Artificial Intelligence in Biofuels Production.

Format:

Book

Author/Creator:

Salama, Sayed.

Contributor:

Salama, Sayed.

Series:

Woodhead Series in Bioenergy Series

Language:

English

Subjects (All):

Renewable energy sources.

Biodiesel fuels.

Physical Description:

1 online resource (553 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier Science & Technology, 2025.

Summary:

Artificial Intelligence in Biofuels Production offers a comprehensive guide to leveraging AI for optimizing and predicting biofuels production.AI tools can significantly enhance process efficiency and reduce costs in the biofuels industry.

Contents:

Front Cover

Artificial Intelligence in Biofuels Production

Copyright Page

Contents

List of contributors

About the editor

Foreword

Preface

1 An overview of biofuels with artificial intelligence

1.1 Introduction

1.1.1 Conventional fuel production rate and current energy crisis

1.1.1.1 Fuel properties and environmental issues

1.1.2 Overview of biofuel

1.2 Various generations of biofuels and types

1.3 Types of biofuels

1.3.1 Bioethanol production

1.3.1.1 Fermentation technologies

1.3.1.2 Fermentation process

1.3.1.3 Higher alcohol

1.3.2 Biodiesel generation

1.3.2.1 Transesterification of oil/lipid

1.3.2.2 Alkaline catalytic transesterification

1.3.2.3 Acid-catalyzed transesterification

1.3.2.4 Enzymatic transesterification

1.3.2.5 Advantages and disadvantages of biodiesel

1.3.2.6 Advantages of lipases and disadvantages of lipases

1.3.3 Anaerobic digestion

1.3.3.1 Factors affecting biogas production

Feedstocks

Temperature

Nutrients

pH and buffering capacity

Volatile fatty acids

Organic loading rate

1.3.3.2 Gas phase analysis

1.3.3.3 Liquid phase

1.3.3.4 Microbial community composition

1.3.4 Biohydrogen production

1.3.5 Fischer-Tropsch process for liquid hydrocarbons

1.4 Current applications of biofuels

1.5 Challenges in biofuel

1.6 Introduction to artificial intelligence and machine learning

1.6.1 Tyes of artificial intelligence and machine learning

1.7 Application of artificial intelligence in different biofuels

1.7.1 Machine learning application in biodiesel

1.7.2 Bioethanol process optimization and production

1.7.3 Anaerobic digestion process optimization

1.8 Relative importance of variables on predictability

1.9 Conclusion

References

2 Waste materials as raw material for biofuel production.

2.1 Introduction

2.2 Waste materials

2.3 Health issues caused by wastewater

2.4 Types of wastewater

2.4.1 Agricultural wastewater

2.4.2 Industrial wastewater

2.5 Types of pollutants in wastewater

2.5.1 Micropollutants and heavy metals

2.6 Types of adsorbents used for wastewater

2.7 Types of conversion technology for producing biofuel

2.8 Current and future challenges

2.9 Conclusion

3 Advancement in biodiesel production and economic feasibility

3.1 Introduction

3.2 Advancements in biodiesel production systems

3.2.1 Critical parameters

3.2.1.1 Compositional variables

Feedstock effects

Catalyst effects

3.2.1.2 Operational variables

Calcination and reaction temperature effects

Alcohol-to-oil ratio and mechanical stirring

3.2.2 Critical technologies

3.2.2.1 Advanced reactors

Ultrasonic radiation reactor

Benefits and constraints

Plasma reactor

Noncatalytic plasma reactors

Catalytic plasma reactors

Hybrid plasma reactors

Microwave reactors

3.2.2.2 Other transesterification reactors

3.3 Economic feasibility

3.3.1 Operating cost

3.3.2 Benefits and constraints

3.4 Conclusion and future outlook

4 Exploring the frontier of bioethanol production: real-time modeling

4.1 Bioethanol

4.2 Feedstocks for bioethanol production

4.2.1 Lignocellulosic source for bioethanol production

4.3 Pretreatment of lignocellulosic biomass

4.3.1 Physical pretreatment

4.3.2 Chemical pretreatment

4.3.3 Physicochemical treatment

4.3.4 Biological pretreatment

4.4 Hydrolysis of lignocellulosic biomass

4.5 Cell immobilization

4.5.1 Optimization of immobilized enzyme systems

4.5.2 Immobilization of yeast cells

4.5.3 Types of immobilization.

4.5.3.1 Entrapment method

4.6 Bioethanol fermentation

4.6.1 Current fermentation strategies for bioethanol production

4.6.2 Hexose and pentose utilization in different microorganisms

4.6.3 Anaerobic xylose metabolism and eukaryotic glycolytic pathways

4.6.4 Alternative carbohydrate catabolic pathways in prokaryotes

4.7 Machine learning models for bioethanol

4.7.1 Ethanol yield prediction using machine learning models

4.7.2 Optimization of ethanol production using hybrid machine learning approaches

4.8 Conclusion

5 Bio-oil and aviation production: applications and upgradation

5.1 Introduction to bio-oil

5.2 Recent technologies used in bio-oil production

5.2.1 Catalytic pyrolysis

5.2.2 Fast pyrolysis

5.2.3 Thermal pyrolysis

5.2.4 Bifunctional catalysts

5.2.5 Novel catalytic processes

5.2.6 Coprocessing techniques

5.2.7 Slow pyrolysis

5.2.8 Hydrothermal liquefaction

5.3 Compositional analysis of bio-oil through different approaches

5.3.1 Chemical composition of bio-oil

5.3.2 Approaches for composition analysis

5.3.2.1 Nuclear magnetic resonance spectroscopy

5.3.2.2 Gas chromatography-mass spectrometry

5.3.2.3 Ultrahigh-resolution mass spectrometry

5.3.2.4 Gel permeation chromatography

5.3.2.5 Centrifugal partition chromatography

5.3.2.6 High performance liquid chromatography

5.3.2.7 Hydroxyl group profiling

5.3.2.8 Other techniques

5.4 Bio-oil properties

5.4.1 Stability and content of bio-oil

5.4.2 Heating value

5.4.3 Viscosity and density

5.4.4 Acidity

5.4.5 Stability

5.4.6 Corrosiveness

5.5 Upgrading bio-oil is essential for enhancing its fuel properties

5.5.1 Hydrotreating

5.5.2 Supercritical fluid upgrading

5.5.3 Hydrocracking

5.5.4 Esterification

5.5.5 Catalytic cracking

5.5.6 Emulsification.

5.5.7 Solvent addition

5.5.8 Integrated systems

5.6 Factors influence the bio-oil

5.6.1 Bio-oil pyrolysis product yield

5.6.2 Substrate composition's effect on pyrolysis

5.6.3 Factor effecting pyrolysis rate

5.7 Artificial intelligence in bio-oil

5.7.1 Artificial intelligence-aided bio-oil optimization

5.7.2 Model training and validation

5.7.3 Application of artificial intelligence in bio-oil production

5.7.4 Process optimization

5.7.5 Challenges for artificial intelligence

5.7.6 Future direction for artificial intelligence

5.8 Economic feasibility production of bio-oil

5.8.1 Feedstock

5.8.2 Industrial scalable technologies

5.8.3 Operational parameters

5.8.4 Techno-economic analysis

5.9 Challenges and future directions

5.10 Conclusion

6 Biohythane as sustainable biofuel

6.1 Introduction

6.2 Importance of biohythane in renewable energy

6.3 Composition of biohythane

6.3.1 Hydrogen and methane as key components

6.3.2 Proportions of hydrogen to methane in biohythane

6.4 The biochemical pathways of biohythane production

6.4.1 Fermentation and anaerobic digestion

6.4.2 Microbial communities involved in biohythane production

6.4.3 Overview of anaerobic digestion process

6.5 Hydrolysis

6.5.1 Acidogenesis

6.5.2 Acetogenesis

6.5.3 Methanogenesis

6.6 Hydrogen production in biohythane

6.6.1 Dark fermentation

6.6.2 Microbes responsible for hydrogen production

6.7 Methane production in biohythane

6.7.1 Methanogenesis pathway

6.7.2 Role of archaea in methane production

6.8 Conventional biohythane production and its limitations

6.9 Biohythane production in bioelectrochemical system

6.10 Factors affecting biohythane production

6.10.1 Feedstock types and their influence

6.10.2 Temperature and pH levels.

6.10.3 Retention time in digesters

6.11 Biohythane as a dual-energy resource

6.11.1 Combined benefits of hydrogen and methane

6.11.2 Comparison with other renewable energy sources

6.12 Environmental impact of biohythane

6.12.1 Reduced greenhouse gas emissions

6.12.2 Waste management and resource recovery

6.13 Applications of biohythane

6.13.1 Power generation

6.13.2 Transportation fuel

6.13.3 Industrial use

6.14 Challenges in biohythane production

6.14.1 Technical barriers

6.14.1.1 Feedstock variability and pretreatment requirements

6.14.1.2 Microbial community management

6.14.1.3 Integration of production processes

6.14.2 Economic considerations

6.14.2.1 Production costs

6.14.2.2 Market competition

6.14.2.3 Investment in research and development

6.15 Future prospects for biohythane

6.15.1 Technological advancements

6.15.2 Policy and regulatory support

6.16 Biohythane and the circular economy

6.16.1 Waste-to-energy concept

6.16.2 Sustainability and renewable energy

6.17 Scaling up biohythane production

6.17.1 From laboratory to industrial scale

6.17.2 Infrastructure requirements

6.18 Conclusion

7 Biofuels for aviation

7.1 Introduction

7.1.1 Definition of aviation biofuels and their role in sustainable aviation

7.1.2 Importance of transitioning from fossil-based jet fuels to biofuels

7.1.3 Current challenges with traditional aviation fuels (e.g., carbon emissions, price volatility, and supply chain risks)

7.1.4 Types of biofuels for aviation

7.1.5 First-generation biofuels

7.1.5.1 Features and manufacturing

7.1.5.2 Advantages and disadvantages

7.1.5.3 Production methods

7.1.6 Second-generation biofuels

7.1.6.1 Features and manufacturing

7.1.6.2 Advantages and disadvantages.

7.1.6.3 Third-generation and advanced biofuels.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

0-443-26719-7

0-443-26718-9

9780443267192

OCLC:

1551920257