Driven by prior evaluation and correction of finished gasoline blending formula,this study proposes a predictive evaluation and causal analysis method for gasoline blending formulation quality based on combining the light gradient boosting machine(LightGBM)with Shapley additive explanation(SHAP)interpretable machine learning and considering the advantages of high precision of complex models and strong post-hoc-SHAP interpretability.This method first optimizes the hyper-parameters of LightGBM by introducing the improved genetic algorithm(IGA),establishes a model that can simultaneously predict the performance and environmental indicators of finished gasoline,and then formulates the formula quality evaluation standards in light of national Ⅵ A standard and the actual production in factories to realize the prior evaluation of formula.Besides,the easy-to-operate univariate correction scheme for defective blending formula is proposed based on the global and local causes analysis of SHAP.The experimental results show that the IGA_LightGBM-based model can present more comprehensive and accurate predictors as compared with the traditional back propagation(BP)and random forest(RF)based model,and the LightGBM model with hyperparameters optimazed by random search and normal GA.The SHAP causal analysis can provide practical correction schemes.This method can be considered as a helpful exploration in applying the intelligent algorithms instead of human experiences.
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