Process engineering is facing new grand challenges, including evolutions in material feedstock (e.g., renewable materials), energies (e.g., various sources, decarbonized), environmental considerations (e.g., carbon, water, biodiversity, chemical pollution in water, air, and soil); while performing new services such as intrant and product flexibility or polygeneration. A broad variety of innovations, fortunately, offers more possibilities to address these challenges, in addition to traditional functions, such as new: materials (e.g., ionic liquids or biomolecules), production techniques (e.g., 3D printing), driving forces (e.g., light or microwave), hybridization (e.g., membrane distillation or adsorption bioreactor), and engineering paradigms (e.g., modularization or decentralized production).
In order to achieve sustainable designs of processes to match current challenges with opportunities, process designers need to make complex decisions among an expansive set of possibilities at all scales (material, unit operation, process system) within a complex environment.
Recent advances in algorithmic techniques, such as Artificial Intelligence (AI), have recently gained in popularity and applicability and could be used as decision-aiding tools to assist process designers to cope with increasing complexity when faced with new horizons. This Research Topic, therefore, concerns the use of AI -in the general sense of automated decision-making on a complex engineering problem by mimicking intelligent behavior- to achieve sustainable process design.
AI covers a wide range of techniques at the intersection of data science and operational research communities. Thus, a broad variety of “intelligent” algorithms could be used, exploiting experiences from reward-driven optimization techniques (meta-heuristics, nature-inspired algorithms, etc.) and data-driven learning techniques (Machine Learning, Deep Learning, etc.).
This Research Topic aims at overviewing how AI could help to tackle sustainable processes; for instance, addressing new molecular separations, reaction pathways, innovative processes conceptualization, improving the sustainability of conventional technologies, or re-purposing them to new challenges. Another aim is to give insights into what AI can offer compared to conventional methods, in terms of design decisions such as materials (molecules, morphology, etc.); process intensification, flowsheet generation, or complex design problems (multi-products, flexibilities, multi-criteria, uncertainties, etc.). We welcome the submission of Original Research, Review, Mini Review, and Perspective articles on themes including, but not limited to:
• Fundamental concepts of AI for process design
• Current applications for sustainable processes
• Specific techniques (e.g., supervised learning, surrogate-modeling, reinforcement, generative models) used to assist process design
• Pure data-driven and hybrid physics-based & data-driven approaches
• Research gaps and potential developments of AI in the chemical engineering field.
Process engineering is facing new grand challenges, including evolutions in material feedstock (e.g., renewable materials), energies (e.g., various sources, decarbonized), environmental considerations (e.g., carbon, water, biodiversity, chemical pollution in water, air, and soil); while performing new services such as intrant and product flexibility or polygeneration. A broad variety of innovations, fortunately, offers more possibilities to address these challenges, in addition to traditional functions, such as new: materials (e.g., ionic liquids or biomolecules), production techniques (e.g., 3D printing), driving forces (e.g., light or microwave), hybridization (e.g., membrane distillation or adsorption bioreactor), and engineering paradigms (e.g., modularization or decentralized production).
In order to achieve sustainable designs of processes to match current challenges with opportunities, process designers need to make complex decisions among an expansive set of possibilities at all scales (material, unit operation, process system) within a complex environment.
Recent advances in algorithmic techniques, such as Artificial Intelligence (AI), have recently gained in popularity and applicability and could be used as decision-aiding tools to assist process designers to cope with increasing complexity when faced with new horizons. This Research Topic, therefore, concerns the use of AI -in the general sense of automated decision-making on a complex engineering problem by mimicking intelligent behavior- to achieve sustainable process design.
AI covers a wide range of techniques at the intersection of data science and operational research communities. Thus, a broad variety of “intelligent” algorithms could be used, exploiting experiences from reward-driven optimization techniques (meta-heuristics, nature-inspired algorithms, etc.) and data-driven learning techniques (Machine Learning, Deep Learning, etc.).
This Research Topic aims at overviewing how AI could help to tackle sustainable processes; for instance, addressing new molecular separations, reaction pathways, innovative processes conceptualization, improving the sustainability of conventional technologies, or re-purposing them to new challenges. Another aim is to give insights into what AI can offer compared to conventional methods, in terms of design decisions such as materials (molecules, morphology, etc.); process intensification, flowsheet generation, or complex design problems (multi-products, flexibilities, multi-criteria, uncertainties, etc.). We welcome the submission of Original Research, Review, Mini Review, and Perspective articles on themes including, but not limited to:
• Fundamental concepts of AI for process design
• Current applications for sustainable processes
• Specific techniques (e.g., supervised learning, surrogate-modeling, reinforcement, generative models) used to assist process design
• Pure data-driven and hybrid physics-based & data-driven approaches
• Research gaps and potential developments of AI in the chemical engineering field.