Application of artificial neural networks for modeling of biohydrogen production

In this study, an artificial neural network (ANN) model was developed to estimate the hydrogen production profile with time in batch studies. A back propagation artificial neural network ANN configuration of 5–6–4–1 layers was developed. The ANN inputs were the initial pH, initial substrate and biomass concentrations, temperature, and time. The model training was done using 313 data points from 26 published experiments. The correlation coefficient between the experimental and estimated hydrogen production was 0.989 for training, validating, and testing the model. Results showed that the trained ANN successfully predicted the hydrogen production profile with time for new data with a correlation coefficient of 0.976. ► ANN model was developed to estimate temporal hydrogen production in batch tests. ► A BPNN configuration of 5–6–4–1 layers was established. ► Inputs were initial pH, substrate and biomass concentrations, temperature and time. ► A total of 313 data points from 26 experiments were used for model training. ► Testing the model with new data points showed a correlation coefficient of 0.976.