Recent Advances in Artificial Intelligence and Machine Learning for Thermochemical and Biochemical Bioprocess

Dr. Mukesh Kumar Awasthi has done his Ph.D. on the topic “Viscous Correction for the Potential Flow Analysis of Capillary and Kelvin-Helmholtz instability”. He is working as an Assistant Professor in the Department of Mathematics at Babasaheb Bhimrao Ambedkar University, Lucknow. Dr. Awasthi is specialized in the mathematical modeling of flow problems. He has taught courses of Fluid Mechanics, Discrete Mathematics, Partial differential equations, Abstract Algebra, Mathematical Methods, and Measure theory to postgraduate students. He has acquired excellent knowledge in the mathematical modeling of flow problems and he can solve these problems analytically as well as numerically. He has a good grasp of the subjects like viscous potential flow, electro-hydrodynamics, magneto-hydrodynamics, heat, and mass transfer. He has excellent communication skills and leadership qualities. He is self-motivated and responds to suggestions in a more convincing manner. Dr. Awasthi has qualified National Eligibility Test (NET) conducted on all India level in the year 2008 by the Council of Scientific and Industrial Research (CSIR) and got Junior Research Fellowship (JRF) and Senior Research Fellowship (SRF) for doing research. He has published 125 plus research publications (journal articles/books/book chapters/conference articles) in Elsevier, Taylor & Francis, Springer, Emerald, World Scientific, and many other national and international journals and conferences. Also, he has published 14 books. He has attended many symposia, workshops, and conferences in mathematics as well as fluid mechanics. He has got the “Research Awards” consecutively four times from 2013-2016 by the University of Petroleum and Energy Studies, Dehradun, India. He has also received the start-up research fund for his project “Nonlinear study of the interface in multilayer fluid system” from UGC, New Delhi. He is also listed in the top 2% influential researchers in the World prepared by Stanford University based on Scopus data in the years 2022 and 2023. His Orcid is 0000-0002-6706-5226, Google Scholar web link is https://scholar.google.co.in/citations?user=Dj3ktGAAAAAJ and research gate web link ishttps://www.researchgate.net/profile/Mukesh-Awasthi-2.Mohammad J. Taherzadeh is professor in Biotechnology since 2004 at University of Borås in Sweden. He is also director of Resource Recovery, a research profile with about 50 researchers to convert wastes to energy and value-added products. Prof. Taherzadeh has PhD in Bioscience and MSc and Bsc in Chemical Engineering. He is working on converting wastes and residuals to ethanol, biogas, fish feed and superabsorbents, in which fermentation development using bacteria, yeast and filamentous fungi has a heavy weight. Prof. Taherzadeh has more than 120 publications in scientific peer-reviewed journals, 10 book chapters and two patents about filamentous fungi. Mohammad is the panel chairman of “biotechnology, chemical technology and environmental technology” of Swedish Research Council, and also in the editorial board of Bioresource Technology and BioResources. Dr. Karthik Rajendran is a distinguished academic and researcher, currently serving as the Associate Dean at SRM University-AP, where he oversees the Office of the Dean Quality Assurance and Rankings. His work primarily focuses on the development of advanced biofuels, the integration of Energy and AI, and conducting thorough techno-economic analyses and lifecycle assessments.

• 1. Introduction and historical perspectives to AI and ML in bioprocess and thermochemical process engineering2. Pretreatment and characterization using machine learning approaches3. Applications of deep learning in gasification and pyrolysis4. Predictive modelling of biochemical reactions for biofuels, bioproducts and biomaterials.5. Bioreactor design and operation using AI6. Role of artificial neural networks in process control and monitoring in bioprocessing7. Natural language processing for bioprocess data analysis and visualization8. Usage of genetic algorithms towards thermochemical reactions and processing9. Support of AI towards experimental design for bio- and thermos-chemical process development10. Machine learning approaches for predictive maintenance in bioprocessing equipment11. Fusing AI and mechanistic modeling for better bioprocessing12. Use of AI for a better decision making of bioprocessing and thermochemical processing13. Machine learning in fermentative biohydrogen production14. ML approaches towards the reduction of technology development in bio- and thermos-chemical processes15. Integrating AI, ML, and IoT in Bioprocess 4.016. Future perspectives, challenges and restrictions related to the current application of AI and ML in the process industry