文献类型：期刊文献

英文题名：Calibration-Augmented and Mechanism-Driven Deep Learning Hybrid Framework for Modeling Actual Distillation Processes

作者：Zuo, Ruyi[1];Li, Yue[1];Wei, Shun'an[1];Li, Zhongmei[2];Shi, Tao[1];Du, Wenli[3];Shen, Weifeng[1,3]

机构：[1]Chongqing Univ, Sch Chem & Chem Engn, Chongqing 400044, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：64

期号：7

起止页码：3856

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20250617842583);WOS:【SCI-EXPANDED(收录号:WOS:001416503600001)】；

基金：We acknowledge the financial support provided by the National Natural Science Foundation of China (no. 22278044); the Chongqing Science Fund for Distinguished Young Scholars (no. CSTB2022NSCQ-JQX0021); the Chongqing Innovation Support Key Program for Returned Overseas Chinese Scholars (cx2023002); and the Open Research Project of the State Key Laboratory of Industrial Control Technology, China (no. ICT2024B01); the Key Project of Technical Innovation and Application Development (No. CSTB2024TIAD-KPX0058); the Xinjiang Autonomous Region Regional Collaborative Innovation Special Science and Technology Assistance Plan Project (No. 2024E02036).

语种：英文

外文关键词：Distillation - Prediction models

摘要：Accurate prediction models are pivotal to improving the production efficiency and ensuring product quality in distillation processes. Traditional mechanism-based models neglect real-world fluctuations, while data-driven models suffer from noise and overlook chemical constraints, leading to inaccurate data and diminished performance. Therefore, a hybrid framework that embeds the mechanism-based model and data calibration into deep learning is proposed to leverage the complementary capabilities of both methodologies. The framework solves the problem of insufficient data accuracy of deep learning models by data calibration, including nonparameter regression, liquid level correction, and a robust estimator. It also takes thermodynamic constraints into account by integrating the mechanism-based models with the convolutional neural network (CNN), thereby capturing dynamic relationships between variables and efficiently predicting the key process parameters. The calibration-augmented and mechanism-driven CNN hybrid framework achieves exceptional predictive performance, validating the effectiveness of complex distillation modeling, further offering a novel insight into mechanism-based and data-driven hybrid paradigms for a digital twin in intelligent factories.