Simulation of biological hydrogen production in a UASB reactor using neural network and genetic algorithm

In this study the performance of a granule-based H2-producing upflow anaerobic sludge blanket (UASB) reactor was simulated using neural network and genetic algorithm. A model was designed, trained and validated to predict the steady-state performance of the reactor. Organic loading rate, hydraulic retention time (HRT), and influent bicarbonate alkalinity were the inputs of the model, whereas the output variables were one of the following: H2 concentration, H2 production rate, H2 yield, effluent total organic carbon, and effluent aqueous products including acetate, propionate, butyrate, valerate, and caporate. Training of the model was achieved using a large amount of experimental data obtained from the H2-producing UASB reactor, whereas it was validated using independent sets of performance data obtained from another H2-producing UASB reactor. Subsequently, predictions were performed using the validated model to determine the effects of substrate concentration and HRT on the reactor performance. The simulation results demonstrate that the model was able to effectively describe the daily variations of the UASB reactor performance, and to predict the steady-state reactor performance at various substrate concentrations and HRTs.