Abstract: This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering: predicting and optimizing light olefin yields in industrial methanol-to-olefins (MTO) processes. Our approach integrates advanced machine learning techniques with chemical engineering principles to tackle the complexities of non-stationary, highly volatile production data in large-scale chemical manufacturing. The framework employs the maximal information coefficient (MIC) algorithm to analyze and select the significant variables from MTO process parameters, forming a robust dataset for model development. We implement a transformer-based time series forecasting model, enhanced through positional encoding and hyperparameter optimization, significantly improving predictive accuracy for ethylene and propylene yields. The model's interpretability is augmented by applying SHapley additive exPlanations (SHAP) to quantify and visualize the impact of reaction control variables on olefin yields, providing valuable insights for process optimization. Experimental results demonstrate that our model outperforms traditional statistical and machine learning methods in accuracy and interpretability, effectively handling nonlinear, non-stationary, highvolatility, and long-sequence data challenges in olefin yield prediction. This research contributes to chemical engineering by providing a novel computerized methodology for solving complex production optimization problems in the chemical industry, offering significant potential for enhancing decisionmaking in MTO system production control and fostering the intelligent transformation of manufacturing processes.

Key words: Methanol-to-Olefins, Transformer, Explainable AI, Mathematical modeling, Model-predictive control, Numerical analysis

摘要： This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering: predicting and optimizing light olefin yields in industrial methanol-to-olefins (MTO) processes. Our approach integrates advanced machine learning techniques with chemical engineering principles to tackle the complexities of non-stationary, highly volatile production data in large-scale chemical manufacturing. The framework employs the maximal information coefficient (MIC) algorithm to analyze and select the significant variables from MTO process parameters, forming a robust dataset for model development. We implement a transformer-based time series forecasting model, enhanced through positional encoding and hyperparameter optimization, significantly improving predictive accuracy for ethylene and propylene yields. The model's interpretability is augmented by applying SHapley additive exPlanations (SHAP) to quantify and visualize the impact of reaction control variables on olefin yields, providing valuable insights for process optimization. Experimental results demonstrate that our model outperforms traditional statistical and machine learning methods in accuracy and interpretability, effectively handling nonlinear, non-stationary, highvolatility, and long-sequence data challenges in olefin yield prediction. This research contributes to chemical engineering by providing a novel computerized methodology for solving complex production optimization problems in the chemical industry, offering significant potential for enhancing decisionmaking in MTO system production control and fostering the intelligent transformation of manufacturing processes.

关键词: Methanol-to-Olefins, Transformer, Explainable AI, Mathematical modeling, Model-predictive control, Numerical analysis