About this Research Topic
First principles or physics- based models (typically material and energy balances) are the foundations of computational modeling in Chemical Engineering. In addition to system constraints, engineers rely on the chemical system of interest being soundly- defined, enough to simulate and make predictions of interest. This has given rise to process simulators such as ASPEN Plus, ASPEN HYSIS and gProms.
Traditional modeling in Chemical Engineering requires knowledge of the physical and chemical phenomena of the system of interest; and very often, intuition replaces or increases knowledge. For some systems, recent high throughput technologies and process monitoring have made data available in large quantities, sometimes approaching BigData magnitudes. This has generated the space for the use of machine learning and other data science technologies to model Chemical Engineering systems.
This Research Topic will focus on shedding light on aspects like (i) when first principles are feasible and adequate, (ii) when machine learning/ artificial intelligence (ML/ AI) are feasible and adequate and (iii) when first principles and ML/ AI have synergies that can be exploited for high fidelity modeling. We welcome Review articles and/ or Original Research articles within following frameworks:
• First principles modeling in chemical engineering – Challenges and opportunities. Other enabling technologies.
• Machine learning modeling in chemical engineering – Challenges and opportunities.
• Synergistic machine learning and first principles modeling in chemical engineering – challenges and opportunities. Future enabling technologies.
Traditional modeling in Chemical Engineering requires knowledge of the physical and chemical phenomena of the system of interest; and very often, intuition replaces or increases knowledge. For some systems, recent high throughput technologies and process monitoring have made data available in large quantities, sometimes approaching BigData magnitudes. This has generated the space for the use of machine learning and other data science technologies to model Chemical Engineering systems.
This Research Topic will focus on shedding light on aspects like (i) when first principles are feasible and adequate, (ii) when machine learning/ artificial intelligence (ML/ AI) are feasible and adequate and (iii) when first principles and ML/ AI have synergies that can be exploited for high fidelity modeling. We welcome Review articles and/ or Original Research articles within following frameworks:
• First principles modeling in chemical engineering – Challenges and opportunities. Other enabling technologies.
• Machine learning modeling in chemical engineering – Challenges and opportunities.
• Synergistic machine learning and first principles modeling in chemical engineering – challenges and opportunities. Future enabling technologies.
Keywords: computational methods in chemical engineering, first principles modeling, machine learning, artificial intelligence, chemical engineering, synergistic modeling
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