Experimental Characterization and Energy Performance Assessment of a Sorption-Enhanced Steam–Methane Reforming System

Artificial Neural Network Model for the Prediction of Methane Bi-Reforming Products Using CO2 and Steam

The bi-reforming of methane (BRM) is a promising process which converts greenhouse gases to syngas with a flexible H2/CO ratio. As there are many factors that affect this process, the coupled effects of multi-parameters on the BRM product are investigated based on Gibbs free energy minimization. Establishing a reliable model is the foundation of process optimization. When three input parameters are changed simultaneously, the resulting BRM products are used as the dataset to train three artificial neural network (ANN) models, which aim to establish the BRM prediction model. Finally, the trained ANN models are used to predict the BRM products when the conditions vary in and beyond the training range to test their performances. Results show that increasing temperature is beneficial to the conversion of CH4. When the molar flow of H2O is at a low level, the increase in CO2 can enhance the H2 generation. While it is more than 0.200 kmol/h, increasing the CO2 flowrate leads to the increase and then decrease in the H2 molar flow in the reforming products. When the numbers of hidden layer neurons in ANN models are set as (3, 3), (3, 6) and (6, 6), all the correlation coefficients of training, validation and test are higher than 0.995. When these ANN models are used to predict the BRM products, the variation range of the prediction error becomes narrower, and the standard deviation decreases with the increase in neuron number. This demonstrates that the ANN model with more neurons has a higher accuracy. The ANN model with neuron numbers of (6, 6) can be used to predict the BRM products even when the operating conditions are beyond the training ranges, demonstrating that this model has good extension performance. This work lays the foundation for an artificial intelligent model for the BRM process, and established ANN models can be further used to optimize the operating parameters in future work.