Back propagation neural network modelling of biodegradation and fermentative biohydrogen production using distillery wastewater in a hybrid upflow anaerobic sludge blanket reactor

In a hybrid upflow anaerobic sludge blanket (HUASB) reactor, biodegradation in association with biohydrogen production was studied using distillery wastewater as substrate. The experiments were carried out at ambient temperature (34 ± 1 °C) and acidophilic pH of 6.5 with constant hydraulic retention time (HRT) of 24 h at various organic loading rates (OLRs) (1–10.2 kg COD m−3 d−1) in continuous mode. A maximum hydrogen production rate of 1300 mL d−1 was achieved. A back propagation neural network (BPNN) model with network topology of 4-20-1 using Levenberg–Marquardt (LM) algorithm was developed and validated. A total of 231 data points were studied to examine the performance of the HUASB reactor in acclimatisation and operation phase. The statistical qualities of BPNN models were significant due to the high correlation coefficient, R2, and lower mean absolute error (MAE) between experimental and simulated data. From the results, it was concluded that BPNN modelling could be applied in HUASB reactor for predicting the biodegradation and biohydrogen production using distillery wastewater.