基于机器学习的生物质热解三态产物产率提升研究  

The Yield Improvement of Three-state Products from Biomass Pyrolysis Based on Machine Learning

作　　者：易植 季玲 李明月 陈虎生 肖运昌 YI Zhi;JI Ling;LI Mingyue;CHEN Husheng;XIAO Yunchang(College of Intelligent Manufacturing,Meiga Polytechnic Institute Hubei,Xiaogan 432000,China;School of Computer and Electrical Engineering,Hunan University of Arts and Sciences,Changde 415000,China;College of Biomedical Engineering,Xinjiang Second Medical College,Karamay 834000,China)

机构地区：[1]湖北孝感美珈职业学院智能制造学院,湖北孝感432000 [2]湖南文理学院计算机与电气工程学院,湖南常德415000 [3]新疆第二医学院生物医学工程学院,新疆克拉玛依834000

出　　处：《林产化学与工业》2023年第6期113-122,共10页Chemistry and Industry of Forest Products

基　　金：湖南省自然科学基金资助项目(2021JJ50023)。

摘　　要：通过挖掘文献中木质纤维素类生物质鼓泡流化床快速热解实验数据并建立随机森林(RF)回归模型,以生物质原料特性与热解条件对生物质热解生物油、生物炭、气体的产率进行预测。从影响生物质热解产物分布的5类关键因素中整理出15个特征变量,将输入变量进行了组合得到7个模型,均能很好地预测生物质热解三态产物,回归系数(R 2)大于0.9。模型6的输入变量最少且准确度最高,对生物炭、生物油、生物质热解气产率预测的R 2分别为0.9428、0.9561、0.9391,均方根误差(RMSE)分别为2.6791、2.9395和3.1083。通过模型贡献度分析可知,热解条件(Ⅴ)为影响热解产物产率的最重要因素,其对生物炭、生物油、生物质热解气产率预测的贡献度分别为0.3327、0.2204和0.2147。采用部分依赖图(PDP)结合各个特征变量的分布箱线图分析,结果表明:热解温度(HT)、木质素质量分数(Lig)、颗粒粒径(PS)为影响生物炭产率的主要因素;生物油与生物质热解气产率则由HT、纤维素质量分数(Cel)与半纤维素质量分数(Hem)、进料速度(FR)、气体流量(GFR)共同决定,受Lig与PS的影响较小;选择纤维素与半纤维素含量高的生物质原料,以及适当增大气体流量都可以提高生物油产率。此外,还分别建立了极端梯度提升(XGBoost)、支持向量机(SVR)和神经网络(ANN)的回归模型与RF回归模型进行比较,结果表明:RF模型对于三态产物产率预测的准确度最高、泛化能力好。研究结果促进了对生物质热解过程的全面了解,为生物质热解三态产物产率调控提供了理论指导。By mining the experimental data in the literatures of fast pyrolysis of lignocellulosic biomass in a bubbling fluidized bed and establishing a random forest(RF)regression model,the yield of bio-oil,biochar,and gas via biomass pyrolysis was predicted based on biomass feedstock characteristics and pyrolysis conditions.Fifteen feature variables were sorted out from five key factors influencing the distribution of biomass pyrolysis products,and seven models were obtained by combining the input variables.All models showed good prediction performance for the three-state products from biomass pyrolysis,with a regression coefficient(R 2)greater than 0.9.Model 6 had the fewest input variables and the highest accuracy,with R 2 values of 0.9428,0.9561,and 0.9391 for the yield predictions of biochar,bio-oil,and biomass pyrolysis gas,and the root mean square errors(RMSE)were 2.6791,2.9395,and 3.1083,respectively.Contribution analysis of the models revealed that pyrolysis conditions(Ⅴ)were the most important factors affecting the pyrolysis products yield,with contributions degree of 0.3327,0.2204,and 0.2147 for biochar,bio-oil,and gas yield predictions,respectively.Partial dependence plots(PDP)combined with the distribution boxplots analysis of each feature variable showed that pyrolysis temperature(HT),lignin mass fraction(Lig),and particle size(PS)were the main factors affecting biochar yield.Bio-oil and biomass pyrolysis gas yields were determined by HT,cellulose mass fraction(Cel),hemicellulose mass fraction(Hem),feed rate(FR),and gas flow rate(GFR),which were less affected by Lig and PS.The bio-oil yield could be improved by selecting biomass feedstock with high-quality fractions of cellulose and hemicellulose and increasing gas flow rate appropriately.In addition,the regression models of extreme gradient boosting(XGBoost),support vector machine(SVM),and artificial neural network(ANN)were established,which were compared with the RF regression model.The RF model showed the highest accuracy and good generalization ability

关 键 词：快速热解 鼓泡流化床 产物产率 随机森林回归模型 

分 类 号：TQ35[化学工程] TK6[动力工程及工程热物理—生物能]