Mohd Redzuan Mohd Sofian*
Volume6-Issue9
Dates: Received: 2025-07-10 | Accepted: 2025-09-19 | Published: 2025-09-20
Pages: 1331-1341
Abstract
This paper presents an empirical comparison of process control algorithms, with particular emphasis on classical Proportional–Integral–Derivative (PID) control, Model Predictive Control (MPC), and neural network-based methods in the context of complex industrial plants. Since industrial sectors frequently demand improvements in operational efficiency, product quality, and safety, it is plausible that robust and adaptable control systems are increasingly necessary to accommodate varying capacity requirements. The investigation made use of a real-world dataset alongside a simulated refinery dataset from the Tennessee Eastman (TE) process to benchmark the selected control strategies under diverse disturbances and process conditions. Evidence suggests that neural network-based controllers frequently achieved superior disturbance rejection and more accurate setpoint tracking than conventional PID or MPC designs. The evaluation was based on established quantitative measures, including the Integral of Squared Error (ISE) and the Integral of Absolute Error (IAE). The data seems to indicate that the enhanced performance of neural network controllers may be associated with their ability to model nonlinear process dynamics more effectively. However, challenges relating to their implementation in safety–critical environments, including operational and computational constraints, should be addressed. One interpretation is that the findings contribute to the theoretical understanding of process control and could provide practitioners with guidance on combining advanced and classical methods. In this context, hybrid control models might offer a viable means of integrating transparency, robustness, and predictive capability.
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DOI: 10.37871/jbres2188
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© 2025 Mohd Sofian MR. Distributed under Creative Commons CC-BY 4.0 
How to cite this article
Mohd Sofi an MR. Empirical Benchmarking of PID, MPC, and Neural Network Controllers Using Industrial Process Simulation Data. J Biomed Res Environ Sci. 2025 Sept 20; 6(9): 1331-1341. doi: 10.37871/jbres2188, Article ID: JBRES2188, Available at: https://www.jelsciences.com/articles/jbres2188.pdf
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References
- Seborg DE, Edgar TF, Mellichamp DA, Doyle FJ. Process dynamics and control .3rd ed. John Wiley & Sons; 2010.
- Qin SJ, Badgwell TA. A survey of industrial model predictive control technology. Control Engineering Practice. 2003;11(7):733-764. doi: 10.1016/S0967-0661(02)00186-7.
- Morari M, Zafiriou E. Robust process control. Prentice Hall; 1989.
- Ljung L. System identification: Theory for the user. 2nd ed. Prentice Hall; 1999.
- Downs JJ, Vogel EF. A plant-wide industrial process control problem. Computers & Chemical Engineering. 1993;17(3):245-255. doi:10.1016/0098-1354(93)80018-I.
- Camacho EF, Bordons C. Model predictive control. 2nd ed. Springer; 2007.
- Field A. Discovering statistics using IBM SPSS statistics. 4th ed. Sage; 2013.
- Åström KJ, Hägglund T. Advanced PID control. ISA; 2006.
- Goodfellow I, Bengio Y, Courville A. Deep learning. MIT Press; 2016.
- Brunton SL, Kutz JN. Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press; 2019.
- Qi H, Zhang R, Hou Z. Interpretability issues in neural network-based industrial controllers. IEEE Transactions on Industrial Informatics. 2023;19(1);342-356.
- Sánchez J, Suárez R, Moreno JC. Neural network-based predictive control for nonlinear processes. Processes. 2020;8(11);1401. doi: 10.3390/pr8111401.
- Rawlings JB, Mayne DQ, Diehl M. Model predictive control: Theory, computation, and design. 2nd ed. Nob Hill Publishing; 2017.
- DoshiVelez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv. 2017;1702.08608. doi: 10.48550/arXiv.1702.08608.
- Lee J, Bagheri B, Jin C. Introduction to cyber manufacturing. Manufacturing Letters. 2020;25:1-3. doi: 10.1016/j.mfglet.2020.02.002.
- Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing. 2021;115:213-237. doi: 10.1016/j.ymssp.2018.05.050.
- Zhu Y, Wang X, Liu Y. Neural network integrated model predictive control for nonlinear process systems. Processes. 2023;11(4):1102. doi: 10.3390/pr11041102.
- Downs JJ, Vogel EF. A plant-wide industrial process control problem. Computers & Chemical Engineering. 1993;17(3):245-255. doi: 10.1016/0098-1354(93)80018-I.
