Computational Modeling and Optimization Strategies for Biohydrogen Production

Biohydrogen has gained recognition as a promising and clean alternative energy source. However, this promising technology faces challenges in terms of microbial metabolism, optimization, and scaling-up the processes. This chapter focuses on the computational models and optimization strategies that are crucial for biohydrogen production. Simplified microbial processes from previous studies have been reviewed in this chapter. Dark and photofermentations have been demonstrated as suitable methods to produce biohydrogen from different types of biomasses, depending on the operational conditions. Studies have shown that response surface methodology (RSM) is a key computational tool to simplify and optimize this microbial process, including the influence of kinetic models for scaling-up the process. Both theoretically and experimentally, sequential dark and photofermentations have been demonstrated to exhibit high yields and productivity rates for the production of biohydrogen. It is crucial to conduct further research into key areas, including optimizing slow metabolism, selecting appropriate feedstocks, and advancing computational models. These strategies can enhance the sustainability and economic viability of biohydrogen production. Biohydrogen production models offer the potential for renewable and sustainable hydrogen production. However, challenges remain in terms of optimizing conversion efficiency, scaling up production, reducing costs, and developing the necessary infrastructure for storage and distribution. Ongoing research and technological advancements are aimed at addressing these challenges and making biohydrogen a viable option for clean energy production. It is important to also note that the effectiveness of ANNs for biohydrogen production depends on the specific application, available data, and the expertise of the modelers. Additionally, other modeling approaches such as mechanistic models or hybrid models, combining ANNs with other techniques, may complement the strengths and weaknesses of ANNs to provide more comprehensive insights into biohydrogen production processes.