Abstract

Biohydrogen production from waste materials presents a sustainable solution for clean energy, but optimizing this process remains challenging due to the complexity of biological systems and variable operational conditions. This paper explores the integration of machine learning (ML) techniques to enhance biohydrogen production processes. By employing ML algorithms for data analysis, predictive modeling, and real-time process control, significant improvements in yield and efficiency can be achieved. The study reviews various applications of ML in optimizing fermentation, substrate selection, and operational conditions, demonstrating how these advanced methods can overcome traditional limitations. Despite the promise of ML, challenges such as data quality, system complexity, and scalability need to be addressed. This analysis highlights the transformative potential of ML in advancing biohydrogen production and offers insights into future research directions for achieving more efficient and sustainable energy solutions.