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Integrating Artificial Intelligence, Digital Twins, and Advanced Process Control for Sustainable and Efficient Chemical Manufacturing

https://doi.org/10.55640/ijaair-v03i08-14

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Muhammad Awais Liaqat ¹ ,

⁴ South Wales Business School, University of South Wales, UK

Abstract

The chemical industry faces unprecedented pressure to enhance operational efficiency while simultaneously reducing environmental impact and meeting stringent regulatory requirements. This article presents a comprehensive framework for integrating Artificial Intelligence (AI), Digital Twins (DT), and Advanced Process Control (APC) to achieve sustainable and efficient chemical manufacturing. The proposed methodology leverages AI-driven predictive models, including neural networks and reinforcement learning, to enable real-time optimization of complex nonlinear processes. Digital twins serve as the central integration platform, providing a continuously evolving virtual representation of physical systems that fuses process data, mechanistic models, and domain knowledge. The framework incorporates a knowledge graph-based semantic architecture that ensures interoperability, scalability, and adherence to FAIR principles for data and model management. Advanced process control strategies, augmented by AI agents, enable autonomous decision-making and adaptive control under varying operational conditions. A case study on a commercial-scale BTX recovery process demonstrates the system's effectiveness, achieving a 28.52% improvement in economic performance, 79.45% reduction in emissions, and 15.96% decrease in carbon footprint while maintaining strict regulatory compliance. The results validate that the synergistic integration of these technologies creates a paradigm shift toward self-optimizing, resilient, and sustainable chemical manufacturing systems.

Keywords

Artificial Intelligence, Digital Twins, Advanced Process Control, Sustainable Manufacturing

References

S. Chowdhury, A. Ghosh, S. Acharya, S. Saha, P.K. Pal, S. Roy, S.K. Lahiri, "Transforming chemical process engineering: The role of AI and machine learning in revolutionizing process systems," Canadian Journal of Chemical Engineering, vol. 104, no. 2, pp. 699, 2026.

"Process Simulation and Data Science," AIChE Process Development Symposium, 2026.

"Artificial intelligence reshapes chemical engineering: from reaction design to smart manufacturing," ENG. Chem. Eng., Higher Education Press, 2026.

"A knowledge graph framework for digital twins of chemical processes," Nature Chemical Engineering, vol. 3, pp. 286–297, 2026.

"AI Comes to Advanced Process Control," Chemical Processing, July 2026.

T.Y. Woo, S.Y. Kim, S. Tariq, S.K. Heo, C.K. Yoo, "Leveraging Generative AI and Large Language Model for Process Systems Engineering: A State-of-the-Art Review," Korean Journal of Chemical Engineering, vol. 42, no. 12, pp. 2787-2808, 2025.

"A semantic framework for chemical process digitalisation using ontologies," Chemical Engineering Journal, 2026.

"Technological innovations for continuous flow catalysis," Advances in Chemical Engineering, 2026.

"Industrial Agentic AI and generative modeling in complex systems," Current Opinion in Chemical Engineering, vol. 48, 101150, 2025.

A. Shrivastava, "Accelerating Speed to Market and Sustainability Using Digital Twins," Chemical Engineering, September 2025.

H.T. Liu, M.C. Fang, H.Y. Lee, J.D. Ward, C.T. Hsieh, "An AIoT-based operation guidance system for constrained emissions control and economic optimization of an operational commercial-scale chemical process," Journal of the Taiwan Institute of Chemical Engineers, 2026.

Additional references supporting the methodology and literature review:

J. Morbach, A. Yang, W. Marquardt, "OntoCAPE—A large-scale ontology for chemical process engineering," Engineering Applications of Artificial Intelligence, vol. 20, no. 2, pp. 147-161, 2007.

L. Pascazio, et al., "OntoSpecies: A comprehensive ontology for chemical species," Journal of Chemical Information and Modeling, 2022.

"OntoKin: An ontology for chemical reaction kinetics," Computers & Chemical Engineering, 2020.

S.J. Qin, "Survey on data-driven industrial process monitoring and diagnosis," Annual Reviews in Control, vol. 36, no. 2, pp. 220-234, 2012.

L. Wang, et al., "Deep learning for industrial process monitoring: A review," IEEE Transactions on Industrial Informatics, 2021.

D.E. Rumelhart, G.E. Hinton, R.J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, pp. 533-536, 1986.

S. Hochreiter, J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.

K. Cho, et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," EMNLP, 2014.

C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.

R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction, MIT Press, 2018.

J.B. Rawlings, D.Q. Mayne, Model Predictive Control: Theory and Design, Nob Hill Publishing, 2009.

S.J. Qin, T.A. Badgwell, "A survey of industrial model predictive control technology," Control Engineering Practice, vol. 11, no. 7, pp. 733-764, 2003.

V. Venkatasubramanian, et al., "A review of process fault detection and diagnosis: Part I," Computers & Chemical Engineering, vol. 27, no. 3, pp. 293-311, 2003.

V. Venkatasubramanian, et al., "A review of process fault detection and diagnosis: Part II," Computers & Chemical Engineering, vol. 27, no. 3, pp. 313-326, 2003.

V. Venkatasubramanian, et al., "A review of process fault detection and diagnosis: Part III," Computers & Chemical Engineering, vol. 27, no. 3, pp. 327-346, 2003.

M.L. Mavrovouniotis, Artificial Intelligence in Chemical Engineering, Springer, 2018.

C. Georgakis, "Design of dynamic experiments," Chemical Engineering Science, vol. 64, no. 14, pp. 3234-3247, 2009.

P. D. Christofides, et al., "Model predictive control for nonlinear processes," Chemical Engineering Science, vol. 57, no. 13, pp. 2347-2364, 2002.

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Integrating Artificial Intelligence, Digital Twins, and Advanced Process Control for Sustainable and Efficient Chemical Manufacturing

Abstract

Keywords

References

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