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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 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.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.