Process optimization of catalytic steam reforming of toluene to hydrogen using response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA)

Abstract

Catalytic steam reforming of toluene (SRT) over nickel-cobalt supported on modified activated carbon for hydrogen production has been investigated. The center composite design of experiment in response surface methodology (RSM) was initially applied to optimize the catalytic SRT for hydrogen production before being utilized in the model building of the hybrid artificial neural network-genetic algorithm (ANN-GA). The genetic algorithm was carried out over the ANN model to achieve the maximum target response. The process optimization modeling using the best fitness function gave an insight of the optimal operating condition in SRT over the prepared catalyst. The results conferred that maximum hydrogen yield could be obtained at the optimal conditions of 700 °C temperature, 0.034 ml/min feed flow rate, 0.1 g catalyst loading and S/C ratio of 1 by ANN-GA model, and 762 °C temperature, 0.022 ml/min feed flow rate, 0.3 g catalyst loading and S/C ratio of 5.6 by the RSM model. Predicted results from ANN model were in higher agreement with the experimental data at R2=0.95 compared with the RSM model.