煤炭转化2024，Vol.47Issue(4)：11-22,12.DOI:10.19726/j.cnki.ebcc.202404002
机器学习模型预测煤热解产物分布研究
Prediction of coal pyrolysis product distribution using machine learning model
摘要
Abstract
Pyrolysis technology is one of the primary methods for clean and efficient con-version and utilization of coal.However,due to the complex composition of coal and the challen-ges in controlling pyrolysis reactions,extensive time and effort are required to repeatedly investi-gate the pyrolysis product distribution for different types of coal.Taking coal composition and fi-nal pyrolysis temperature as input conditions and yield of coal pyrolysis products as output condi-tion,three machine learning models with adjusted parameters,that is,random forest(RF),support vector machine(SVM)and XGBoost model,were utilized to predict the yield of coal py-rolysis products.The importance of various input features on the yield of pyrolysis products was analyzed,and the effects of these input features were also quantified via two-factor partial de-pendence(PDP).The results show that the XGBoost model have the best performance among these three models for predicting the yield of coal pyrolysis products,and its decision coefficient and root mean square error for tar yield reach the highest 0.95 and the lowest 0.86,respectively.The important input features for coke yield are fixed carbon,volatiles,oxygen and pyrolysis final temperature,and their importance accounts for 84%.The tar yield are mainly affected by vola-tiles,hydrogen,nitrogen and pyrolysis final temperature,accounting for 83%of total impor-tance.The PDP analysis indicates that an increase in mass fraction of hydrogen and volatile in coal,the yield of coal pyrolysis tar will increase.According to the XGBoost model,the yield of pyrolysis tar exceeds 9%when the mass fraction of hydrogen is between 5.0%and 6.0%in coal,coupled with mass fraction of volatile between 30%and 40%.When mass fraction of the fixed carbon is greater than 50%,a decrease in mass fraction of hydrogen and volatile in coal,as well as a decrease in the final pyrolysis temperature,would result in a decline in the yield of py-rolysis gas.关键词
煤热解/产物收率/机器学习/重要特征性分析/部分依赖性分析Key words
coal pyrolysis/product yield/machine learning/important feature analysis/partial dependence analysis分类
化学化工引用本文复制引用
王敏欣,师印光,刘长波,吴雷,周军,蒋绪..机器学习模型预测煤热解产物分布研究[J].煤炭转化,2024,47(4):11-22,12.基金项目
陕西省自然科学基础研究计划一般项目(2024JC-YBQN-0090)和陕西省教育厅服务地方专项计划项目(22JC045). （2024JC-YBQN-0090）
