基于NMI-GBR-SHAP的甲醇制烯烃催化剂积炭过程解释性分析OA
Interpretative analysis of carbon deposition process of methanol to olefins catalyst based on NMI-GBR-SHAP
甲醇制烯烃(methanol to olefins,MTO)工艺中催化剂积炭过程关联因素多且存在复杂交互作用,难以有效调控积炭量.本研究构建基于归一化互信息(NMI)、梯度提升回归(GBR)与SHAP的集成分析框架对催化剂积炭的主要影响因素进行解释性研究,发现当再生一旋入口线速维持在16.0~18.5 m/s,同时配合9~11 t/h的反应器保护蒸汽,将再生器烧焦总风量控制在26000~30000 m3/h且再生滑阀阀位保持在34%~36%,并且蒸汽配入率不低于34%时,能够对催化剂的积炭情况进行有效调控(6.5%~7.3%).基于上述工艺指标构建基准限,一旦运行工况超出该监测限,通过对交互特征进行SHAP分析,能够对相应的积炭趋势(变化率)作出解释,为工艺过程的平稳运行提供必要的监测信息,为MTO过程催化剂积炭监测提供兼具预测精度和机理解释性的技术方案.
Catalyst carbon deposition in the methanol to olefins(MTO)process is complex and interdependent,making it difficult to effectively control carbon deposition.This study employed an integrated analytical framework based on normalized mutual information(NMI),gradient boosting regression(GBR),and SHAP to explain the key factors influencing catalyst carbon deposition.The results revealed that when the regeneration swirl inlet linear velocity was maintained at 16.0-18.5 m/s,combined with a reactor protection steam rate of 9-11 t/h,the total regenerator char air volume was controlled at 26000-30000 m3/h,the regeneration slide valve position was maintained at 34%—36%,and the steam injection rate was no less than 34%,catalyst carbon deposition could be effectively controlled(6.5%—7.3%).Based on the above process parameters,a baseline limit is constructed.Once the operating conditions exceed this monitoring limit,SHAP analysis of the interaction characteristics can explain the corresponding coking trend(rate of change),providing necessary monitoring information for the stable operation of the process and offering a technical solution with both predictive accuracy and mechanistic explanation for catalyst coking monitoring in the MTO process.
李文亮;张浩;陈鸣睿;梁晨;翟持
昆明理工大学化学工程学院,云南 昆明 650500西南大学化学化工学院,重庆 400715昆明理工大学化学工程学院,云南 昆明 650500昆明理工大学化学工程学院,云南 昆明 650500昆明理工大学化学工程学院,云南 昆明 650500
化学化工
甲醇制烯烃催化剂积炭归一化互信息梯度提升回归可解释机器学习过程控制预测化学过程
methanol to olefinscatalyst cokingnormalized mutual informationgradient boosting regressioninterpretable machine learningprocess controlpredictionchemical processes
《化工学报》 2026 (2)
791-802,12
云南省兴滇英才支持计划项目(KKRD202205037)
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