstly, relevant operation factors are selected by analyzing the mechanism of blast furnace combined with mutual information method. Next, wavelet transform is used to remove the noise including in the production data. Additionally, relative gray correlation analysis is applied for temporal registration to avoid the time delay of blast furnace operation, and then the prediction model of carbon monoxide utilization ratio is established. Support vector regression method and adaptive particle swarm optimization algorithm(APSO) are united to overcome the randomness of the selection of parameters and improve the accuracy of the prediction model. The simulation results demonstrate that APSO-SVR provides an effective way to predict the carbon monoxide utilization, which serves as a scientific decision support for the following optimization of blast furnace operation as well as energy saving and emission reduction.
