Predicting biohydrogen production from dark fermentation of organic waste biomass using multilayer perceptron artificial neural network (MLP–ANN)

The focus on sustainable energy has increased interest in biohydrogen production through dark fermentation of organic waste biomass, offering dual benefits of energy production and waste management. Optimizing this process is challenging due to complex interactions among substrate composition, microbial consortia, and fermentation parameters. A multilayer perceptron artificial neural network model was developed to predict biohydrogen yield from organic waste. The model, trained on 180 data points from 35 studies, uses inputs, such as substrate type, inoculum type, concentration, pH, and temperature, with hydrogen yield as the output. The multilayer perceptron artificial neural network model achieved high accuracy, with a root mean square error of 0.3838, a mean absolute percentage error of 0.1938, and a coefficient of determination of 0.8381. These results demonstrate the model's effectiveness in predicting biohydrogen production, providing a valuable tool for optimizing the fermentation process and advancing sustainable energy solutions.