Improving green hydrogen production from Chlorella vulgaris via formic acid-mediated hydrothermal carbonisation and neural network modelling. (December 2022)

• Record Type: Journal Article
• Authors: Gruber, Zita; Toth, Andras Jozsef; Menyhárd, Alfréd; Mizsey, Peter; Owsianiak, Mikołaj; Fozer, Daniel
• Is Part Of: Bioresource technology. Volume 365(2022)
• Publication Date: 2022-12
• Subjects: Hydrothermal carbonisation -- Microalgae -- Gas formation -- Green hydrogen -- Machine learning -- Combined severity factor; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.128071
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Formic acid mediator in hydrothermal carbonisation raises biomass-to-H2 conversion. Artificial neural network accurately predicts HTC yields and products properties. The HTC gas yield is increased from 1.67 to 13.42 mol kg −1 using an acid mediator. Higher combined severity factor (>1.5) intensifies green hydrogen evolution. For dilute suspensions, CSF>2.8 is needed to maintain a high H2 yield (3 mol kg −1 ). Abstract: 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 −1 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 z = 1.71E−06 & 1.40E−06) and accuracy (R 2 = 0.9974 & R 2 = 0.9781). The enhanced H2 yield indicates an effective alternative green hydrogen production scenario at low temperatures using high-moisture-containing biomass feedstocks.