Machine learning in biohydrogen production: a review

Abstract

Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high energy yield to replace conventional fossil fuels. However, biohydrogen commercial uptake is mainly hindered by the supply side. As a result, various operating parameters must be optimized to realize biohydrogen commercial uptake on a large-scale. Recently, machine learning algorithms have demonstrated the ability to handle large amounts of data while requiring less in-depth knowledge of the system and being capable of adapting to evolving circumstances. This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production. The accuracy and potential of different machine learning algorithms are reported. Also, the practical implications of machine learning models to realize biohydrogen uptake by the transportation sector are discussed. The review indicates that machine learning algorithms can successfully model non-linear and complex interactions between operational and performance parameters in biohydrogen production. Additionally, machine learning algorithms can help researchers identify the most efficient methods for producing biohydrogen, leading to a more sustainable and cost-effective energy source.

Non-thermal plasma catalysis driven sustainable pyrolysis oil upgrading to jet fuel under near-ambient conditions

“…This confidence is based on the elimination of an additional step involving pure H 2 -a process often derived from energy-intensive and highly endothermic natural gas reforming. 71 Consequently, we anticipate that plasma-driven pyrolysis oil with CH 4 will emerge as a promising and sustainable approach for the production of premium fuels and chemicals even from heavy oils.…”

Life cycle assessment of hydrogen production, storage, and utilization toward sustainability

“…It is worth noting that biohydrogen, a carbon-free and high-energy-dense fuel, holds promise for clean energy production, though its complexity poses challenges for optimization (Alagumalai et al, 2023). It is generated biologically through various methods like fermentation (Sarangi & Nanda, 2020;Wang et al, 2012), biophotolysis (Ghirardi et al, 2014;Javed et al, 2022), and microbial electrolysis cells (Cardeña et al, 2019;Gautam et al, 2023), utilizing biowastes, thus reducing costs and pollution, as shown in Figure 1.…”

“…The key to cost-effective biohydrogen production lies in efficiently converting complex organic feedstock into fermentable glucose. Different biomass types require specific pretreatment methods; for instance, lignocellulosic materials necessitate thorough pretreatment due to their complex composition of cellulose, hemicellulose, and lignin (Alagumalai et al, 2023).…”