• Energy Research

This paper reviews the current technological development of bio-hydrogen (BioH2) generation, focusing on using lignocellulosic feedstock via dark fermentation (DF). Using the collected reference reports as the training data set, supervised machine learning via the constructed artificial neuron networks (ANNs) imbedded with feed backward propagation and one cross-out validation approach was deployed to establish correlations between the carbon sources (glucose and xylose) together with the inhibitors (acetate and other inhibitors, such as furfural and aromatic compounds), hydrogen yield (HY), and hydrogen evolution rate (HER) from reported works. Through the statistical analysis, the concentrations variations of glucose (F-value = 0.0027) and acetate (F-value = 0.0028) were found to be statistically significant among the investigated parameters to HY and HER. Manipulating the ratio of glucose to acetate at an optimal range (approximate in 14:1) will effectively improve the BioH2 generation (HY and HER) regardless of microbial strains inoculated. Comparative studies were also carried out on the evolutions of electron equivalent balances using lignocellulosic biomass as substrates for BioH2 production across different reported works. The larger electron sinks in the acetate is found to be appreciably related to the higher HY and HER. To maintain a relative higher level of the BioH2 production, the biosynthesis needs to be kept over 30% in batch cultivation, while the biosynthesis can be kept at a low level (2%) in the continuous operation among the investigated reports. Among available solutions for the enhancement of BioH2 production, the selection of microbial strains with higher capacity in hydrogen productions is still one of the most phenomenal approaches in enhancing BioH2 production. Other process intensifications using continuous operation compounded with synergistic chemical additions could deliver additional enhancement for BioH2 productions during dark fermentation.

In this work, the impact of chemical additions, especially nano-particles (NPs), was quantitatively analyzed using our constructed artificial neural networks (ANNs)-response surface methodology (RSM) algorithm. Fe-based and Ni-based NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Fe-based NPs and ions, but not for Ni-based NPs and ions. An optimal range of particle size (86–120 nm) and Ni-ion/NP concentration (81–120 mg L−1) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40–50 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ > Mg2+ > Cu2+ > NH4+ > K+.