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Full Description
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, and synthesizes the latest scientific advancements and practical applications in the field of thermochemical and biochemical bioprocesses. The book 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. Recent Advances in Artificial Intelligence and Machine Learning for Thermochemical and Biochemical Bioprocess provides a solid foundation for applying AI and ML techniques to tackle pressing challenges in clean fuel production, waste management, and climate change mitigation.
Contents
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
