林业工程学报2025，Vol.10Issue(2)：60-66,7.DOI:10.13360/j.issn.2096-1359.202404008
杉木砂光粉尘爆炸最小点火能预测模型
The prediction model of minimum ignition energy for wood dust explosions
摘要
Abstract
The study employed the XGBoost algorithm to develop the prediction model for the minimum ignition energy(MIE)of sanding dust of Chinese fir wood.XGBoost is a powerful machine learning technique that combines multiple weak learners to create a strong learner,effectively capturing complex relationships among variables.The model considered four key influencing factors,i.e.,dust mass concentration,moisture content,blowing pressure,and specific surface area diameter.These factors were used as input variables,while the MIE served as the target variable.The dataset was split into training and test sets to evaluate the model's performance and generalization ability.The results showed that the XGBoost model developed in this study demonstrated remarkable performance in predicting the MIE of sanded Chinese fir wood dust.The model's effectiveness was validated through a comprehensive evaluation using five key indicators,i.e.,coefficient of determination(R2),mean absolute error(MAE),root mean square error(RMSE),mean absolute deviation(MAD),and mean absolute percentage error(MAPE).The model achieved an impressive R2 value of 0.999 61 on the training set and 0.969 05 on the test set,indicating that it had outstanding ability to capture the complex relationships between the input variables and the target variable.The model's robustness was further confirmed by its excellent performance in predicting the upper limit of MIE,with R2 values of 0.999 71 and 0.986 38 on the training and test sets,respectively.Error analysis using MAE,RMSE,MAD and MAPE demonstrated the model's high prediction accuracy.The low values of these metrics on the training set indicated that the model's predictions closely align with the actual MIE values.The low MAPE value suggested that the model's predictions deviated from the actual values by a small percentage,confirming its reliability and consistency across the range of MIE values.The XGBoost model also provided insights into the relative importance of the influencing factors.The weight analysis revealed that the dust mass concentration played the most significant role in predicting the MIE of sanding dust of Chinese fir wood.This finding emphasized the need for strictly controlling and monitoring dust mass concentration levels in wood processing environments to minimize the risk of fire and explosions.This study pioneered a new perspective on predicting the sanding dust MIE of Chinese fir wood by applying the advanced XGBoost machine learning model.The research findings not only filled the gap in the field of MIE prediction for sanding dust of Chinese fir,but also provided the wood processing industry with an efficient and practical risk assessment tool.This tool can help enterprises optimize production processes and enhance safety management levels,thereby promoting the sustainable development of the wood industry.关键词
杉木粉尘/最小点火能/预测模型/XGBoost/误差分析Key words
Chinese fir dust/minimum ignition energy/the prediction model/XGBoost/error analysis分类
安全科学引用本文复制引用
李时维,喻孜,颜振东,刘海良,周捍东..杉木砂光粉尘爆炸最小点火能预测模型[J].林业工程学报,2025,10(2):60-66,7.基金项目
国家重点研发计划(2022YFD2200705). （2022YFD2200705）
