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

Erscheinungsjahr 2026
ISBN: 978-0-443-36672-7
Verlag: Elsevier Science

Buch, Englisch, 500 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Recent Advances in Artificial Intelligence and Machine Learning for Thermochemical and Biochemical Bioprocess delves into the transformative role of AI and ML in enhancing the efficiency and effectiveness of bioprocess operations. The book covers everything from predictive modeling and bioreactor design to the integration of IoT in Bioprocess 4.0 while also synthesizing the latest scientific advancements and practical applications in the field of thermochemical and biochemical bioprocesses. It explores a wide array of topics, including deep learning applications in gasification, the role of artificial neural networks in process control, and the use of genetic algorithms in thermochemical processing.

Each chapter offers invaluable insights into current methodologies and future perspectives, providing a solid foundation for applying AI and ML techniques to tackle pressing challenges in clean fuel production, waste management, and climate change mitigation.

Autoren/Hrsg.

Awasthi, Mukesh Kumar

Rajendran, Karthik

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen

Inhaltsverzeichnis

1. Introduction and historical perspectives to AI and ML in bioprocess and thermochemical process engineering
2. Pretreatment and characterization using machine learning approaches
3. Applications of deep learning in gasification and pyrolysis
4. Predictive modelling of biochemical reactions for biofuels, bioproducts and biomaterials.
5. Bioreactor design and operation using AI
6. Role of artificial neural networks in process control and monitoring in bioprocessing
7. Natural language processing for bioprocess data analysis and visualization
8. Usage of genetic algorithms towards thermochemical reactions and processing
9. Support of AI towards experimental design for bio- and thermos-chemical process development
10. Machine learning approaches for predictive maintenance in bioprocessing equipment
11. Fusing AI and mechanistic modeling for better bioprocessing
12. Use of AI for a better decision making of bioprocessing and thermochemical processing
13. Machine learning in fermentative biohydrogen production
14. ML approaches towards the reduction of technology development in bio- and thermos-chemical processes
15. Integrating AI, ML, and IoT in Bioprocess 4.0
16. Future perspectives, challenges and restrictions related to the current application of AI and ML in the process industry

Über Autor(innen)

Rajendran, Karthik
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.

Awasthi, Mukesh Kumar
Mukesh Kumar Awasthi is Assistant Professor in the Department of Mathematics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. His research focuses on the mathematical modelling of fluid flow problems, with particular interests in viscous potential flow, electrohydrodynamics, magnetohydrodynamics, heat and mass transfer, and fluid instability phenomena. He teaches postgraduate courses in fluid mechanics, discrete mathematics, partial differential equations, abstract algebra, mathematical methods, and measure theory. His work encompasses both analytical and numerical approaches to modelling complex flow systems. He has received research funding for projects in nonlinear fluid dynamics and has been recognized with research awards for his contributions to the field. He is the author and editor of numerous scholarly publications, including books, book chapters, journal articles, and conference papers. His research contributes to the advancement of mathematical methods and modelling techniques in fluid mechanics and related disciplines.