焦炉火道温度稳定性优化控制仿真

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英文篇名：Optimization Control of Temperature Stability of Coke Oven 作者：李爱莲 ; 孟冠杰 英文作者：LI Ai-lian;MENG Guan-jie;Information Engineering Institute,Inner Mongolia University of Science and Technology; 关键词：焦炉 ; 小波神经网络 ; 火道温度控制 ; 设定值优化 英文关键词：Coke Oven;;Wavelet neural network;;Fire temperature control;;Set value optimization 中文刊名：JSJZ 英文刊名：Computer Simulation 机构：内蒙古科技大学信息工程学院; 出版日期：2018-07-15 出版单位：计算机仿真 年：2018 期：v.35 基金：内蒙古自治区自然科学基金资助(2016MS0610);; 内蒙古科技大学产学研合作培育基金项目(PY-201512) 语种：中文; 页：JSJZ201807059 页数：5 CN：07 ISSN：11-3724/TP 分类号：277-280+321

摘要

焦炉立火道温度的稳定性直接关系到炼焦生产的产量和质量。火道温度的稳定性将直接导致焦炭质量的下降、生产成本的提高。而现在焦化厂焦炉火道温度不能实时在线检测且设定值完全依靠人工经验给定,无法根据现场炉况变化做出实时调整致使焦炭生产不稳定,能耗大,生产成本高。针对这一现状提出基于软测量与设定值优化的火道温度闭环控制方案。设计中采用基于案例推理对焦炉火道标准温度进行寻优。结合海量现场数据和专家系统建立优化模型得到能耗小、产高的目标火道标准温度值。采用最小二乘支持向量机模型实现火道温度软测量。用小波神经网络进行焦炉立火道温度的预测。并通过仿真研究验证了方案的有效性。仿真结果表明该方案可以找出最佳标准温度设定值使煤气消耗量减少,能耗降低。可以为实际生产提供良好的指导。
        The stability of the coke oven temperature directly relate to the production and quality of coking production, which will directly lead to the decline in the quality of coke and increase production cost. At present, coke oven temperature can not be measured online in real-time in the coking plant, and the coke oven temperature setting is complete dependent on human experience. It can not be adjusted in real-time according to the practical coke oven condition change. This makes coke production unsteadiness, high energy consumption and production costs. Aiming at this situation, we proposed a closed-loop temperature control scheme that bases on soft-sensing and optimization to set the values. This design uses case-based reasoning to optimize the standard temperature of coke oven. Combined with the massive measurement data and expert system optimization model, and obtains the coke oven temperatures standard value with low energy consumption and high output. The least square support vector machine model was used for the soft measurement of coke oven temperatures. Wavelet neural network was used to predict the standing fire temperature of coke oven. And simulation result shows that the program can find the best standard temperature setting to reduce gas and energy consumption. It provides good guidance for actual production.

引文

[1]刘君贤.焦炉生产过程焦炭质量与炼焦能耗智能预测模型[D].中南大学,2010.
    [2]李伟明,穆志纯.推理建模中基于KDD和粗糙集的案例修改[J].计算机仿真,2006,(10):141-143.
    [3]张君昌,李明,谷卫东.融合减法聚类与C-均值聚类的目标定位方法[J].计算机仿真,2012,(07):269-273
    [4]梁辉,童朝南.基于减法聚类的带钢厚度数据驱动建模[J].北京科技大学学报,2012,(11):1338-1345.
    [5]王伟,吴敏,雷琪,曹卫华.焦炉生产过程综合生产指标的改进神经网络预测方法[J].控制理论与应用,2009,(12):1419-1424.
    [6]黄太安,生佳根,徐红洋,黄泽峰.一种改进的简化粒子群算法[J].计算机仿真,2013,(2):327-330.
    [7]崔桂梅,孙彤,张勇.基于最小二乘支持向量机的高炉铁水温度预测[J].计算机仿真,2013,(10):354-357.
    [8]L J Cao,F H Tay.Support vector machine with adaptive paramaters in financial time series orecasting[J].IEEE Transactions on Neural Networks.2003,14(6):1506-18.
    [9]陈振华,郭拯危.小波神经网络预测模型的仿真实现[J].计算机仿真,2008,25(6):147-150.