Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Abstract

This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), for modeling refining units comprised of two reactors and a separation train. The model is comprised of self-organizing-map and the neural network parts. The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part. In the neural network part, residual blocks enhance the convergence and accuracy, ensuring that the structure will not be overfitted easily. Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products. The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model, thus leading to more accurate optimization of the hydrocracker operation. Moreover, the MISR model has smoother error convergence than the previous model. Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms. Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

Graphical abstract

Keywords

hydrocracking / convolutional neural networks / self-organizing map / deep learning / data-driven optimization

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Acknowledgements

This work was supported by the National Natural Science Fund for Distinguished Young Scholars (Grant No. 61725301), the National Natural Science Foundation of China (Basic Science Center Program: Grant No. 61988101), International (Regional) Cooperation and Exchange Project (Grant No. 61720106008), and General Program (Grant No. 61873093).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://dx.doi.org/10.1007/s11705-022-2269-5 and is accessible for authorized users.

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