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By: Dr. Manjula Mallya and V Basil Hans.
1*Dept of Economics, Government First Grade College for Women, Mangalore, Karnataka, India
2 Research Professor, Srinivas University, Mangalore, Karnataka, India
Chemical separation processes play a critical role across chemical manufacturing, energy production, pharmaceuticals, and environmental remediation, yet they remain among the most energy-intensive operations in modern industry. Recent advancements in artificial intelligence (AI) offer transformative opportunities to significantly improve the efficiency, selectivity, and sustainability of these systems by leveraging computational power and data-driven insight. This article reviews the emerging integration of machine learning, data-driven modeling, and advanced optimization algorithms with both conventional and next-generation separation technologies, including distillation, membrane separations, adsorption, extraction, and various hybrid processes that combine multiple techniques for enhanced performance.
We discuss how AI enhances process design by enabling rapid prediction of separation performance, supporting the development of accurate surrogate models, and allowing adaptive process control under dynamic and uncertain operating conditions. AI-assisted tools enable multi-objective optimization that balances energy use, product purity, cost, and environmental impact more effectively than traditional approaches. Several recent case studies illustrate AI’s potential to reduce energy consumption, minimize solvent use, and accelerate materials discovery for next-generation separation media such as advanced membranes, porous adsorbents, and engineered sorbents.
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Refrences:
[1] N. Thomas Stacey, “Alternatives to distillation: multi-membrane permeation and petrol pre-blending for bio-ethanol recovery,” 2016. https://scholar.google.com/
[2] W. Wu, H. Xu, D. Zhang, and F. Mo, “Intelligent Chemical Purification Technique Based on Machine Learning,” 2024. (arXiv:2404.09114v1),
[3] S. Takeda, T. Hama, H. H. Hsu, T. Yamane et al., “AI-driven Inverse Design System for Organic Molecules,” 2020. arXiv
[4] Madenoor Ramapriya, Gautham, “Low-cost and Energy-efficient Solutions for Multicomponent Distillation” (2016). Open Access Dissertations. 1257. https://docs.lib.purdue.edu/open_access_dissertations/1257
\[5] R. Maleki, S. Mohammadreza Shams, Y. Mehdizadeh Chellehbari, S. Rezvantalab et al., “Materials discovery of ion-selective membranes using artificial intelligence,” 2022. ncbi.nlm.nih.gov
[6] Y. Yang, “DEVELOPMENT OF NOVEL DESIGN AND CONTROL APPROACHES FOR INTEGRATED CRYSTALLIZATION OPERATION AND SYSTEMS,” 2016. Open Access Dissertations, https://docs.lib.purdue.edu/
[7] Y. Luo, “Multi-Agent Control in Sociotechnical Systems,” 2017. doctoral thesis University of Delaware
[8] A. Himmel, J. Matschek, R. Kok, B. Morabito et al., “Machine Learning for Process Control of (Bio)Chemical Processes,” 2023. https://www.researchgate.net/publication
[9] W. B. Zimmerman and J. M. Rees, “Optimal modelling and experimentation for the improved sustainability of microfluidic chemical technology design,” 2009. Chemical Engineering Research and Design
[10] E. Costa Almeida, C. de Menezes Rebello, M. Fontana, L. Schnitman et al., “A Robust Learning Methodology for Uncertainty-aware Scientific Machine Learning models,” 2022. Mathematics 2023, 11(1), 74; https://doi.org/10.3390/math11010074
[11] C. Menezes Rebello, J. Jäschkea, and I. B. R. Nogueira, “Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry,” 2023. https://www.intel.com/
[12] C. Appl, A. Moser, F. Baganz, and V. C. Hass, “Digital Twins for Bioprocess Control Strategy Development and Realisation,” 2021. https://www.siemens.com/
[13] S. Shankaran Iyer, “Multi-scale Optimization Frameworks for Integrated Process and Material Design and Intensification,” 2019. https://oaktrust.library.tamu.edu/
[14] C. Liu, Y. Chen, and F. Mo, “Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence,” 2023. National Science Open, 3(2), 20230037