摘要
为快速预测含油污泥热解后残渣含油率的变化规律,指导含油污泥热解工艺参数优化,选取热解终温,热解时间,升温速率,氮气流量,含油污泥的含油率、含水率和含渣率作为输入变量,热解残渣含油率作为输出变量,采用梯度提升决策树(GBDT)、极端梯度提升(XGB)、支持向量机(SVM)及随机森林(RF)算法分别建立了含油污泥热解残渣含油率的预测模型。通过228组数据进行训练和测试,结果表明,GBDT、XGB、SVM以及RF 4种含油率预测模型在测试集上的决定系数R^(2)分别为0.8716、0.8667、0.8356和0.9171。经过贝叶斯优化算法(BOA)超参优化后,该4种含油率预测模型的测试集决定系数R^(2)分别提升至0.9012、0.9001、0.8965和0.9204。其中,贝叶斯优化的随机森林(BO-RF)模型预测效果更佳,能更准确地预测含油污泥热解残渣含油率的动态变化规律。
To rapidly predict changes in residual oil content after the pyrolysis of oily sludge and to guide the optimization of pyrolysis process parameters,this study collected a dataset comprising 228 samples and employed machine learning methods to predict the oil content in the oily sludge pyrolysis residues.Several factors were used as input variables,including final pyrolysis temperature,pyrolysis time,heating rate,nitrogen flow rate,initial oil content,water content,and residue content of the oily sludge.The oil content in the pyrolysis residues was used as the output variable.Methodologically,this study applied four advanced machine learning algorithms in an innovative manner:Gradient Boosting Decision Trees(GBDT),eXtreme Gradient Boosting(XGB),Support Vector Machines(SVM),and Random Forests(RF),to construct high-precision prediction models for the oil content in pyrolysis residues.These models were rigorously trained and cross-validated on a dataset comprising 228 samples to ensure their generalization ability and prediction accuracy.The results showed that the coefficients of determination(R^(2))for the GBDT,XGB,SVM,and RF models on the test set reached 0.8716,0.8667,0.8356,and 0.9171,respectively,providing initial validation of the effectiveness of these machine learning models in predicting oil content in oily sludge pyrolysis residues.To further improve the predictive performance of the models,this study introduced the Bayesian Optimization Algorithm(BOA)to fine-tune the hyperparameters of the models.After BOA optimization,the R^(2) values of the four models significantly increased to 0.9012,0.9001,0.8965,and 0.9204,respectively.Among them,the Bayesian-Optimized Random Forest(BO-RF)model exhibited the best predictive performance,demonstrating high consistency on the test set and extremely high accuracy in predicting the dynamic trends of oil content in oily sludge pyrolysis residues.Furthermore,through feature importance analysis,it was found that the final pyrolysis temperature,initial oil content in the sludge,and pyrolysis duration were the most critical factors influencing the oil content in the residues.In summary,by introducing advanced machine learning algorithms combined with a Bayesian optimization strategy,this study successfully constructed high-precision prediction models for the oil content in oily sludge pyrolysis residues.The BO-RF model,in particular,offers an effective and accurate approach for predicting oil content.This achievement contributes to enhancing the pyrolysis process of oily sludge,boosting resource utilization efficiency,and advancing sustainable waste treatment methods.It provides strong support for the pyrolysis treatment of oily sludge at both theoretical and practical levels,opening up new perspectives and approaches for environmental management and resource recovery research.
作者
彭黄湖
姜勇
杨帆
陈泽洲
吴圣姬
车磊
PENG Huanghu;JIANG Yong;YANG Fan;CHEN Zezhou;WU Shengji;CHE Lei(School of Engineering,Huzhou University,Huzhou 313000,China;Zhejiang Eco Environmental Technology Co.,Ltd.,Huzhou 313000,China)
出处
《能源环境保护》
2025年第6期188-198,共11页
Energy Environmental Protection
基金
国家自然科学基金资助项目(52100143)。
关键词
含油污泥
热解
含油率预测
特征重要性分析
机器学习
贝叶斯优化算法
Oily sludge
Pyrolysis
Oil content prediction
Feature importance analysis
Machine learning
Bayesian optimization algorithm
