Data-Driven Process Simulation Using Connected Surrogate Unit Models Exemplified on a Steam Methane Reforming Process

Process simulation based on physical models often faces computational problems with respect to convergence, especially if the underlying flowsheets are complex. The use of data-driven surrogate models connected to flowsheets promises to overcome these challenges. Using the steam methane reforming process, this paper presents the development of surrogate models - artificial neural networks - for the key units of the process that are subsequently connected to form the entire flowsheet. The accuracy of the individual surrogate models is analyzed based on the test error; the accuracy of the flowsheet is evaluated by a benchmark process simulation performed in Aspen Plus (R). Therefore, the predicted key variables, here outlet temperatures and compositions, are compared to the benchmark. It is shown that their maximum error is below the typical measurement error. The comparison of the accuracy of the surrogate-based flowsheet simulation with the Aspen Plus (R) simulation proves to match very well, as long as the training ranges of the underlying surrogate models are not violated. The promising results of this paper pave the way for future work, such as the optimization of process parameters or superstructure optimization.