Journal
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 59, Issue 7, Pages 3077-3090Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.iecr.9b06295
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Funding
- National Natural Science Foundation of China (Basic Science Center Program) [61988101]
- International (Regional) Cooperation and Exchange Project [61720106008]
- National Natural Science Foundation of China [61590923, 61973124, 61873093]
- Programme of Introducing Talents of Discipline to Universities (the 111 Project) [B17017]
- Fundamental Research Funds for the Central Universities [222201917006]
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In the refinery process, a vast amount of data is generated in daily production. How to make full use of these data to improve the simulation's accuracy is crucial to enhancing the refinery operating level. In this paper, a novel deep learning framework integrating the self-organizing map (SOM) and the convolutional neural network (CNN) is developed for modeling the industrial hydrocracking process. The SOM is used to map input variables into two-dimensional maps to extract process features. Then, these maps are fed into the CNN to predict the outputs of the hydrocracking process. The SOM adopted is free of training, which reduces the computational complexity, simplifies the application, and improves the prediction accuracy. Practical guidance on the application of the proposed framework is provided by comparing and analyzing different structures and parameters. Finally, an online modeling scheme is developed and applied in an actual hydrocracking process. Experimental results demonstrate that the proposed framework has great performance in modeling the hydrocracking process and provides a good reference for process optimization.
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