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Artificial Intelligence in Biofuels: Progress, Trends, and Directions.

Majid Hosseini1,2, Seyed Javad Amirfakhri3, Reza Ghiaasiaan4

  • 1Department of Manufacturing and Industrial Engineering, The University of Texas Rio Grande Valley, Edinburg, Texas, USA.

Chemistry & Biodiversity
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Artificial intelligence (AI) modeling enhances biofuels production by improving efficiency and yield across various fuel types. Integrating AI with existing technologies presents the most viable path for commercial success in sustainable transportation fuels.

Keywords:
algae biofuelsartificial intelligenceartificial neural networkbiodieselbioethanolbiofuelsbiogasbiohydrogenfuzzy logicsgenetic algorithm

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Area of Science:

  • Biotechnology and Bioengineering
  • Sustainable Energy
  • Artificial Intelligence

Background:

  • Biofuels offer a sustainable alternative for transportation fuels, but their production systems are complex.
  • Artificial intelligence (AI) modeling provides advanced capabilities for prediction, optimization, and quality control in biofuel production.
  • Large-scale AI applications in biofuel production are still in early development stages compared to laboratory research.

Purpose of the Study:

  • To discuss the application of AI-based modeling in bioconversion technologies for biofuels.
  • To critically examine AI applications across diverse biofuel types (biodiesel, bioethanol, biogas, etc.).
  • To investigate the benefits and drawbacks of AI in managing, optimizing, and predicting biofuel yields.

Main Methods:

  • Review of AI techniques including fuzzy logic, genetic algorithms (GAs), artificial neural networks (ANNs), expert systems (ES), and adaptive neuro-fuzzy inference system (ANFIS).
  • Analysis of hybrid AI approaches and other AI-based methods for enhancing biofuel production.
  • Discussion of AI's role in improving end-user system performance.

Main Results:

  • AI-based modeling shows significant potential for improving efficiency, dependability, and management in biofuel production.
  • Various AI techniques are being employed to increase yield and quality across different biofuel types.
  • Benefits include enhanced prediction, optimization, and control, while drawbacks relate to current large-scale implementation challenges.

Conclusions:

  • AI integration is crucial for advancing biofuel production technologies.
  • The technology readiness level analysis suggests that integrating AI with established technologies is the most effective commercial approach.
  • Continued research and development are needed to overcome challenges and fully realize AI's potential in large-scale biofuel production.