Development of short chain fatty acid-based artificial neuron network tools applied to biohydrogen production

Abstract

The biological production of biohydrogen through dark fermentation is a very complex system where the use of an artificial neuron network (ANN) for prediction, controlling and monitoring has a great potential. In this study three ANN models based on volatile fatty acids (VFA) production and speciation were evaluated for their capacity to predict (i) accumulated H2 production, (ii) hydrogen production rate and (iii) H2 yield. Lab-scale biohydrogen and VFA production kinetics from a previous study were used for training and validation of the models. The input parameters studied were: time and acetate and butyrate concentrations (model 1), time and lactate, acetate, propionate and butyrate concentrations (model 2), time and the sum of all VFA (model 3) and time and butyrate/acetate (model 4). All models could predict biohydrogen accumulated production, hydrogen production rate and H2 yield with high accuracy (R2 > 0.987). VFAT is the input parameter indicated for processes using pure cultures, while for complex/mixed cultures a model based on acetate and butyrate is recommended.