Table Of ContentOptimization of Sustainable
Enzymes Production
This book is designed as a reference book and presents a systematic approach
to analyzing evolutionary and nature-inspired population-based search algo-
rithms. Beginning with an introduction to optimization methods and algo-
rithms 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 learn-
ing and discusses optimization techniques to solve real-life problems.
• The book compiles the different machine learning models for opti-
mization of process parameters for the 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 the opti-
mization of sustainable enzyme production with AI integration in a
real-time environment.
• Featuring valuable insights, the book helps readers explore new ave-
nues leading toward 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.
Optimization of
Sustainable Enzymes
Production
Artificial Intelligence and Machine
Learning Techniques
Edited by
J. Satya Eswari
Nisha Suryawanshi
First edition published 2023
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
CRC Press is an imprint of Taylor & Francis Group, LLC
© 2023 selection and editorial matter, J Satya Eswari and Nisha Suryawanshi; individual
chapters, the contributors
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Library of Congress Cataloging‑in‑Publication Data
Names: Jujjavarapu, Satya Eswari, editor. | Suryawanshi, Nisha, editor.
Title: Optimization of sustainable enzymes production: artificial intelligence and machine
learning techniques / edited by J. Satya Eswari, Nisha Suryawanshi.
Description: First edition. | Boca Raton: Chapman & Hall/CRC Press, 2023.
| Includes bibliographical references and index.
Identifiers: LCCN 2022025614 (print) | LCCN 2022025615 (ebook) | ISBN 9781032273372
(hardback) | ISBN 9781032273433 (paperback) | ISBN 9781003292333 (ebook)
Subjects: LCSH: Enzymes--Biotechnology. | Sustainable engineering.
| Mathematical optimization. | Artificial intelligence--Industrial applications.
| Machine learning--Industrial applications.
Classification: LCC TP248.65.E59 O67 2023 (print) | LCC TP248.65.E59 (ebook)
| DDC 628--dc23/eng/20220830
LC record available at https://lccn.loc.gov/2022025614
LC ebook record available at https://lccn.loc.gov/2022025615
ISBN: 978-1-032-27337-2 (hbk)
ISBN: 978-1-032-27343-3 (pbk)
ISBN: 978-1-003-29233-3 (ebk)
DOI: 10.1201/9781003292333
Typeset in Palatino
by SPi Technologies India Pvt Ltd (Straive)
Contents
Preface ....................................................................................................................vii
Editors ......................................................................................................................ix
Contributors ............................................................................................................xi
1. Industrially Important Enzymes ..................................................................1
A. V. Narasimha Swamy
2. Applications of Industrially Important Enzymes ..................................19
Monalisa Padhan and Sushri Priyadarshini Panda
3. Optimization of Fermentation Process: Influence on Industrial
Production of Enzymes ................................................................................53
Ajay Nair, Archana S. Rao, S. M. Veena, Uday Muddapur, K. S. Anantharaju,
and Sunil S. More
4. Reforming Process Optimization of Enzyme Production Using
Artificial Intelligence and Machine Learning .........................................75
Rajeev Kumar, Ajay Nair, Archana S. Rao, S. M. Veena, Uday Muddapur,
K. S. Anantharaju, and Sunil S. More
5. Scale-Up Models for Chitinase Production, Enzyme Kinetics,
and Optimization ..........................................................................................99
P. V. Atheena, Keyur Raval, and Ritu Raval
6. Genetic Algorithm for Optimization of Fermentation
Processes of Various Enzyme Productions .............................................121
Karan Kumar, Heena Shah, and Vijayanand S. Moholkar
7. Optimization of Process Parameters of Various Classes
of Enzymes Using Artificial Neural Network .......................................145
Rajeev Kumar, S. M. Veena, C. Sowmya, Ajay Nair, Archana S. Rao,
Uday Muddapur, K. S. Anantharaju, and Sunil S. More
v
vi Contents
8. Advanced Evolutionary Differential Evolution and Central
Composite Design: Comparative Study for Process
Optimization of Chitinase Production ...................................................165
Nisha Suryawanshi and J. Satya Eswari
9. Artificial Bee Colony for Optimization of Process Parameters for
Various Enzyme Productions ....................................................................195
Dheeraj Shootha, Pooja Thathola, and Khashti Dasila
Index .....................................................................................................................217
Preface
Artificial intelligence has revolutionized the industrial procedures. Reduced
optimization computational cost is one of the most common artificial intel-
ligence applications. Several algorithms were introduced and tested based
on their assumptions for solving issues, each with its own set of hypoth-
eses. In this book, the principle of optimization is described and different
optimization techniques such as genetic algorithm (GA), artificial neural
network (ANN), particle swarm optimization (PSO), differential evolution
(DE), and artificial bee colony (ABC) algorithm are used in some case stud-
ies for process optimization of enzyme development, and various models
are completely developed and discussed. Machine learning is motivated by
its ability to save resources, machining time, and energy while increasing
yield in situations where traditional methods have reached their limits. This
is a complicated task in which a huge number of controllable parameters
have an impact on production in some way. Changing anywhere between
100 different control parameters to get the best combination of all the factors
is required. The optimization problem is to identify the best combination of
these parameters to optimize the production rate. This book takes a system-
atic method to examining population-based search algorithms that are evo-
lutionary and nature-inspired.
The books on optimization currently available deal with machine learning
and process optimization using algorithms or describe various algorithms for
optimization. The applications of various machine learning models or algo-
rithms, particularly for process optimization for the production of enzymes
are not reported. This book aims to compile different machine learning mod-
els for optimization of process parameters for the production of industrially
important enzymes. The production and optimization of various enzymes
produced by different microorganisms are elaborated in the book.
The students and researchers in optimization, operations research, artificial
intelligence, data mining, machine learning, computer science, and manage-
ment sciences will see the pros and cons of a variety of algorithms through
detailed examples and a comparison of algorithms.
Dr. J. Satya Eswari
Dr. Nisha Suryawanshi
vii
Editors
Dr. J. Satya Eswari has been an assistant professor for more than 8 years at
the Biotechnology Department of the National Institute of Technology (NIT),
Raipur, India. She did her M.Tech in Biotechnology at the Indian Institute
of Technology (IIT) Kharagpur and a Ph.D. at the IIT, Hyderabad, India.
During her research career, she worked as a Scientist (Woman Scientist –
Department of Science and Technology (DST)) in the Indian Institute of
Chemical Technology (IICT), Hyderabad. She has published more than 60
SCI/Scopus research papers, 6 books, a few book chapters, and 40 inter-
national conference proceedings. Her research contributions have received
wide global citations. She completed one DST woman scientist project (22
lakhs) and is currently handling one DST-Early career research project (43
lakhs) and one CCOST (4 lakhs). She has more than 7 years of teaching expe-
rience and 3 years of research experience. Dr. Eswari has been a guest edi-
tor for the Indian Journal of Biochemistry and Biophysics (SCI) and the Journal
of Chemical Technology and Biotechnology. She has rigorously pursued her
research in the areas of Environmental bioremediation, wastewater treat-
ment, bioprocess, and product development and bioinformatics. She gained
pioneering expertise in the application of mathematical and engineering
tools to Biotechnological processes. She has received the IEI Young Engineer
award, the Outstanding Woman by Venus International award, and the DK
Best Faculty award. Dr. Eswari has already guided three Ph.D. students and
is currently guiding three other Ph.D. students.
Dr. Nisha Suryawanshi is currently working as a guest faculty in the
Department of Zoology at Government Arts and Commerce College, Sagar,
Madhya Pradesh, India. She completed her Bachelor of Science (B.Sc.) in
Biotechnology (Honours) from Guru Ghasidas Central University Bilaspur
(Chhattisgarh, India), her Masters of Science (M.Sc.) in Biotechnology from
Dr. Hari Singh Gour Central University, Sagar (Madhya Pradesh). She
received a Doctor of Philosophy from the Department of Biotechnology,
National Institute of Technology, Raipur (Chhattisgarh, India). She has ten
publications in her research area in peer-reviewed SCI journals. During her
Ph.D., she worked in the area of bioprocess and product development. She
also has qualified national-level examinations CSIR-NET-JRF (Life science),
GATE (Biotechnology), ICAR-NET (Agriculture Biotechnology), and the
state-level examination MPSET.
ix
