Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Area of Science:

• Biotechnology and Bioengineering
• Sustainable Energy
• Computational Science

Background:

• Biological processes offer an eco-friendly route to hydrogen production using organic waste.
• The inherent complexity of these biological systems poses challenges in predictability and scalability.

Purpose of the Study:

• To review the application of machine learning (ML) in biohydrogen production.
• To highlight ML's role in modeling and predicting complex biohydrogen processes.

Main Methods:

• Review of contemporary research on ML algorithms in biohydrogen production.
• Implementation of ML for modeling operational and performance parameters.
• Utilizing microbial sequencing data as input for ML models.

Main Results:

• ML algorithms effectively model nonlinear relationships in biohydrogen production.
• Reinforced ML methods show precise state prediction and kinetic retrieval.
• Incorporating microbial data enhances ML-based prediction accuracy.

Conclusions:

• Machine learning is crucial for overcoming predictability and reliability issues in biohydrogen production.
• ML can aid in developing process control tools for stable hydrogen generation.
• Further ML research can link process performance with microbial population dynamics.