基于随机森林算法的制浆造纸碳足迹预测及影响因素分析OA

To address the complexity of influencing factors in carbon emissions in pulp and paper industry and the limitation of traditional life cycle as-sessment(LCA)in rapidly supporting process optimization,this study constructed a carbon footprint prediction framework for paper products based on LCA and machine learning.With a"cradle-to-gate"system boundary,a dataset was developed based on the Ecoinvent v3.11 and FisherSolve data-bases.The feature space of the dataset covered variables such as raw materials,energy structure,process parameters,and waste treatment,while the target variable was the carbon footprint intensity calculated using the IPCC 2013 GWP 100a method.On this basis,a random forest(RF)model was used to identify key influencing factors,and XGBoost was introduced to relearn the prediction residuals of the RF model.The results showed that dry-ing-section steam consum ption,grid emission factor,fossil fuel ratio,unit electricity consumption,and lime kiln calcination emissions were the dom-inant factors.The RF-XGBoost stacked model significantly outperformed single model,with an R2 increasing from 0.482 to 0.928 and a root mean square error(RMSE)decreasing from 104.3 kg CO2eq/t to 38.9 kg CO2eq/t.