A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method

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 ions, including Mg2+, Cu2+, Na+, NH4+, K+, behave differently towards response hydrogen yield (HY) evolution rate (HER). Manipulating size concentration found to be effective in enhancing HY for but not ions. An optimal range particle (86–120 nm) Ni-ion/NP (81–120 mg L−1) existed HER. Meanwhile, manipulation ineffective both iron nickel improvement fact, variation enhancement HER demonstrated an appreciable difference. The smaller (less than 42 were definitely improve HY, whereas HER, relatively bigger (40–50 seemed significantly increase H2 rate. It also that variations investigated ions only statistically influenced HY. level (the enhanced HER) inputs underpinned order significance identified as following: Na+ > Mg2+ Cu2+ NH4+ K+.