Ensemble transfer learning framework for outflow compositions prediction in steam cracking process
Methods for modeling the steam cracking process were reviewed,and the problem of data scarcity faced in industrial realities was described.Facing the massive small dataset modeling requirements in petrochemical industry,an ensemble transfer learning framework was proposed by making full use of the historical production data.First,basic deep learning models were established on a specific working condition with sufficient data.Then,transfer learning techniques were applied to the new working conditions with a small dataset.The process knowledge from the source domain was transferred to the target domain using parameter-based methods.Finally,ensemble learning was introduced to integrate the obtained transfer learning models,resulting in enhanced performance.The performance of entire modeling framework was found to be industrially acceptable on several practical cases,and further layer transferability analysis and SHapley Additive exPlanation(SHAP)feature importance analysis were implemented to provide a better understanding of the model.The results illustrated that the model trained by this method had good accuracy,stability,computational efficiency and interpretability to meet industrial requirements.
万方数据
维普
