Optimization of Sustainable Enzymes Production - Satya Eswari, J; Suryawanshi, Nisha; (ed.) - Prospero Internet Bookshop

Optimization of Sustainable Enzymes Production

Artificial Intelligence and Machine Learning Techniques
 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
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Short description:

This book presents a systematic approach to analyze evolutionary and nature-inspired population-based search algorithms. The book presents current research on various applications of machine learning and discusses optimization techniques to solve real-life problems.

Long description:

This book is designed as a reference book and presents a systematic approach to analyze evolutionary and nature-inspired population-based search algorithms. Beginning with an introduction to optimization methods and algorithms and various enzymes, the book then moves on to provide a unified framework of process optimization for enzymes with various algorithms. The book presents current research on various applications of machine learning and discusses optimization techniques to solve real-life problems.



  • The book compiles the different machine learning models for optimization of process parameters for production of industrially important enzymes. The production and optimization of various enzymes produced by different microorganisms are elaborated in the book

  • It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making

  • Covers the best-performing methods and approaches for optimization sustainable enzymes production with AI integration in a real-time environment

  • Featuring valuable insights, the book helps readers explore new avenues leading towards multidisciplinary research discussions

The book is aimed primarily at advanced undergraduates and graduates studying machine learning, data science and industrial biotechnology. Researchers and professionals will also find this book useful.

Table of Contents:

1. Industrially Important Enzymes. 2. Applications of Industrially important enzymes. 3. Optimization of Fermentation Process: Influence on Industrial Production of Enzymes. 4. Reforming process optimization of enzyme production using artificial intelligence and machine learning. 5. Scale-up models for chitinase production, enzyme kinetics, and optimization. 6. Genetic Algorithm for optimization of fermentation process of various enzyme production. 7. Optimization of process parameter of various classes of enzymes using artificial neural network. 8. Advanced Evolutionary Differential Evolution and Central Composite Design: Comparative Study for process optimization of chitinase production. 9. Artificial bee colony for optimization of process parameters for various enzyme productions.