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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
This book 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.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Artificial Intelligence in Biofuel Applications
1.1 Introduction
1.2 AI in Feedstock Selection and Optimization
1.3 AI-Driven Process Optimization
1.4 AI in Biofuel Quality Control
1.5 AI for Sustainable Biofuel Production
1.6 Biofuel Supply Chain Optimization
1.7 AI in Biofuel Research and Development
1.8 Challenges and Future Directions
1.9 Conclusion
References
2 Artificial Intelligence in Biofuel Production
2.1 Introduction
2.2 Biomass to Biofuel Production
2.3 Hydrothermal Liquefaction Technology
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
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
3.5 Advantages and Challenges of Biofuels
3.6 Conclusion
References
4 Enhancement in Productivity of Biofuels by Artificial Intelligence
4.1 Introduction
4.2 Method of Energy Generation
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)
5.1 Introduction
5.2 Bioenergy System
5.3 Artificial Intelligence
5.4 Conclusions
References
6 Production of Biobutanol Based on Artificial Intelligence (AI)
6.1 Introduction
6.2 Biobutanol Production via Microbial Fermentation
6.3 Microalgae as Feedstock
6.4 Butanol Recovery and Isolation
6.5 Genetic and Pathway Modifications to Improve Solvent Tolerance and Reduce Sporulation
6.6 Genetic Approach to Improve Production of Biobutanol by Microalgae
6.7 Artificial Intelligence
6.8 ANN Model
6.9 RF Model
6.10 Bioethanol Production
References
7 How Artificial Intelligence Affect the Role of Manpower in Biofuels Industry
7.1 Introduction
7.2 AI in Biofuel
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
8.1 Introduction
8.2 Feedstock for Biofuels
8.3 Conversion Technologies
8.4 Economic Strategies
8.5 Conclusion
References
9 Life Cycle Assessment of Biofuels Industry
9.1 Introduction
9.2 Biofuel
9.3 Life Cycle Assessment
9.4 Biomass Supply
9.5 The Effect of Biomass Energy on Carbon Store and Its CO
2
Neutrality
9.6 GHG Emissions Other Than CO
2
in Bioenergy Systems
9.7 Methods of Environmental Evaluation for Biofuels
9.8 Conclusion
References
10 Regulation and Government Policy for Artificial Intelligent–Based Industry
10.1 Understanding AI and the Need for Regulation
10.2 The Imperative for Regulatory Frameworks and Government Policies in AI
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
10.6 Current Regulatory Landscape
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
10.9 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
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
11.5 AI-Driven Cost Reduction Strategies
11.6 Competition and Demand
11.7 Use of AI in Biofuel Production
11.8 Applications of AI in Biofuel Production
11.9 Case Study on AI-Driven Optimization of Bioenergy Generation Parameters
11.10 Monthly Biofuel Production
11.11 Challenges and Opportunities for Biofuels
11.12 Sustainability and Future Directions
11.13 Conclusion
References
12 Major Industry in India as Sources for Biofuels Production
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
12.4 Advancements in Developing Methods to Improve the Generation of Biofuels
12.5 Recent Developments in the Genetic Engineering Area of Biofuels
12.6 Opportunities and Challenges in the Development of Biofuels
12.7 Conclusions and Future Perspectives
References
13 Societal Impact of Biofuels Industry
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
13.5 Discussion
13.6 Conclusions
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 AI technologies applied in biofuel production processes over the las...
Chapter 2
Table 2.1 Comparison summary of pyrolysis and hydrothermal liquefaction techno...
Table 2.2 Comparative study of bio-oil/biocrude upgrading methods.
Chapter 3
Table 3.1 Fossil fuels origin and application.
Table 3.2 Fossil energy consumption.
Table 3.3 Biofuel origin and application.
Chapter 4
Table 4.1 Artificial intelligence in biochemical conversion for bioenergy prod...
Table 4.2 AI-enhanced production of biofuel through optimizing the factors inf...
Table 4.3 AI-enhanced thermochemical conversion of biomass into fuel productio...
Chapter 5
Table 5.1 Advantages and limitations of different types of machine learning te...
Table 5.2 Performance analysis of various AI tools used in the production of b...
Chapter 6
Table 6.1 Various AI applications in bioethanol production.
Chapter 7
Table 7.1 Findings of different research article on the basis of title.
Table 7.2 Impacts and opportunities that AI brings to HR [66–68].
Chapter 8
Table 8.1 Lignocellulosic content is present in different biomass feedstocks. ...
Table 8.2 Comparison of biofuel production from various thermochemical convers...
Chapter 12
Table 12.1 GHG reduction from the usage of different biofuels.
List of Illustrations
Chapter 2
Figure 2.1 Graphical depiction of the HTL process for biocrude production [29]...
Figure 2.2 Physical and chemical methods for biocrude upgradation [42].
Figure 2.3 Artificial Intelligence (AI) and major techniques under this tool [...
Figure 2.4 Comparison of human and artificial intelligence for any bioenergy s...
Chapter 4
Figure 4.1 Rise in utilization of fossil fuels from 1965 to 2021 (“Our World I...
Figure 4.2 Various biomass conversion technologies.
Figure 4.3 Diagram illustrating the biomass conversion into biofuels through b...
Chapter 5
Figure 5.1 Major components and conversion pathways of bioenergy systems for p...
Figure 5.2 Different learning approaches for AI tool for production of bioetha...
Figure 5.3 MLP architecture for enzyme hydrolysis and microbial fermentation p...
Figure 5.4 Analysis of experimental data of ANN model in terms of training, te...
Chapter 6
Figure 6.1 Metabolic pathway for production of biobutanol by
Clostridium aceto
...
Figure 6.2 Flowchart for different section of industrial plant for production ...
Figure 6.3 Different strategies for
in situ
separation of fermentative product...
Figure 6.4 Stages for pervaporation process for the recovery of biobutanol fro...
Figure 6.5 Schematic diagram for carbohydrate metabolism in microalgae using d...
Figure 6.6 AI categories and major techniques are using of production of bioet...
Chapter 7
Figure 7.1 Output of different research [35–37].
Figure 7.2 AI for sustainable utilization in the biofuel industry.
Figure 7.3 Interrelation of AI with different parts of ecosystem.
Chapter 8
Figure 8.1 Process of biomass feedstock varied to produce biofuels (modified f...
Chapter 10
Figure 10.1 Sequential development of AI.
Figure 10.2 Several regulations governing AI development.
Figure 10.3 Ethical standards and bias mitigations.
Chapter 12
Figure 12.1 A visual representation of the development of biofuel production....
Figure 12.2 Triglycerides are converted through the transesterification proces...
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
About the Editors
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Optimizing Biofuel Production with Artificial Intelligence
Edited by
Arindam Kuila
and
Depak Kumar
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781394301218
Front cover images supplied by Pixabay.comCover design by Russell Richardson
Preface
This book uses artificial intelligence (AI) as a technique to discuss the production of biofuels. Recent decades have seen a tremendous development 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. Our need to transition to new energy is a result of finite resources and economic sustainability. To ensure the quality of the final product and boost bioconversion performance efficiency, biotechnological process optimization is crucial. When combined with traditional simulation and modeling methods, artificial intelligence and computer technology can help define the ideal process parameters and save total process costs. The main advantage of biomass over fossil fuels is that it is not exhaustible. Since there are many plants on Earth, biomass has the potential to become a major renewable energy source and a long-term replacement for fossil fuels. The new frontier for energy in the future is biomass. Biomass is produced by as many industries in India as outside. Maybe that will fuel the next generation. The energy business can benefit from the concept of 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. Biofuel productivity can be increased in this way. It also divided the bioenergy systems into three phases: (i) biomass feedstock detection; (ii) bioenergy production/process; and (iii) energy utilization. These were paired with instances of AI applications. This book covers all the aspect of artificial intelligence for biofuel production. This book has 13 chapters, which provide an overview of current status of biofuel production from lignocellulosic biomass and all the aspect of artificial intelligence for biofuel production.
The book will be useful for students, researchers in the areas of various branches such as – artificial intelligence, environmental biotechnology, bioprocess engineering, renewable energy, chemical engineering, nanotechnology, biotechnology, microbiology etc.
We are grateful to Scrivener Publishing’s Phil Carmical for his complete cooperation and assistance in the timely publishing of this book. We would like to express our gratitude to the writers and the publication staff for their efforts for publishing this book.
Dr. Arindam Kuila
Dr. Depak Kumar
1Artificial Intelligence in Biofuel Applications
Neha Jain1, Anuj Rohatgi2, Jain Suransh2 and Depak Kumar3*
1Department of Chemical Engineering, Indian Institute of Technology, Roorkee, India
2Department of Biotechnology and Bioengineering, Institute of Advanced Research, Gandhinagar, Gujarat, India
3Department of Chemical Engineering, Banasthali Vidyapith, Newai, Tonk, Rajasthan, India
Abstract
As the world grapples with the challenges of climate change, energy security, and the need for sustainable solutions, biofuels are emerging as a promising alternative to fossil fuels. This chapter delves into how artificial intelligence (AI) is playing a key role in transforming the biofuel industry. From selecting the best crops for fuel production to fine-tuning the processes that turn biomass into energy, AI is making biofuel production more efficient, cost-effective, and sustainable. AI-driven systems can optimize every stage—helping farmers grow better feedstocks, improving production methods, ensuring fuel quality, and even reducing environmental impacts through smart assessments. Real-world examples show how AI is already increasing biofuel yields and reducing waste while tackling challenges like limited data and the complexity of scaling these technologies for widespread use. Despite these hurdles, AI’s potential to revolutionize biofuel production is clear, and its future looks even more promising. By combining biofuels with AI technologies, we can move more quickly toward a cleaner, greener energy future, where renewable fuels are critical in addressing global environmental and energy concerns.
Keywords: Biofuels, artificial intelligence, sustainable energy, feedstock optimization, process control, life cycle assessment
1.1 Introduction
The contending factors that are making traditional energy models unviable are climate change, energy security and sustainable development. Under these circumstances, biofuels have become classified as one of the prospective sources of the energy that could replace fossil fuels, and which can potentially be characterized as being more friendly to the environment. At the same time, there is a considerable development of artificial intelligence (AI) technologies, which influence different fields among which is the energy field. This chapter focuses on the discussion of the conjunction between these two advanced areas, with the subject on how AIs are revolutionizing biofuel efforts. Biofuels refer to liquid, gas or biogas obtained because of transformation of biomass or biological materials including plants and animal products (Russell & Norvig, 2016). They can also be classified into first-generation biofuels which comes from food crops; second-generation biofuels that come from non-food biomass and the third-generation biofuels such as algal biofuels (Naik et al., 2010). The significance of biofuels is thereby hinged on the ability of these latter to help mitigate greenhouse gas emissions, improve energy security and spur countryside revenue generation from the growth of new agricultural outlets (Koh & Ghazoul, 2008).
On the other hand, artificial intelligence can be defined as the creation of more advanced computer systems which possess the competency to complete activities that a human being can perform (LeCun et al., 2015). AI consists of many categories such as but not limited to machine learning, deep learning, natural language processing and computer vision. These technologies allow different systems to analyze the data and perform decisions, as well as to alter their operation without direct programming (LeCun et al., 2015). AI application is playing diverse and now deeply influential roles in the areas of biofuel research and development. AI has been adopted in all aspects of biofuel value system including feedstock sourcing and growing, production, quality assurance, and logistics. In turn, using data analyses, prognosis, and methods of optimization, AI is addressing many concerns connected with biofuel production and utilization (Yue & You, 2013).
Some key areas where AI is making significant contributions include:
Feedstock optimization: AI is being employed in the methods of identifying the appropriate species of crops, the expected individual yields, and environmental settings for the feedstock for biofuels (Liakos
et al.
,
2018
).
Process control and efficiency: They reduce the costs and increase the conversion rates of biofuel production from biomass as well as other raw materials in different reaction conditions (Aghbashlo
et al.
,
2021
).
Quality assurance: Other methods that are making quality assessment of biofuel faster and accurate include the use of computer vision and spectroscopic methods together with the artificial intelligence technology (Pérez Romero
et al.
,
2022
).
Sustainability assessment: By implementing the life cycle assessments through the use of artificial intelligence, biofuel production is kept both environmentally sustainable and economically viable (You
et al.
,
2012
).
Supply chain management: AI is enhancing biofuel distribution networks and refining the prediction of the demand (Awudu & Zhang,
2012a
).
Research and development: More advanced techniques such as high–throughput screening with AI support are improving the identification of new microbial species as well as new biofuel molecules (Carbonell
et al.
,
2018
).
Let us now look at each of these applications one by one, as we proceed further into this chapter to describe how AI is disrupting the biofuel landscape for a cleaner energy transition. Thus, researchers and industry professionals apply artificial intelligence to biofuel production and utilization, which once was too complicated, getting the potential to bring the society to low carbon economy faster.
1.1.1 Brief Overview of Biofuels and Their Importance
Biofuels are a type of renewable energy generated from organic substances with possible applicability as conventional fuels. They can be broadly categorized into:
First-generation biofuels: They are manufactured from food commodities like corn, sugarcane, and vegetable oils (Demirbas,
2009
).
Second-generation biofuels: Obtained from non-food biomass such as agricultural residues, woody crops and Municipal solid waste (Naik
et al.
,
2010
).
Third-generation biofuels: Mainly produced from the growth of algae and occasional other microorganisms (Koh & Ghazoul,
2008
).
Biofuels have significant role in energy scenario of the world. They offer several key advantages:
Reduced greenhouse gas emissions: Biofuels may reduce carbon dioxide emissions than fossil fuels because the carbon largely emitted during burning is taken from the atmosphere during the growth of crops (Russell & Norvig,
2016
).
Enhanced energy security: Since biofuels bring about diversification of energy sources and decrease of the fossil fuel importation dependence, the biofuels enhance the national energy security (LeCun
et al.
,
2015
).
Rural development: Thus, the biofuel industry opens new markets for agriculturally produced products and a new dynamism to rural economies (Yue & You,
2013
).
Waste reduction: A few biofuels employ waste products which then help towards the solutions of waste (Liakos
et al.
,
2018
).
Biodegradability: Some biofuels are less hazardous to the environment than the fossil fuels are in case of spillage and leakage (Aghbashlo
et al.
,
2021
).
However, as with other newer products, there are some issues with the production and use of biofuel such as food issue, changes in land use and the efficiency of the biofuel production. It is important for biofuels to increase its utilization rates and become self-sustaining; that is why these challenges must be addressed.
1.1.2 The Role of AI in Advancing Biofuel Technology
Biofuels are proving to be an important focus in the contemporary management of energy challenges and artificial intelligence is assuming an ever more significant position in facing the challenges and seizing the opportunities. AI’s contributions span the entire biofuel value chain:
Feedstock optimization: Currently, machine learning is being used to forecast crop yields, environment suitable for cultivation and appropriate plant breeds for biofuels (You
et al.
,
2012
).
Process enhancement: Process control systems which have been enhanced through the integration of AI are helping in enhancing the conversion rates and the reduction of energy utilization in plants that deal with biofuels (Awudu & Zhang,
2012a
).
Quality control: Computer vision and machine learning are making it possible to make the quality assessment of biofuel faster and accurate to conform to the set quality standards (Carbonell
et al.
,
2018
).
Sustainability assessment: AI has enabled the production of the more holistic of life cycle impact assessments which serves to protect the environment as well as the economy in the net production biofuels (Baral
et al.
,
2019
).
Supply chain optimization: Increased application of AI is observed in the biofuel supply chain by improving demand forecasting models, inventory control, and distribution (Esmaeili
et al.
,
2020
).
Research and development: AI is assisting in identifying new microbial strains for biofuel production and even in the conceptualization of new biofuel molecules (Wehrs
et al.
,
2019
).
Predictive maintenance: These application of artificial intelligence and analytics has made it easier to have predictive maintenance hence reducing downtime in the production of biofuel production plants (Bertolini
et al.
,
2021
; Jiao
et al.
,
2020
; Kang
et al.
,
2020
).
1.2 AI in Feedstock Selection and Optimization
Selection of feedstock and optimizing them for conversion to biofuels is one of the most crucial activities in biofuel conversion chains, a determining factor for feasibility, sustainability, and cost-effectiveness of biofuel systems. Recently, a lot of focus has been placed on feedstock management, and the management of it as well, and AI has become a most effective tool for solving the present difficulties from various points of view which cannot be overpassed using traditional approaches (Aghbashlo et al., 2021). Machine learning techniques and the genetic algorithm are the two most common AI technologies that are changing the way feedstocks are chosen and improved for biofuel production. Such sophisticated computational methods can handle large volumes of patterned information such as climate conditions, genotypes, and past results for a proper decision and prognosis (Liakos et al., 2018).
AI is widely used in this area regarding feedstock selection, optimal crops for certain regions, as well as the yields’ forecast. AI processing can take into account at the same time that region’s climate, the soil type, and the economics of that area and apply those considerations to the selection and growth of crops (Chlingaryan et al., 2018). More so, AI is helping in devising new enhanced biofuel crops; this is by mimicking thousands of generations of crop breeding in a short time like what would take years for normal breeding. This capability lets researchers determine what set of genes provides the highest levels of biofuel production while not compromising with the crop’s ability to withstand these conditions (Eftekhari et al., 2024; Khan et al., 2022). The developments in AI in feedstock selection and in optimization are enhancing efficient and sustainable biofuel systems that also have a lower cost. Using such technologies the biofuel industry stands in a better place to meet the increasing global demand for renewable energy sources without adverse effects on the environment (K. Ahmad & Ming, 2024; Duarah et al., 2022). Thus it is anticipated that AI applications on feedstock selection and optimization will receive increasing attention as the technology unfolds further. Future developments in this area also shows the potential to boost the production of biofuel while increasing its sustainability so as to support the energy change required towards a low carbon economy (Song et al., 2024; S. Wu et al., 2024).
1.2.1 Machine Learning for Crop Selection and Yield Prediction
With help of the Machine learning (ML) crop selection and yield prediction for feedstocks has got far more efficient with the help of complex datasets and their patterns which are not visible easily in a normal approach.
1.2.1.1 Crop Selection
The features of landscape amenities and producer surplus have also been seen to benefit from the usage of ML algorithms, especially from Random Forests and Gradient Boosting Machines in considering the likelihood of biofuel crops across given regions. These models integrate various data sources, including reflectance data (temperature, precipitations, solar radiation); pH, texture, and nutrient status of soils; topographical features; historical crop performance; and economic indicators. A Random Forest model was integrated by (Shevchenko et al., 2024) to analyze suitability of different energy crops on the various geographical regions of China. It took into consideration 23 environmental variables and it has 92% efficiency in predicting suitable bioenergy crops for the five main bioenergy crops (Shevchenko et al., 2024). Yet, another creativity application utilized a deep learning model that incorporates both the CNNs and LSTM networks to identify statistical data satellite images, and climate patterns to estimate favorable switch grass growing zones. This model boasted of a slightly better performance than the standard statistical analysis with a 15% increase in predictive probability (Cacho et al., 2023; Momm et al., 2020).
1.2.1.2 Yield Prediction
A good forecast of the yield is essential in the coordination of production in the biofuels industry. Some of the advanced techniques of ML includes deep learning have enhanced the yields forecasts with greater precision. A recent groundbreaking work present a satellite imagery analysis of the terrestrial environment complemented with ground sensors by a deep hybrid CNN-Transformer model. Overall, the application of this method allowed for achieving high accuracy of corn yields’ predictions for ethanol production – 95%, with geographical differences being taken into consideration (Briones et al., 2024). Another innovative application uses the Graph Neural Network (GNN) in the interaction of different environmental factors and crop genetics of Miscanthus for yield prediction. The GNN approach described in the work showed 20% better results in terms of prediction accuracy compared to a regular regression (Šurić et al., 2023).
1.2.2 Genetic Algorithms for Optimizing Some Biofuel Crop Attributes
Genetic Algorithms (GAs) are among the most effective technologies which have been applied in the optimization of biofuel crop traits; thanks to their ability to explore large volumes of a genetic landscape swiftly to arrive at the most suitable blend.
1.2.2.1 Trait Optimization
GAs are being applied to optimize various traits crucial for biofuel production, including:
Biomass yield
Available lignin and cellulose content
Tolerance to water deficit and to pests
Fatty matter content of the feedstock (for biodiesel production feedstocks)
One of the recent studies applied a multi-objective GA for the development of multiple traits of switchgrass for biofuel production. The algorithm aimed for the maximization of biomass yield alongside minimizing for lignin content and at the same time for increasing drought tolerance. The GA-optimized varieties had an increase of 30% of the theoretical ethanol yields than the conventional varieties (Cacho et al., 2023).
1.2.2.2 Integration with Genomic Data
The combination of GAs with genomic information has given new directions as to how crop improvement can be done. A new method integrating GAs with GWAS data was later on designed to optimize Miscanthus traits. This method helped in identifying the genetic indicators with reference to biomass yield and bioconversion and as a result Miscanthus of higher biofuel potential by 25% was developed (Sandhu et al., 2022).
1.3 AI-Driven Process Optimization
Artificial intelligence (AI) is at the forefront of process improvement in the biofuel sector through its enormous processing, cost-saving, and quality promotion advantages. Thus, it could be noted that, biofuel production processes are complicated often involving several parameters and complex interactions and this is where AI techniques can be helpful to overcome this complicatedness and help in extracting the optimum performance (Okolie, 2024). Within the process of biofuel production, integration of AI involves application of the machine learning and deep learning models for pretreatment of the feedstock, fermentation process, distillation as well as purification. These high-level mathematical models can take large quantity of real time inputs, process them, and adapt quickly to the parameter settings to respond to the process observed (Saad & Murray, 2022).
AI and its role in Processes is not merely to operate at the control layer but is also present in other layers such as predict, detect, and learn layers. Hence, with the help of historical information and instant feed, AI systems can forecast process variations, avoid equipment breakdowns, and optimize the yield and quality of products on a permanently ongoing basis (Alagumalai et al., 2023). Moreover, AI-driven process optimization is playing a crucial role in addressing one of the key challenges in biofuel production: introduction of variability it feedstock composition. Through the real time monitoring of feedstock properties and the instant control of process parameters AI systems have provided the rather rigid system with a level of flexibility that permits the conversion of a broader range of feedstock materials (Y. Yang & Harper, 2024).
AI is being complemented by IoT and better sensors to improve the systems of process optimization. The integration of these two provides better ways of monitoring and controlling the production hence efficiency control of energy use, wastage, and overall sustainability of biofuels (Shelare et al., 2023). It is therefore anticipated that with increasing improvement of the AI technologies, their involvement in the enhancement of processes used in biofuel production will also increase. Some of the evolutions are reinforcement learning for self-sufficient process management, digital twins for virtual improvement, and federated learning for synchronized enhancement across several facilities (Petsagkourakis et al., 2020a). The usage of AI in the process optimization of biofuel production is not without difficulties, which can relate to data quality, the understanding of the results provided by the implemented model, or integration with existing systems. But the opportunities for improvement of efficiency, cost decrease, and environment preservation make the investments and innovations in this sphere persistent (T. Ahmad et al., 2021).
In addition, Table 1.1 illustrates various AI-driven process technologies that have been applied in biofuel production over the past decade, along with their specific applications and relevant references. This table provides a comprehensive overview of the major technological advancements in the field and their contributions to optimizing biofuel production processes. These technologies, as outlined in Table 1.1, showcase how AI applications have been utilized across various stages of biofuel production to improve efficiency, sustainability, and scalability. Each technology targets specific challenges such as process variability, resource optimization, and quality control, making them vital components in advancing the biofuel industry toward a cleaner energy future.
Table 1.1 AI technologies applied in biofuel production processes over the last decade.
Technology
Application in biofuel production
References
Machine Learning (ML)
Used for feedstock optimization by predicting the best crops and environmental conditions for biofuel production.
Liakos
et al
.,
2018
; Eftekhari
et al
.,
2024
Genetic Algorithms (GA)
Optimization of biofuel crop traits like yield, lignin content, and drought resistance.
Cacho
et al
.,
2023
; Sandhu
et al
.,
2022
Artificial Neural Networks (ANNs)
Modeling and optimization of fermentation processes, enhancing ethanol yields from lignocellulosic biomass.
Kumar
et al
.,
2016
; David
et al
.,
2023
Reinforcement Learning (RL)
Process control optimization for biodiesel and ethanol production, improving yields and reducing energy consumption.
Petsagkourakis
et al
.,
2020
; Wang
et al
.,
2024
Deep Learning (DL)
Real-time quality control using computer vision for assessing biomass and biofuel quality.
Zhang
et al
.,
2023
; Contreras-Medina
et al
.,
2024
IoT with AI Integration
Real-time monitoring of biofuel production, optimizing water and energy use in largescale biodiesel plants.
Shelare
et al
.,
2023
; Duarah
et al
., 2024
Life Cycle Assessment (LCA) with AI
Dynamic sustainability assessments of biofuel production, accounting for environmental impacts from feedstock to biofuel use.
Algren
et al
.,
2021
; Losada
et al
.,
2024
Ensemble Machine Learning
Predicting bioethanol yields based on biomass composition and process conditions, improving process efficiency.
Konishi,
2020
; Briones
et al
.,
2024
Computer Vision + Spectroscopy
Non-destructive real-time quality assessment of biofuels using image and spectroscopic data for contaminant detection.
Liang
et al
.,
2024
; Rao
et al
.,
2023
Digital Twins + AI
Virtual optimization of biofuel production processes to reduce energy use and emissions in biorefineries.
Demirel & Rosen,
2023
; Gurawalia
et al
.,
2024
1.3.1 Neural Networks for Biofuel Production Process Control
Neural networks, particularly deep learning models, are very powerful tools for process control and optimization of biofuel production processes. The methods allow handling of complex and nonlinear relationships and adaptation to changes, making them quite well-suited for the dynamism of biofuel production. Recurrent Neural Networks, more specifically Long Short-Term Memory networks, have enjoyed immense success in process control problems by capturing temporal dependencies in sequential data. A very recent work by Liu et al. (2024) applied an LSTM-based control system in a continuous ethanol fermentation process. The system processed real-time data from multiple sensors: temperature, pH, and substrate concentration, against which the process parameters were dynamically adjusted. This was an AI-driven approach that increased ethanol yield by 15% and decreased process variability by 20% in comparison to a traditional PID controller (Mondal et al., 2023). Applications of CNN in visual data analysis in biofuel production make it possible to implement real-time control based on image analysis. Zhang et al. (2023) presented a CNN-based system that monitored algal growth in photobioreactors for biodiesel production. It estimated images of microscopic algal cultures in real time, showing changes in biomass concentration and lipid content with a view to optimizing nutrient addition and harvesting times. Application of this approach will increase its lipid productivity by 25% (Contreras-Medina et al., 2024). Especially algorithms from reinforcement learning and deep Q-Networks are being researched for adaptive process control in the production of biofuels. In this pioneering study, (Wang et al.2024) designed a DQN-based control system for multi-stage biodiesel production. The RL agent learned to adjust reaction conditions of temperature, catalyst concentration, and mixing speed under different feedstocks. After training, the system demonstrated a 10% increase in biodiesel yield and a 30% reduction in energy consumption compared to the best expert-designed control strategies (Petsagkourakis et al., 2020a).
1.3.2 Predictive Modeling for Conversion Efficiency Improvement
Predictive modeling using AI techniques is playing a critical role in enhancing the conversion efficiency of biofuel production processes. Ensemble methods, as including many machine learning models, have been outstanding in the performance of predicting biofuel yields under varying conditions. A recent work demonstrated the application of a stacked ensemble model in which Random Forests, Gradient Boosting Machines, and Neural Networks were combined to predict the bioethanol yield from lignocellulosic biomass. The approach developed an R² of 0.95 in ethanol yield prediction with 25 input variables, including biomass composition, pretreatment conditions, and enzyme loadings; this is better than any single constituent model (Konishi, 2020). GPR is used not only for predicting process outcomes but also for quantifying the uncertainty in these predictions. A recent study applied GPR to model the transesterification step in biodiesel production (Gülșen, 2012). Their model predicted biodiesel yield and quality parameters, providing confidence intervals for these predictions. This approach enabled risk-aware optimization, where process conditions could be adjusted based on both the predicted outcomes and the associated uncertainties.
1.4 AI in Biofuel Quality Control
Another important part of this production process is the quality control of the final product, meeting the regulatory standards and customer specifications. Artificial intelligence has been offering fast, accurate, cost-effective methods for assessing and maintaining biofuel quality all along the line of production (Okolie, 2024). AI technologies, including machine learning and computer vision, are being increasingly used in the analysis of complex data from a wide range of analytical instruments—spectroscopy, chromatography, and imaging systems. These AI-driven approaches further allow realization of real-time monitoring of key quality parameters, contaminant detection, and property prediction for fuels with an accuracy and speed as never before (Mourched et al., 2024).
Such key benefits of AI in the field of biofuel quality control include management of the natural variability of feedstocks and production processes. AI models learn to adapt from patterns within the large datasets, to changes in the input material and process conditions, thereby assuring reliable quality even under fluctuating circumstances (Y. Yang & Harper, 2024). More than that, AI is opening new opportunities for conventional analytical techniques. For instance, the application of machine learning combined with spectroscopic methods has developed a rapid, non-destructive testing procedure that provides instant feed on biofuel quality, replacing the laboratory test (Fadiji et al., 2023). AI-powered sensors and IoT devices monitor the quality of biofuels during transportation and storage, ensuring that the product will not lose any of its properties throughout the supply chain (Shelare et al., 2023). With the increasing emphasis on more stringent regulations concerning the quality of biofuels, AI assumes a very important role in guaranteeing compliance. Advanced predictive models can project how variations in different production parameters will impact the final properties of a product, thereby enabling manufacturers to take corrective action and adapt to evolving standards accordingly (Corral-Bobadilla et al., 2024, C.-T. Yang et al., 2023).
1.4.1 Computer Vision and Spectroscopy for Rapid Quality Assessment
The application of computer vision, in addition to the spectroscopic techniques, with AI, has brought a revolution in rapid quality assessment in biofuel production by offering real-time and non-destructive analysis methodologies.
1.4.1.1 Computer Vision for Visual Quality Inspection
Computer vision systems are ensembled with deep learning algorithms for visual quality inspection related to impurity detection, color assessment, and identification of phase separation in biofuels. In a recent study, a real-time CNN-based visual inspection system for biodiesel was developed. It analyzed high-resolution images of biodiesel samples, detecting contaminants, and assessing the overall quality of the biodiesel sample. The hyperspectral technique, with an accuracy of 99.2%, hugely exceeds some traditional visual inspection methods, by 80% off inspection time (Akande et al., 2024).
1.4.1.2 Hyperspectral Imaging for Comprehensive Quality Analysis
Hyperspectral imaging, coupled with machine learning, is quickly emerging as a very powerful tool for the comprehensive assessment of quality and biofuel authentication. A hyperspectral imaging system integrated with a deep learning model is developed for simultaneous determination of multiple quality parameters of bioethanol. It enabled real-time prediction of ethanol and water content and the presence of key impurities in bioethanol. This approach allowed the prediction accuracy to meet that of standard laboratory methods while it has reduced the time of analysis from hours to seconds (Liang et al., 2024).
1.4.1.3 Raman Spectroscopic for Molecular-Level Analysis
AI-enabled Raman spectroscopy allows for the fast molecular analysis of biofuels at their molecular level. In a remarkable study, authors used SERS with a machine learning algorithm to achieve ultrahigh sensitivity for trace contaminant detection in biodiesel. The system was able to detect and quantify sulfur-containing species down to 0.1 ppm, hence the stricter state of quality. In this system, the AI feature allows for automatic peak identification by quantified peak readings to avoid the expert interpretation required (Rao et al., 2023).
1.4.2 Predicting Fuel Properties Using Machine Learning
Machine learning-based prediction of various biofuel properties is becoming the order of the day with a strong direction toward proactive quality control as well as process optimization.
1.4.2.1 Comprehensive Property Prediction Using Ensemble Methods
Methods of ensemble learning—those that combine many machine learning algorithms—have shown good performance in the prediction of wide biofuel properties. A further improvement that came was the design of a stacked ensemble model that directly combines Random Forests, Gradient Boosting Machines, and Neural Networks for multiple property prediction, such as cetane number, oxidation stability, cold filter plugging point, etc., of biodiesel. The model was designed and trained from a dataset of over 10,000 biodiesel samples and produced an average prediction accuracy of 95%, outperforming individual models and traditional regression techniques (Ge et al., 2023).
1.4.2.2 Deep Learning on Complex Property Relationships
Deep learning models have been used to capture the complex and nonlinear relationship between process parameters and fuel properties. The application of these methods is demonstrated in which a deep neural network was used to predict the octane number of bioethanol blends based on compositions of the feedstock and process parameters. The model could effectively predict the octane numbers of several ethanol-gasoline blends, thereby providing real-time optimization of the blending process in the presence of a specific octane requirement (Sun et al., 2024).
1.4.2.3 Transfer Learning for Improved Generalization
In this work, our major goal is to see how transfer learning techniques can be used in the development of models that are more generalizable and predictive in nature of properties across different types of biofuels. A methods was developed for effectively applying transfer learning techniques making a biodiesel property prediction model useful for a renewable diesel property prediction and really reduced sensitively the scale of data that is necessary in all aspects of developing an accurate model for new types of biofuels, therefore speeding up development in quality control systems of emerging biofuel technologies (Aghbashlo et al., 2021).
1.5 AI for Sustainable Biofuel Production
With the quest by the biofuel industry to have its sustainability credibility advanced, Artificial intelligence is fast emerging as one important tool for optimizing production processes while minimizing environmental impacts and improving overall efficiency. AI technologies are applied across the entire value chain of biofuel to address critical questions related to sustainability and unlocking new opportunities for eco-friendly fuel production (Okolie, 2024). Resource use optimization is another major application of AI in the production of sustainable biofuels. Machine learning algorithms are used in reducing water and energy use, setting low levels of waste production, and enhancing the efficiency of land use during feedstock cultivation. All these AI-based techniques help alleviate environmental footprint concerns associated with biofuel production (R. R. Kumar et al., 2024).
Moreover, AI has been playing a crucial role in biofuels’ life cycle assessment. Advanced modelling now enables a more precise and proper assessment of environmental impacts from feedstock cultivation to the end use of the biofuel products. Such AI-boosted LCA tools are really useful to provide valuable insights for policymakers and industry stakeholders to take decisions on sustainable development with respect to biofuels (Corral-Bobadilla et al., 2024). AI also contributes to developing more sustainable feedstocks. Model applications in machine learning are identifying and developing crop varieties that are not only high-yielding but also more resilient against climate change and requiring fewer inputs. This use of AI is also helping to allay fears of competing for food crops or land-use changes (Okolie, 2024).
Process optimization—next, how AI will enable more efficient and cleaner production processes. From predictive modeling, which reduces energy use and associated emissions, to real-time optimization algorithms that yield improvement in the biofuel refineries, these advancements provide critical improvements in the overall sustainability of biofuel production (Demirel & Rosen, 2023; Gurawalia et al., 2024). Another application area of AI is in aiding the integration of biofuel production with other sustainable technologies. For instance, machine learning algorithms are being used to optimize the coupling of production with carbon capture and utilization technologies, making carbon-negative fuel production systems possible (Al-Sakkari et al., 2024).
With growing regulatory requirements on the sustainability of biofuel production, AI is also playing a part in ensuring compliance and transparency. Research into blockchain technologies is being conducted in combination with AI for the potential to provide greater traceability within the biofuel supply chain and guarantee that sustainability criteria are maintained throughout its production process (Munir et al., 2022). Even though the use of AI has huge potential for the sustainable production of biofuels, there still exist many challenges at large, high-quality datasets, integration of AI systems with the underlying infrastructure, and assurance that AI-driven decisions remain aligned to broader sustainability goals (Xing et al., 2021). With AI technologies only continuing to further develop, so will their impact on sustainable biofuel production. Future developments may include more sophisticated AI-driven biorefinery designs and autonomous systems for the cultivation of sustainable feedstocks, together with advanced predictive models for long-term sustainability assessment (T. Ahmad et al., 2021).
1.5.1 Life Cycle Assessment Optimization Using AI
Life Cycle Assessment is one of the most important tools for estimating the environmental impact of biofuel production. The integration of artificial intelligence will transform LCA into a more accurate, speedy, comprehensive method.
1.5.1.1 Machine Learning for Data Gap Filling in LCA
One of the major challenges associated with LCA is how to handle incomplete or missing data. Machine learning algorithms are applied to fill this gap. In a recent study, the authors proposed a new method that applies Gaussian Process Regression for inventory data that is missing regarding biofuel LCAs. Having been trained on a large existing dataset of LCA inventories, it was capable of accurately predicting emissions and resource use for multiple biofuel production pathways. The authors reduced the time and monetary costs of full LCAs by up to 40% with this method (Algren et al., 2021).
1.5.1.2 Dynamic and Consequential LCA with AI
AI is enabling more dynamic and consequential LCAs, considering wider systemic implications of biofuel production. For example, one pioneering work has combined agent-based modeling with reinforcement learning in order to simulate long-term land use, food prices, and greenhouse gas emission outcomes for the purpose of large-scale biofuel adoption. This AI-driven modeling technique uncovers potential unintended consequences and feedback loops, which are admittedly often left on the table for static LCAs (Losada et al., 2024).
1.5.2 AI-Powered Resource Management and Waste Reduction
AI is playing a critical role in resource optimization and waste reduction in biofuel production, hence providing much significant contribution toward sustainable development.
1.5.2.1 Predictive Maintenance for Equipment Efficiency
AI-driven predictive maintenance now optimizes biofuel production and minimizes waste. More recently, a deep learning model was developed for the prediction of bioethanol plant equipment failure incidences based on sensor data. That system could forecast potential breakdowns up to two weeks in advance, enabling them to plan maintenance. This approach decreased unplanned downtime by 35% and increased general equipment efficiency by 12%, massively reducing energy waste and production losses (Saad & Murray, 2022).
1.5.2.2 AI for Water Management in Biofuel Production
Water management is one of the most important elements of sustainable biofuel production, and in this respect, AI is giving new solutions. Duarah et al. (2024) implemented a reinforcement learning algorithm in the largescale biodiesel production facility to optimize water use. Dynamic changes towards the recycling rates and treatment processes of water based on real-time information of water quality and production needs were being affected by the AI system. This method resulted in a 25% reduction in freshwater consumption and a 30% reduction in waste water generation when compared to traditional management strategies (Aghbashlo et al., 2021).
1.5.2.3 Machine Learning for Byproduct Valorization
AI is also being applied in reducing waste byproducts and increasing value in biofuel production. Kumar, et al., 2024, developed an ML approach to optimize the conversion of lignin, a major byproduct from cellulosic ethanol production, into chemicals. The AI system was used to analyze process conditions and characteristics of lignin in predicting the optimal conversion pathways. The approach enhanced the economic value derived from lignin byproducts by 40% and reduced the waste streams (Decker et al., 2023).
1.6 Biofuel Supply Chain Optimization
The biofuel supply chain is a complex network that spans from feedstock cultivation to end-user distribution, involving numerous stakeholders and intricate processes. As the biofuel industry continues to grow and evolve, optimizing this supply chain has become increasingly crucial for ensuring economic viability, environmental sustainability, and meeting the growing global demand for renewable energy sources. In recent years, artificial intelligence (AI) has emerged as a powerful tool for addressing the multifaceted challenges of biofuel supply chain optimization.
AI technologies, particularly machine learning and optimization algorithms, are revolutionizing various aspects of the biofuel supply chain. One of the key areas where AI is making significant impacts is in demand forecasting and inventory management. Accurate demand forecasting is crucial for balancing supply with demand, minimizing waste, and ensuring profitability in the biofuel industry. Traditional forecasting methods often struggle to capture the complex dynamics influencing biofuel demand, which can be affected by factors ranging from government policies and economic indicators to weather patterns and consumer behaviour.
Closely related to demand forecasting is the challenge of inventory management. Efficient inventory management is critical for minimizing costs, reducing waste, and ensuring a steady supply of biofuels to meet market demand. AI algorithms are being increasingly applied to optimize inventory levels across the biofuel supply chain. Krisnaningsih et al., (2024) developed a Decision Support System (DSS) using machine learning techniques to optimize raw material inventories in biodiesel production. The system helped in reducing inventory costs while maintaining production efficiency by considering multiple factors such as market demand, production capacity, storage constraints, and raw material perishability.
Another critical aspect of biofuel supply chain optimization is distribution route planning. Efficient distribution is paramount in the biofuel industry, where margins can be tight and environmental considerations are crucial. AI is playing an increasingly important role in optimizing distribution routes, reducing costs, and minimizing environmental impacts. Advanced AI algorithms are enabling more efficient route planning for biofuel distribution by considering multiple factors simultaneously, such as distance, fuel consumption, delivery time windows, and vehicle capacities.
Shen et al., (2020) developed a hybrid artificial intelligence optimization algorithm for vehicle routing problems, which could be applied to biofuel distribution. The algorithm showed superior performance in terms of solution quality and computational efficiency compared to traditional optimization methods. By efficiently routing distribution vehicles, such AI-driven systems can significantly reduce transportation costs, decrease fuel consumption, and lower greenhouse gas emissions associated with biofuel distribution.
Moreover, AI is facilitating more complex, multi-objective optimizations that balance economic and environmental considerations in biofuel distribution. A study by (Zhen et al., 2019) proposed a multi-objective mathematical model for a green closed-loop supply chain network design problem in the biofuel industry. The model, solved using meta-heuristic algorithms, considered both economic factors (such as production and transportation costs) and environmental factors (such as carbon emissions) in optimizing the supply chain. This holistic approach to supply chain optimization is crucial for ensuring the long-term sustainability of the biofuel industry.
While AI shows great promise in optimizing biofuel supply chains, several challenges remain. Data integration and quality is a significant hurdle, as AI systems require large amounts of high-quality, consistent data across the entire supply chain. Scalability is another challenge, as developing AI systems that can operate effectively across global supply networks with varying conditions and regulations is complex. Regulatory compliance is also a key consideration, as AI systems need to adapt to varying regulatory environments across different regions. Additionally, as supply chains become more digitized and AI-driven, cybersecurity becomes increasingly important to protect these systems from potential threats.
Looking ahead, future research in AI-driven biofuel supply chain optimization is likely to focus on developing more integrated, end-to-end AI systems that can manage and optimize the entire supply chain in real-time. There is also growing interest in exploring the use of blockchain technology in conjunction with AI for enhanced traceability and transparency across the supply chain. Furthermore, as the impacts of climate change become more pronounced, advancing AI techniques for more robust longterm planning in the face of environmental uncertainties will be crucial.
1.6.1 AI for Demand Forecasting and Inventory Management
Accurate demand forecasting and efficient inventory management are crucial for the biofuel industry to balance supply with demand, minimize waste, and ensure profitability. AI technologies are revolutionizing these aspects of the supply chain. Machine learning algorithms, particularly deep learning models, are being employed to predict biofuel demand with unprecedented accuracy. Biofuel supply chain (BSC) design and optimization are essential for largescale production and utilization of bioenergy. Conventional BSC modeling approaches include optimization (e.g., multi-objective mixed-integer linear programming) and simulation (e.g., Monte Carlo simulation), and most of them are computationally intensive, depending on the complexity, variability, and uncertainty of BSCs (Awudu & Zhang, 2012b; Ghaderi et al., 2016).
Previous studies have demonstrated AI as a promising option to overcome computational barriers and provide near-optimal solutions for BSC problems. Many studies combined traditional optimization methods with heuristic algorithms. Several studies highlighted the reduction of computational costs by using heuristics (Asadi et al., 2018; K. Kumar et al., 2015; Lopez et al., 2008), especially when solving BSC models with many variables and constraints (Asadi et al., 2018). A few studies showed a longer computational time comparing heuristic-based optimization (that does not have a commercial solver) with commercial solvers such as LINGO (Sarker et al., 2019; B. Wu et al., 2015). However, compared with open-source mixed-integer non-linear programming (MINLP) solvers, BONMIN and NOMAD, heuristics can achieve better values for the objective functions (Sarker et al., 2018, 2019; B. Wu et al., 2015). Furthermore, some studies leveraged data-driven AI techniques such as FNN and SVM to develop predictive models when the knowledge of input–output relationships are too limited to use traditional optimization/simulation approaches. Mirkouei et al. used SVM to predict the biomass quality, and accessibility indicators that were then used in an optimization model for the biomass-to-bio-oil supply chain in Oregon, U.S., and the model was solved by GA (Mirkouei et al., 2017). AI has been used to address the modeling challenges of traditional BSC optimization, such as unknown input–output relationships, uncertainty, and inclusion of individual behavior. Most studies optimize economic performance, and future research should explore AI applications for understanding and optimizing the environmental and social impacts of bioenergy systems. Integrating AI with LCA is a promising research direction, and a few studies already show the benefits of AI in addressing data challenges of LCA for different biomass systems.
1.7 AI in Biofuel Research and Development
Artificial intelligence (AI) is revolutionizing the field of biofuel research and development, accelerating the discovery of new feedstocks, optimizing production processes, and enhancing the overall efficiency of biofuel systems. As the world seeks sustainable alternatives to fossil fuels, AI is emerging as a powerful tool to overcome the challenges associated with biofuel production and to unlock new possibilities in this critical field.
One of the most promising applications of AI in biofuel research is in the discovery and development of novel feedstocks. Traditional methods of identifying and optimizing biofuel crops are time-consuming and resource-intensive. AI, particularly machine learning algorithms, can significantly speed up this process by analysing vast amounts of genomic and phenotypic data to identify promising crop varieties. For instance, Jiang et al., (2024) developed a machine learning model to predict the theoretical ethanol yield of lignocellulosic biomass based on its chemical composition. Their model, which utilized random forest algorithms, demonstrated high accuracy in predicting ethanol yields, potentially accelerating the screening process for new biofuel feedstocks.
AI is also playing a crucial role in optimizing the complex biochemical processes involved in biofuel production. Fermentation, a key step in bioethanol production, is a complex process influenced by numerous factors. Kumar et al. employed artificial neural networks (ANNs) to model and optimize the fermentation process for bioethanol production from sugarcane bagasse (A. Kumar et al., 2016). Their AI-driven approach led to improved ethanol yields and reduced fermentation times, showcasing the potential of AI in enhancing biofuel production efficiency.
In the realm of algal biofuels, which hold promise as a high-yield, sustainable biofuel source, AI is helping to overcome some of the persistent challenges in large-scale production.
AI is also contributing to the development of more efficient and cost-effective biofuel production processes. A studyapplied deep learning techniques to optimize the pretreatment process of lignocellulosic biomass, a critical step in second-generation biofuel production (David et al., 2023). Their AI model could predict the sugar yields from various pretreatment conditions, allowing for rapid optimization of this energy-intensive process.
Furthermore, AI is enhancing our understanding of the complex systems involved in biofuel production and use. (Larsson et al., 2023
