改进型决策树在加热炉热效率评估中的应用研究
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摘要
在石化企业加热炉设备的长期运转和热效率评估过程中,针对属性数量众多且多为连续型属性这一特点,利用传统规则库的调炉决策很难对加热炉进行有效的能效评估。因此,提出了一种基于C4.5算法的改进型分类算法,弥补C4.5算法没有考虑问题无关属性和冗余属性对分类结果的影响。在经典的C4.5算法基础上引入模拟退火算法对分类属性进行优化组合,使得分类算法在分类时间和准确率上都有改进和提升。同时,在已有的炉况在线监测系统上加以改进,将决策树算法运用到该系统中,用于石化加热炉热效率的评估,为管理人员提供炉况调整解决方案。
In the process of long-term operation and thermal efficiency evaluation of heating furnaces in petrochemical enterprises, the traditional rule base has difficulty in evaluating the energy efficiency of heating furnaces effectively. Therefore, an improved classification algorithm based on C4.5 algorithm was proposed to compensate the C4.5 algorithm which does not consider the impact of problem-independent attributes and redundant attributes on the classification results. Based on the classical C4.5 algorithm, the simulated annealing algorithm was introduced to optimize the combination of classification attributes so that the classification algorithm can be improved in both classification time and accuracy. At the same time, the existing on-line monitoring system for furnace condition was improved and the decision tree algorithm was applied to evaluating the heating efficiency of the petrochemical heating furnace and to provide a solution for managers to adjust furnace conditions.
In the process of long-term operation and thermal efficiency evaluation of heating furnaces in petrochemical enterprises, the traditional rule base has difficulty in evaluating the energy efficiency of heating furnaces effectively. Therefore, an improved classification algorithm based on C4.5 algorithm was proposed to compensate the C4.5 algorithm which does not consider the impact of problem-independent attributes and redundant attributes on the classification results. Based on the classical C4.5 algorithm, the simulated annealing algorithm was introduced to optimize the combination of classification attributes so that the classification algorithm can be improved in both classification time and accuracy. At the same time, the existing on-line monitoring system for furnace condition was improved and the decision tree algorithm was applied to evaluating the heating efficiency of the petrochemical heating furnace and to provide a solution for managers to adjust furnace conditions.
引文
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[3] 梁光川,黄兴,王勇,等.燃气加热炉热效率计算方法的改进及应用[J].油气储运,2016,35(5):560～563.
[4] 王志国,马一太,卢苇.不确定度分析原理在锅炉热效率测算中的应用[J].中国电机工程学报,2005,25(3):125～129.
[5] Chen M C,Wu P J,Tsau C K.Analyzing Cargo Loss Severity of Electronics Products with Decision Tree[C].Industrial Engineering,Management Science and Applications 2015.Berlin:Springer,2015:477～484.
[6] Amin R K,Indwiarti,Sibaroni Y.Implementation of Decision Tree Using C4.5 Algorithm in Decision Making of Loan Application by Debtor (Case Study:Bank Pasar of Yogyakarta Special Region)[C].International Conference on Information and Communication Technology.Piscataway,NJ:IEEE,2015:75～80.
[7] Guo Q,Jiang D.Method for Walking Gait Identification in a Lower Extremity Exoskeleton Based on C4.5 Decision Tree Algorithm[J].International Journal of Advanced Robotic Systems,2015,12(4):1～11.
[8] 辛少杰.燃煤工业锅炉热负荷预测的研究[D].上海:上海交通大学,2011.
[9] 卢伟业,李越胜,冯国行,等.一种用于燃天然气蒸汽锅炉热效率的快速预测方法[J].节能技术,2017,35(1):21～25.
[10] 刘波.加热炉燃烧系统先进控制策略的设计与实现[D].合肥:中国科学技术大学,2017.
[11] 王文霞.数据挖掘中改进的C4.5决策树分类算法[J].吉林大学学报(理学版),2017,55(5):1274～1277.
[12] 徐胜,马小军,钱海,等.基于遗传-模拟退火的蚁群算法求解TSP问题[J].计算机测量与控制,2016,24(3):143～144.
[13] Li R P.Research of Decision Tree Classification Algorithm in Data Mining[J].Journal of East China Institute of Technology (Social Science),2010,(2):77～81.