Improving green hydrogen production from Chlorella vulgaris via formic acid-mediated hydrothermal carbonisation and neural network modelling.

This study investigates the formic acid-mediated hydrothermal carbonisation (HTC) of microalgae biomass to enhance green hydrogen production. The effects of combined severity factor (CSF) and feedstock-to-suspension ratio (FSR) are examined on HTC gas formation, hydrochar yield and quality, and composition of the liquid phase. The hydrothermal conversion of Chlorella vulgaris was investigated in a CSF and FSR range of -2.529 and 2.943; and 5.0 wt.% - 25.0 wt.%. Artificial neural networks (ANNs) were developed based on experimental data to model and analyse the HTC process. The results show that green hydrogen formation can be increased up to 3.04 mol kg by applying CSF 2.433 and 12.5 wt.% FSR reaction conditions. The developed ANN model (BR-2-11-9-11) describes the hydrothermal process with high testing and training performance (MSE = 1.71E-06 & 1.40E-06) and accuracy (R = 0.9974 & R = 0.9781). The enhanced H yield indicates an effective alternative green hydrogen production scenario at low temperatures using high-moisture-containing biomass feedstocks.