Combined approach using mathematical modelling and artificial neural network for chemical industries: Steam methane reformer

Abstract

The steam methane reformer (SMR) has become more attractive owing to the increasing importance of hydrogen production using natural gas. This study developed a rigorous dynamic model for an SMR including sub-models for a multiscale reactor, wall, and furnace. The developed SMR model was validated within a small error (lower than 4%) using the reference data such as temperature, pressure, mole fraction, and average heat flux. The results predicted by changing the catalyst parameters and operation conditions confirmed the reliability of the model. Therefore, the developed model was used to generate the SMR performance data using a deterministic and stochastic simulation with four main operating variables: the inlet flow rate, temperature, S/C ratio of the reactor side, and the inlet flow rate of the furnace side. To reduce the data dimensionality, the resultant dataset was analyzed using the principle components based on a singular value decomposition method. Artificial neural network (ANN) trained through 81 datasets was applied for the feed-forward back propagation of a neural network to map the relationship between the operating variables and predicted outputs. And the ANN relation predicted the outputs (temperature, velocity, pressure, and mole fraction of components) with higher than 98.91% accuracy. Furthermore, the computational time was significantly reduced from 1200 s (dynamic simulation) to 2 s (ANN). The developed methodology can be applied not only for the online operation and optimization of a reformer with high accuracy but also for the design of a hydrogen production system at low computational cost.