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

References

[1] Marafi A, Albazzaz H, Rana M S. Hydroprocessing of heavy residual oil: opportunities and challenges. Catalysis Today, 2019, 329: 125–134

[2] Iplik E, Aslanidou I, Kyprianidis K. Hydrocracking: a perspective towards digitalization. Sustainability, 2020, 12(17): 7058

[3] Ward J W. Hydrocracking processes and catalysts. Fuel Processing Technology, 1993, 35(1): 55–85

[4] Sánchez S, Rodríguez M A, Ancheyta J. Kinetic model for moderate hydrocracking of heavy oils. Industrial & Engineering Chemistry Research, 2005, 44(25): 9409–9413

[5] Kumar H, Froment G F. Mechanistic kinetic modeling of the hydrocracking of complex feedstocks, such as vacuum gas oils. Industrial & Engineering Chemistry Research, 2007, 46(18): 5881–5897

[6] Félix G, Ancheyta J. Using separate kinetic models to predict liquid, gas, and coke yields in heavy oil hydrocracking. Industrial & Engineering Chemistry Research, 2019, 58(19): 7973–7979

[7] Singh J, Kumar M, Saxena A K, Kumar S. Reaction pathways and product yields in mild thermal cracking of vacuum residues: a multi-lump kinetic model. Chemical Engineering Journal, 2005, 108(3): 239–248

[8] Qader S, Hill G. Hydrocracking of gas oil. Industrial & Engineering Chemistry Process Design and Development, 1969, 8(1): 98–105

[9] Bhutani N, Ray A K, Rangaiah G. Modeling, simulation, and multi-objective optimization of an industrial hydrocracking unit. Industrial & Engineering Chemistry Research, 2006, 45(4): 1354–1372

[10] Laxminarasimhan C S, Verma R P, Ramachandran P A. Continuous lumping model for simulation of hydrocracking. AIChE Journal, 1996, 42(9): 2645–2653

[11] Lababidi H M S, AlHumaidan F S. Modeling the hydrocracking kinetics of atmospheric residue in hydrotreating processes by the continuous lumping approach. Energy & Fuels, 2011, 25(5): 1939–1949

[12] Quann R J, Jaffe S B. Structure-oriented lumping: describing the chemistry of complex hydrocarbon mixtures. Industrial & Engineering Chemistry Research, 1992, 31(11): 2483–2497

[13] Becker P J, Serrand N, Celse B, Guillaume D, Dulot H. Comparing hydrocracking models: continuous lumping vs. single events. Fuel, 2016, 165: 306–315

[14] Becker P J, Serrand N, Celse B, Guillaume D, Dulot H. A single events microkinetic model for hydrocracking of vacuum gas oil. Computers & Chemical Engineering, 2017, 98: 70–79

[15] RosliMAzizN. Review of neural network modelling of cracking process. In: Second International Conference on Chemical Engineering (ICCE). Bandung, Indonesia: IOP, 2016

[16] Bhutani N, Rangaiah G P, Ray A K. First-principles, data-based, and hybrid modeling and optimization of an industrial hydrocracking unit. Industrial & Engineering Chemistry Research, 2006, 45(23): 7807–7816

[17] Fang H, Zhou J, Wang Z, Qiu Z, Sun Y, Lin Y, Chen K, Zhou X, Pan M. Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations. Frontiers of Chemical Science and Engineering, 2022, 16(2): 274–287

[18] Ma Y, Gao Z, Shi P, Chen M, Wu S, Yang C, Wang J, Cheng J, Gong J. Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization. Frontiers of Chemical Science and Engineering, 2022, 16(4): 523–535

[19] McGreavy C, Lu M, Wang X Z, Kam E K T. Characterisation of the behaviour and product distribution in fluid catalytic cracking using neural networks. Chemical Engineering Science, 1994, 49(24): 4717–4727

[20] Ochoa-Estopier L M, Jobson M, Smith R. Operational optimization of crude oil distillation systems using artificial neural networks. Computers & Chemical Engineering, 2013, 59: 178–185

[21] Yang F, Dai C, Tang J, Xuan J, Cao J. A hybrid deep learning and mechanistic kinetics model for the prediction of fluid catalytic cracking performance. Chemical Engineering Research & Design, 2020, 155: 202–210

[22] Song W, Mahalec V, Long J, Yang M, Qian F. Modeling the hydrocracking process with deep neural networks. Industrial & Engineering Chemistry Research, 2020, 59(7): 3077–3090

[23] Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278–2324

[24] IoffeSSzegedyC. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. Lille, France: JMLR, 2015

[25] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84–90

[26] SzegedyCLiuWJiaY QSermanetPReedSAnguelovDErhanDVanhouckeVRabinovichA. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA: IEEE, 2015

[27] HeKZhangXRenSSunJ. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV: IEEE, 2016

[28] Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao R X. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 2019, 115: 213–237

[29] Serin G, Sener B, Ozbayoglu A M, Unver H O. Review of tool condition monitoring in machining and opportunities for deep learning. International Journal of Advanced Manufacturing Technology, 2020, 109(3): 953–974

[30] Souza R M, Nascimento E G, Miranda U A, Silva W J, Lepikson H A. Deep learning for diagnosis and classification of faults in industrial rotating machinery. Computers & Industrial Engineering, 2021, 153: 107060

[31] Yuan J, Tian Y. A multiscale feature learning scheme based on deep learning for industrial process monitoring and fault diagnosis. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 151189–151202

[32] Elhefnawy M, Ragab A, Ouali M S. Fault classification in the process industry using polygon generation and deep learning. Journal of Intelligent Manufacturing, 2022, 33(5): 1531–1544

[33] Glaeser A, Selvaraj V, Lee S, Hwang Y, Lee K, Lee N, Lee S, Min S. Applications of deep learning for fault detection in industrial cold forging. International Journal of Production Research, 2021, 59(16): 4826–4835

[34] SimonyanKZissermanA. Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations. San Diego, CA: OpenReview.net, 2015

[35] XieSGirshickRDollárPTuZHeK. Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE, 2017

[36] ZagoruykoSKomodakisN. Wide residual networks. In: Proceedings of the British Machine Vision Conference (BMVC). York, UK: BMVA, 2016

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