Evolutionary artificial neural network for temperature control in a batch polymerization reactor
Main Article Content
Abstract
The integration of artificial intelligence techniques introduces fresh perspectives in the implementation of these methods. This paper presents the combination of neural networks and evolutionary strategies to create what is known as evolutionary artificial neural networks (EANNs). In the process, the excitation function of neurons was modified to allow asexual reproduction. As a result, neurons evolved and developed significantly. The technique of a batch polymerization reactor temperature controller to produce polymethylmethacrylate (PMMA) by free radicals was compared with two different controls, such as PID and GMC, demonstrating that artificial intelligence-based controllers can be applied. These controllers provide better results than conventional controllers without creating transfer functions to the control process represented.
Keywords
ANNs, Evolved Neural Networks, Reactor Batch, Function Excitation, PMMA RNA, redes neuronales evolucionadas, función de excitación, PMMA
References
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[2] N. F. Salahuddin, A. Shamiri, M. A. Hussain, and N. Mostoufi, “Hybrid fuzzy-gmc control of gas-phase propylene copolymerization in fluidized bed reactors,” Chemical Engineering Journal Advances, vol. 8, p. 100161, 2021. [Online]. Available: https://doi.org/10.1016/j.ceja.2021.100161
[3] M. S. Mahmoud, M. Maaruf, and S. El-Ferik, “Neuro-adaptive fast terminal sliding mode control of the continuous polymerization reactor in the presence of unknown disturbances,” International Journal of Dynamics and Control, vol. 9, no. 3, pp. 1167–1176, Sep 2021. [Online]. Available: https://doi.org/10.1007/s40435-020-00731-x
[4] E. S. Yadav, P. Shettigar J, S. Poojary, S. Chokkadi, G. Jeppu, and T. Indiran, “Datadriven modeling of a pilot plant batch reactor and validation of a nonlinear model predictive controller for dynamic temperature profile tracking,” ACS Omega, vol. 6, no. 26, pp. 16 714–16 721, 2021, pMID: 34250331. [Online]. Available: https://doi.org/10.1021/acsomega.1c00087
[5] M. Maaruf, M. M. Ali, and F. M. Al-Sunni, “Artificial intelligence-based control of continuous polymerization reactor with input dead-zone,” International Journal of Dynamics and Control, vol. 11, no. 3, pp. 1153–1165, Jun 2023. [Online]. Available: https://doi.org/10.1007/s40435-022-01038-9
[6] P. Shettigar J, K. Lochan, G. Jeppu, S. Palanki, and T. Indiran, “Development and validation of advanced nonlinear predictive control algorithms for trajectory tracking in batch polymerization,” ACS Omega, vol. 6, no. 35, pp. 22 857–22 865, 2021. [Online]. Available: https://doi.org/10.1021/acsomega.1c03386
[7] H. Wang and Y. Chen, “Application of artificial neural networks in chemical process control,” Asian Journal of Research in Computer Science, vol. 14, no. 1, pp. 22–37, 2022. [Online]. Available: https://doi.org/10.9734/ajrcos/2022/v14i130325
[8] M. L. Dietrich, A. Brandolin, C. Sarmoria, and M. Asteasuain, “Mathematical modelling of rheological properties of low-density polyethylene produced in high-pressure tubular reactors,” IFAC-PapersOnLine, vol. 54, no. 3, pp. 378–382, 2021, 16th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2021. [Online]. Available:https://doi.org/10.1016/j.ifacol.2021.08.271
[9] P. Shettigar J, J. Kumbhare, E. S. Yadav, and T. Indiran, “Wiener-neural-networkbased modeling and validation of generalized predictive control on a laboratory-scale batch reactor,” ACS Omega, vol. 7, no. 19, pp. 16 341–16 351, 2022. [Online]. Available:https://doi.org/10.1021/acsomega.1c0749
[10] D. Q. Gbadago, J. Moon, M. Kim, and S. Hwang, “A unified framework for the mathematical modelling, predictive analysis, and optimization of reaction systems using computational fluid dynamics, deep neural network and genetic algorithm: A case of butadiene synthesis,” Chemical Engineering Journal, vol. 409, p. 128163, 2021. [Online]. Available: https://doi.org/10.1016/j.cej.2020.128163
[11] M. García-Carrillo, A. B. Espinoza-Martínez, L. F. Ramos-de Valle, and S. Sánchez-Valdés, “Simultaneous optimization of thermal and electrical conductivity of high density polyethylene-carbon particle composites by artificial neural networks and multi-objective genetic algorithm,” Computational Materials Science, vol. 201, p. 110956, 2022. [Online]. Available: https://doi.org/10.1016/j.commatsci.2021.110956
[12] L. Ghiba, E. N. Dragoi, and S. Curteanu, “Neural network-based hybrid models developed for free radical polymerization of styrene,” Polymer Engineering & Science, vol. 61, no. 3, pp. 716–730, 2021. [Online]. Available: https://doi.org/10.1002/pen.25611
[13] K. Bi, S. Zhang, C. Zhang, H. Li, X. Huang, H. Liu, and T. Qiu, “Knowledge expression, numerical modeling and optimization application of ethylene thermal cracking: From the perspective of intelligent manufacturing,” Chinese Journal of Chemical Engineering, vol. 38, pp. 1–17, 2021. [Online]. Available: https://doi.org/10.1016/j.cjche.2021.03.033
[14] K. Ahmad, H. R. Ghatak, and S. M. Ahuja, “Response surface methodology (rsm) and artificial neural network (ann) approach to optimize the photocatalytic conversion of rice straw hydrolysis residue (rshr) into vanillin and 4-hydroxybenzaldehyde,” Chemical Product and Process Modeling, 2022. [Online]. Available: https://doi.org/10.1515/cppm-2022-0003
[15] E. M. de Medeiros, H. Noorman, R. Maciel Filho, and J. A. Posada, “Production of ethanol fuel via syngas fermentation: Optimization of economic performance and energy efficiency,” Chemical Engineering Science: X, vol. 5, p. 100056, 2020. [Online]. Available: https://doi.org/10.1016/j.cesx.2020.100056
[16] S. Greydanus, M. Dzamba, and J. Yosinski, “Hamiltonian neural networks,” in Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://bit.ly/3qhXPxB
[17] J. Zeng, L. Cao, M. Xu, T. Zhu, and J. Z. H. Zhang, “Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation,” Nature Communications, vol. 11, no. 1, p. 5713, Nov 2020. [Online]. Available: https://doi.org/10.1038/s41467-020-19497-z
[18] H. Wang and R. Mo, “Review of neural network algorithm and its application in reactive distillation,” Asian Journal of Chemical Sciences, vol. 9, no. 3, pp. 20–29, 2021. [Online]. Available: https://doi.org/10.9734/ajocs/2021/v9i319073
[19] I. Moreno and J. Serracín, “Dr. Santiago Ramón y Caja,” Prisma Tecnológico, vol. 12, no. 1, pp. 86–87, 2021. [Online]. Available: https://doi.org/10.33412/pri.v12.1.2985
[20] V. Buhrmester, D. Munch, and M. Arens, “Analysis of explainers of black box deep neural networks for computer vision: A survey,” Machine Learning and Knowledge Extraction, vol. 3, no. 4, pp. 966–989, 2021. [Online]. Available: https://doi.org/10.3390/make3040048
[21] H. Chen, C. Fu, J. Zhao, and F. Koushanfar, “Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 4658–4664. [Online]. Available: https://doi.org/10.24963/ijcai.2019/647
[22] E. Zihni, V. I. Madai, M. Livne, I. Galinovic, A. A. Khalil, J. B. Fiebach, and D. Frey, “Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome,” PLOS ONE, vol. 15, no. 4, pp. 1–15, 04 2020. [Online]. Available: https://doi.org/10.1371/journal.pone.0231166
[23] R. Miikkulainen, J. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, H. Shahrzad, A. Navruzyan, N. Duffy, and B. Hodjat, “Chapter 15 - evolving deep neural networks,” in Artificial Intelligence in the Age of Neural Networks and Brain Computing, R. Kozma, C. Alippi, Y. Choe, and F. C. Morabito, Eds. Academic Press, 2019, pp. 293–312. [Online]. Available: https://doi.org/10.1016/B978-0-128154809.00015-3
[24] Bilal, M. Pant, H. Zaheer, L. Garcia-Hernandez, and A. Abraham, “Differential evolution: A review of more than two decades of research,” Engineering Applications of Artificial Intelligence, vol. 90, p. 103479, 2020. [Online]. Available: https://doi.org/10.1016/j.engappai.2020.103479
[25] A. Bashar, “Survey on evolving deep learning neural network architectures,” Journal of Artificial Intelligence, vol. 1, no. 2, pp. 73–82, 2019. [Online]. Available:https://doi.org/10.36548/jaicn.2019.2.003
[26] Y. Sun, B. Xue, M. Zhang, and G. G. Yen, “Evolving deep convolutional neural networks for image classification,” IEEE Transactions on Evolutionary Computation, vol. 24, no. 2, pp. 394–407, 2020. [Online]. Available: https://doi.org/10.1109/TEVC.2019.2916183
[27] E. Ekpo and I. Mujtaba, “Evaluation of neural networks-based controllers in batch polymerization of methyl methacrylate,” Neurocomputing, vol. 71, no. 7, pp. 1401–1412, 2008, progress in Modeling, Theory, and Application of Computational Intelligenc. [Online]. Available: https://doi.org/10.1016/j.neucom.2007.05.013
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Nov 22, 2023
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Mechanical Engineering - Materials
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