Search

Browse Subject Areas

For Authors

Submit a Proposal

Optimizing Biofuel Production with Artificial Intelligence

Edited by Arindam Kuila & Depak Kumar
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394301218  |  Hardcover  |  
370 pages
Price: $225 USD
Add To Cart

One Line Description
Optimizing Biofuel Production with Artificial Intelligence will help readers discover how integrating artificial intelligence with biotechnological advancements can revolutionize biofuel production, ensuring a sustainable energy future in response to pressing global challenges like pollution and climate change.

Audience
Students, educators, researchers, policymakers, and industry professionals interested in the use of artificial intelligence in the energy sector

Description
Optimizing Biofuel Production with Artificial Intelligence presents artificial intelligence as a technique to aid the production of biofuels. Recently, tremendous developments have been made in energy and environmental biotechnologies, spurred by societal issues like pollution control, energy security, and climate change. Energy can be obtained from a variety of sources, including coal, oil, natural gas, solar, wind, and nuclear energy. The need to transition to new energy results from finite resources and economic sustainability. Biotechnological process optimization is crucial for ensuring a quality final product and boosting bioconversion performance efficiency. When combined with traditional simulation and modeling methods, artificial intelligence and computer technology can help define ideal process parameters and save total process costs. The energy sector can benefit from artificial intelligence in several ways, including increased asset efficiency, early detection and assessment of wildfire risks, assistance with vegetation management and storm recovery, and optimized energy use. The new frontier for energy is biomass.

Back to Top
Author / Editor Details
Arindam Kuila, PhD is an assistant professor in the Department of Bioscience and Biotechnology, Banasthali Vidyapith, Rajasthan, India. He has co-authored 35 peer-reviewed articles and 18 book chapters, edited 12 books, and filed six patents. In addition to his written work, he has completed an Indo-Brazil collaborative project funded by India’s Department of Biotechnology. His research interests include environmental biotechnology, lignocellulosic biofuel, and bioremediation.

Depak Kumar, PhD is an assistant professor in the Department of Chemical Engineering, Banasthali Vidyapith, Rajasthan, India. He has published several articles in international journals of repute, as well as three book chapters and three books. His research interests include solar cell degradation, stability of solar cells, and energy conversion devices.

Back to Top

Table of Contents
Preface
1. Artificial Intelligence in Biofuel Applications
Neha Jain, Anuj Rohatgi, Jain Suransh and Depak Kumar
1.1 Introduction
1.1.1 Brief Overview of Biofuels and Their Importance
1.1.2 The Role of AI in Advancing Biofuel Technology
1.2 AI in Feedstock Selection and Optimization
1.2.1 Machine Learning for Crop Selection and Yield Prediction
1.2.1.1 Crop Selection
1.2.1.2 Yield Prediction
1.2.2 Genetic Algorithms for Optimizing Some Biofuel Crop Attributes
1.2.2.1 Trait Optimization
1.2.2.2 Integration with Genomic Data
1.3 AI-Driven Process Optimization
1.3.1 Neural Networks for Biofuel Production Process Control
1.3.2 Predictive Modeling for Conversion Efficiency Improvement
1.4 AI in Biofuel Quality Control
1.4.1 Computer Vision and Spectroscopy for Rapid Quality Assessment
1.4.1.1 Computer Vision for Visual Quality Inspection
1.4.1.2 Hyperspectral Imaging for Comprehensive Quality Analysis
1.4.1.3 Raman Spectroscopic for Molecular-Level Analysis
1.4.2 Predicting Fuel Properties Using Machine Learning
1.4.2.1 Comprehensive Property Prediction Using Ensemble Methods
1.4.2.2 Deep Learning on Complex Property Relationships
1.4.2.3 Transfer Learning for Improved Generalization
1.5 AI for Sustainable Biofuel Production
1.5.1 Life Cycle Assessment Optimization Using AI
1.5.1.1 Machine Learning for Data Gap Filling in LCA
1.5.1.2 Dynamic and Consequential LCA with AI
1.5.2 AI-Powered Resource Management and Waste Reduction
1.5.2.1 Predictive Maintenance for Equipment Efficiency
1.5.2.2 AI for Water Management in Biofuel Production
1.5.2.3 Machine Learning for Byproduct Valorization
1.6 Biofuel Supply Chain Optimization
1.6.1 AI for Demand Forecasting and Inventory Management
1.7 AI in Biofuel Research and Development
1.8 Challenges and Future Directions
1.8.1 Current Limitations of AI in Biofuel Applications
1.8.1.1 Data Quality and Availability
1.8.1.2 Model Interpretability
1.8.1.3 Complexity of Biological Systems
1.8.1.4 Scalability and Real-World Implementation
1.8.2 Emerging AI Technologies and Their Potential Impact
1.8.2.1 Federated Learning
1.8.2.2 Explainable AI (XAI)
1.8.2.3 Transfer Learning
1.8.2.4 Reinforcement Learning for Process Control
1.8.2.5 Integration of AI with High-Throughput Experimentation
1.9 Conclusion
References
2. Artificial Intelligence in Biofuel Production
Vasu Chaudhary and Depak Kumar
2.1 Introduction
2.2 Biomass to Biofuel Production
2.2.1 Pyrolysis
2.2.2 Hydrothermal Liquefaction
2.3 Hydrothermal Liquefaction Technology
2.3.1 Effects of Process Parameters
2.3.1.1 HTL Reaction Temperature
2.3.1.2 Initial Reaction Pressure
2.3.1.3 Reaction Time
2.3.1.4 Biomass to Solvent Ratio
2.3.1.5 HTL Catalyst
2.3.2 HTL-Derived Crude Properties
2.4 Biocrude Upgradation Technologies
2.5 Artificial Intelligence in Biofuel Technology
2.6 Application of AI in Biomass Characteristics
2.7 Applications of AI in Biomass to Biofuel Conversion
2.8 Conclusions
References
3. Biofuels as Energy for Tomorrow
Ananya Trivedi and Saurabh Joshi
3.1 Introduction
3.2 Reliability, Efficiency, and Renewability of Energy Sector
3.3 Addressing Environmental Issue and Energy Demand
3.4 Biofuel for Tomorrow
3.4.1 Classification of Biofuels
3.4.1.1 Feedstock Source
3.4.1.2 Methods of Biofuel Production
3.4.2 Production of Specific Biofuels
3.4.2.1 Fermentation for Ethanol Production
3.4.2.2 Transesterification for Biodiesel Production
3.4.2.3 Hydroprocessing for Renewable Diesel Production
3.4.2.4 Pyrolysis for Bio-Oil Production
3.4.2.5 Algae Cultivation for Algal Biofuels
3.4.2.6 Microwave-Assisted Biodiesel Production
3.4.2.7 Ultrasonic Assisted Biofuel Production
3.4.2.8 Catalytic Hydrodeoxygenation of Vegetable Oils to Green Diesel
3.4.2.9 Biological Methanation
3.4.2.10 Thermochemical Methanation
3.4.2.11 Synthetic Natural Gas (SNG) Production
3.4.3 Potential Biofuels for Tomorrow and Challenges
3.5 Advantages and Challenges of Biofuels
3.6 Conclusion
References
4. Enhancement in Productivity of Biofuels by Artificial Intelligence
Rajesh Singh Gurjar, Alisha Kakkar and Sudesh Kumar
4.1 Introduction
4.2 Method of Energy Generation
4.2.1 Bioconversion Technique
4.2.1.1 AI in the Application of Bioconversion Technology for Energy Generation
4.2.2 Fermentation
4.2.2.1 AI-Enhanced Fermentation Technology in Energy Generation
4.2.3 Thermochemical Conversion Technology
4.2.3.1 Artificial Intelligence in Thermochemical Technique in Energy Generation
4.2.4 Gasification
4.2.4.1 AI in Gasification for Bioenergy Production
4.2.5 Hydrothermal Liquefaction
4.2.5.1 AI in Hydrothermal Liquefaction for Bioenergy Production
4.3 AI Methods for Bioenergy Supply Chain Management
4.4 Methods for Economic and Environmental Assessment of Bioenergy Generation
4.5 Future Recommendations
4.6 Conclusion
References
5. Production of Bioethanol Based on Artificial Intelligence (AI)
Ram Bhajan Sahu and Priyanka Singh
5.1 Introduction
5.2 Bioenergy System
5.3 Artificial Intelligence
5.3.1 Bioethanol Production
5.3.2 Application of AI Tools in Production of Bioethanol
5.3.3 Application of AI Model for Optimizing Process of Pretreatment and Hydrolysis
5.3.4 Application of AI Model in Fermentation Process
5.4 Conclusions
References
6. Production of Biobutanol Based on Artificial Intelligence (AI)
Ram Bhajan Sahu, Anurag Sharma, Aditi Singh and Priyanka Singh
6.1 Introduction
6.2 Biobutanol Production via Microbial Fermentation
6.2.1 Metabolism of Clostridium sp. During ABE Fermentation
6.2.2 Raw Materials for ABE Fermentation
6.2.3 Starch-Based Raw Material
6.2.4 Lignocellulose Raw Material
6.2.5 Molasses
6.2.6 Agricultural Residues
6.2.7 Food Waste
6.2.8 Cheese-Whey and High Sugar Content Beverages (HSCBs)
6.3 Microalgae as Feedstock
6.3.1 Selection of Microalgal Strain
6.3.2 Cultivation
6.3.3 Harvesting and Dewatering of Algal Biomass
6.3.4 Biobutanol Production via ABE Fermentation with Microalgae as Substrate
6.4 Butanol Recovery and Isolation
6.4.1 Adsorption
6.4.2 Gas Stripping
6.4.3 Pervaporation
6.4.4 Liquid–Liquid Extraction
6.5 Genetic and Pathway Modifications to Improve Solvent Tolerance and Reduce Sporulation
6.6 Genetic Approach to Improve Production of Biobutanol by Microalgae
6.6.1 Expression of Exogenous Genes and Elimination of Competitive Pathway in Biobutanol Production
6.6.2 Integrated Approaches for Improved Butanol Tolerance in Microalgae
6.6.3 Improving Carbohydrate Content in Microalgae
6.7 Artificial Intelligence
6.8 ANN Model
6.9 RF Model
6.10 Bioethanol Production
6.10.1 AI Applications in Bioethanol Production Cycle
6.10.2 Role of AI in Pretreatment and Hydrolysis
6.10.3 AI Applications in Fermentation
References
7. How Artificial Intelligence Affect the Role of Manpower in Biofuels Industry
Rajesh Singh Gurjar and Sudesh Kumar
7.1 Introduction
7.2 AI in Biofuel
7.2.1 Management of Energy
7.2.2 Management of Water
7.2.3 Management of Technology
7.2.4 Management of Soil
7.2.5 Management of Human Resources
7.2.6 Management of Raw Materials
7.2.7 Management of Relationships
7.3 Role of AI in SDGs
7.4 Role of AI in Human Resource
7.5 Conclusions
References
8. Major Engineering Issues in Conventional Biofuel Technologies
Akansha Pandey, Depak Kumar and Sandeep Kumar Patel
8.1 Introduction
8.2 Feedstock for Biofuels
8.2.1 Agricultural Challenges
8.2.2 Feedstock Variability
8.3 Conversion Technologies
8.3.1 Thermochemical Conversion Process
8.3.2 Biochemical Conversion Process
8.4 Economic Strategies
8.4.1 Socioeconomic Impacts of Biofuel Production
8.4.2 Technoeconomic Analysis
8.4.3 Policy and Regulatory Developments
8.4.4 Challenges and Opportunities
8.5 Conclusion
References
9. Life Cycle Assessment of Biofuels Industry
Manju Choudhary, Ruchi Goyal, Arvind Kumar and Sunishtha Mishra
9.1 Introduction
9.2 Biofuel
9.2.1 Solid Biofuels
9.2.2 Liquid Biofuels
9.2.3 Gaseous Biofuels
9.2.4 Classification of Biofuels on the Basis of Feedstock
9.3 Life Cycle Assessment Biofuel Are Used Maximum
9.3.1 Liquefaction
9.3.2 Enzymatic Hydrolysis
9.3.3 Anaerobic Digestion
9.3.4 Fermentation
9.4 Biomass Supply
9.4.1 Wastes and Leftovers From Biomass
9.4.2 Biomass from Specialized Energy Crops
9.5 The Effect of Biomass Energy on Carbon Store and Its CO2 Neutrality
9.6 GHG Emissions Other Than CO2 in Bioenergy Systems
9.6.1 N2O Emissions
9.6.2 CH4 Emission
9.7 Methods of Environmental Evaluation for Biofuels
9.7.1 Management of Greenhouse Gases (GHGs) and Carbon Footprint (CF)
9.7.2 Ecological Footprint (EF)
9.7.3 Energy Assessments
9.7.4 Analysis of the Fuel Cycle
9.7.5 Life Cycle Assessment
9.8 Conclusion
References
10. Regulation and Government Policy for Artificial Intelligent–Based Industry
Manya Sharma
10.1 Understanding AI and the Need for Regulation
10.2 The Imperative for Regulatory Frameworks and Government Policies in AI
10.2.1 The Evolution and Regulation of AI Technology
10.3 Essential Role of Regulation in AI Development
10.4 Categories of AI Rules and Regulation
10.5 Diverse Approaches to AI Policy: Countries Charting Their Unique Courses
10.5.1 Government- Led Regulation
10.5.2 Market-Driven
10.5.3 Hybrid Perspective
10.6 Current Regulatory Landscape
10.6.1 EU Regulation and Policies on AI: Addressing Market Interdependence and Global Competition
10.6.2 Comprehensive Overview of US Policies and Regulation of AI for Industry
10.6.3 Strategic Policy Framework for AI Development in China
10.6.4 The Regulation and Governance of Artificial Intelligence in the United Kingdom
10.6.5 Navigating the Future: South Korea’s Strategic AI Governance and Innovation Policies
10.7 Safeguarding the Digital Frontier: Data Protection and Privacy in the Age of Big Data and IoT
10.8 Navigating the Ethical Maze of Artificial Intelligence
10.8.1 The Crucial Role of Transparency in Ethical AI Practices
10.8.2 Ensuring Harm-Free AI: The Principle of Non-Maleficence
10.8.3 Empowering Autonomy: Upholding Freedom in AI Ethics
10.8.4 Privacy in Ethical AI: Value, Right, and Approaches
10.8.5 Sustainability in AI: Goals and Approaches
10.8.6 Justice in AI Ethics
10.9 Greening AI: Mitigating the Environmental Impact of Artificial Intelligence
10.10 Navigating the Complexities of AI Regulation
10.11 Future Trends and Directions of AI
10.12 Final Reflections on AI Regulation and Policy
References
11. Cost Analysis of Artificial Intelligent-Based Biofuels Industry
Rashmi Priya and Preeti Yadav
11.1 Introduction
11.2 The Importance of Energy in Modern Society
11.3 Arrival of AI in Biofuel Production
11.4 Optimizing Feedstock Selection and Supply Chain Management
11.4.1 Enhancing Production Efficiency
11.4.2 Predictive Maintenance
11.4.3 Cost Components in Biofuel Production
11.4.4 Feedstock Costs
11.4.5 Production Costs
11.4.6 Capital Costs
11.4.7 Operating and Maintenance Costs
11.4.8 Environmental and Regulatory Costs
11.5 AI-Driven Cost Reduction Strategies
11.5.1 Feedstock Optimization
11.5.2 Process Optimization
11.5.3 Predictive Maintenance
11.5.4 Supply Chain Management
11.6 Competition and Demand
11.6.1 Profitability
11.6.2 Biofuel Production in Taiwan
11.6.3 Sustainability and Life Cycle Costing (LCC)
11.6.4 Life Cycle Costing (LCC)
11.6.5 Economic Life Cycle Costing (eLCC)
11.6.6 Life Cycle Assessment (LCA)
11.6.7 Addressing Uncertainties for Biofuels
11.6.8 Uncertainties in LCC
11.6.9 Design of Experiments (DOE)
11.6.10 Ensuring Robust Decision Making
11.7 Use of AI in Biofuel Production
11.7.1 Optimization Models
11.7.2 Dynamic Optimization
11.7.3 Optimization through AI
11.8 Applications of AI in Biofuel Production
11.8.1 Modeling and Optimization
11.8.2 Predictive Analytics
11.8.3 Supply Chain Optimization
11.8.4 Cost Analysis and Optimization
11.8.5 Factors Influencing Biofuel Production Costs
11.8.6 Technoeconomic Analysis (TEA)
11.9 Case Study on AI-Driven Optimization of Bioenergy Generation Parameters
11.10 Monthly Biofuel Production
11.11 Challenges and Opportunities for Biofuels
11.11.1 Resource Competition and Environmental Concerns
11.11.2 Interconnected Markets
11.11.3 The Need for Sustainable Energy Solutions
11.12 Sustainability and Future Directions
11.12.1 Sustainable Bioenergy Production
11.12.2 AI in Sustainable Bioenergy
11.13 Conclusion
References
12. Major Industry in India as Sources for Biofuels Production
Ankita Kumari, Depak Kumar, Priyanka Sati and Sudesh Kumar
12.1 Introduction
12.2 Developments in the Worldwide Recovery of Energy from Biomass Resources
12.3 Availability of Biomass for the Manufacture of Biofuels
12.3.1 Crop Residue Composition and Other Biomass Wastes
12.3.2 Microalgae Biomass
12.4 Advancements in Developing Methods to Improve the Generation of Biofuels
12.4.1 Biomass is Mechanically Converted to Biofuels
12.4.2 Thermochemical Conversion of Biomass to Renewable Energy
12.4.3 Biomass is Converted Biochemically into Biofuels
12.5 Recent Developments in the Genetic Engineering Area of Biofuels
12.6 Opportunities and Challenges in the Development of Biofuels
12.6.1 Considerable Technical Challenges in the Production of Biofuels
12.6.2 Environmental Issues in the Development of Biofuels
12.6.2.1 GHG Emissions with the Development of Biofuels Difficulties
12.6.2.2 Production of Biofuel and Issues with Land Usage
12.6.3 Socioeconomic Issues
12.7 Conclusions and Future Perspectives
References
13. Societal Impact of Biofuels Industry
Ankita Kumari, Depak Kumar and Sudesh Kumar
13.1 Introduction
13.2 What are Social Impacts
13.3 The Social Effects of Producing Liquid Biofuel in Wealthy Nations
13.4 Social Effects of Large-Scale Manufacturing of Liquid Biofuel in Poor Nations
13.4.1 Why Marginal Populations Suffer When “Marginal” Land is Targeted
13.4.2 Why Small Communities are Frequently Harmed in the Process of Major Enterprises Extracting Commodities
13.4.3 The Reasons Why Shifting to Cash Crops and Generating Paid Employment Might Not be Beneficial for Rural Areas
13.5 Discussion
13.5.1 Motivation for Production
13.5.2 Motivation for Consumption
13.5.3 Choice of Scale
13.6 Conclusions
References
About the Editors
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page