基于RF-GA的热磨机生产参数优化方法  

Optimization of Hot Mill Production Parameters Based on RF-GA

作　　者：陈光伟[1] 杨凯 许威[2] 黄鹤飞 王丙军 CHEN Guang-wei;YANG Kai;XU Wei;HUANG He-fei;WANG Bing-jun(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin Heilongjiang 150040,China;Harbin University of Commerce,Harbin Heilongjiang 150028,China;Jinyu Tiantan(Tangshan)Wood Industry Technology Co.,Ltd,Tangshan Hebei 063205,China)

机构地区：[1]东北林业大学机电工程学院,黑龙江哈尔滨150040 [2]哈尔滨商业大学,黑龙江哈尔滨150028 [3]金隅天坛(唐山)木业科技有限公司,河北唐山063205

出　　处：《林业机械与木工设备》2024年第6期42-48,共7页Forestry Machinery & Woodworking Equipment

基　　金：中国博士后科学基金面上项目(2013M541330)。

摘　　要：热磨机是纤维板生产中最重要的设备之一;热磨机的综合能耗在纤维板生产的整体能源消耗中占比显著,优化生产参数以降低热磨机能耗一直是纤维板生产的要求之一。以华北某纤维板企业采集的数据为依据,针对影响比能耗(SEC)和纤维质量(QF)的多个生产参数,用粒子群算法优化的BP神经网络、支持向量机(SVM)和随机森林(RF)建立比能耗和纤维质量预测模型,经对比确定最佳比能耗和纤维质量预测模型;后基于最佳预测模型构建生产参数优化模型,采用遗传算法(GA)对其求解得到最佳生产参数,以此提出基于RF-GA模型的热磨机生产参数优化方法。结果表明,热磨机各生产参数之间存在较强耦合关系,且与比能耗和纤维质量为非线性关联;粒子群算法对机器学习算法中超参数的优化是必要的,可以有效降低机器学习算法误差;选取5组不同纤维质量下的数据,采用建立的热磨机生产参数优化方法优化后,比能耗平均下降6.9kW·h/t、降幅为5.22%。该研究验证了RF-GA热磨机生产参数优化方法的可行性,能够应用于生产参数的优化,有效地实现节能降耗的目标,并为热磨机生产参数的设定提供依据。The hot mill is one of the most important pieces of equipment in fiberboard production;The comprehensive energy consumption of hot mills accounts for a significant portion of the overall energy consumption of fiberboard production,and optimizing the production parameters to reduce the energy consumption of the hot mill has consistently been a requirement in fiberboard production.Based on the data collected from a fiberboard enterprise in North China,for several production parameters that affect specific energy consumption(SEC) and fiber quality(QF),the SEC and QF prediction models were established with BP neural network,support vector machine(SVM) and random forest(RF) optimized by particle swarm algorithm,and the best SEC and QF prediction models were determined by comparison;then the production parameter optimization model was constructed based on the best prediction model,and the best production parameters were obtained by solving it with genetic algorithm(GA),so as to propose the optimization method of production parameters of the hot mill based on RF-GA model.The results show that thereis a strong coupling relationship between each production parameter of the hot mill,and it is nonlinear with SEC andQF;the PSO algorithm is necessary for the optimization of hyperparameters in the machine learning algorithm,whichcan effectively reduce the error of the machine learning algorithm;five sets of data with different fiber quality wereselected and optimized using the established hot mill production parameter optimization method,and the optimizedspecific energy consumption was reduced by 6.9kW·h/t and 5.22% on average.This study verifies the feasibility ofthe RF-GA production parameter optimization method for hot mills,which can be applied to optimize production pa-rameters,effectively achieve the goal of energy saving and consumption reduction,and provide a basis for the settingof production parameters of hot mills.

关 键 词：热磨机 节能 优化 生产参数 粒子群算法 RF-GA 

分 类 号：S777[农业科学—森林工程]