Constrained adaptive sampling for domain reduction in surrogate model generation: Applications to hydrogen production

Abstract

We propose a new approach for sampling domain reduction for efficient surrogate model generation. Currently, the standard procedure is to use box constraints for the independent variables when sampling the exact simulator. However, by including additional inequality constraints to account for interdependencies between these variables, we can drastically reduce the sampling domain and ensure consistency of unit operations. Moreover, we present a methodology for constructing surrogate models based on penalized regression and error-maximization sampling. All these algorithms have been implemented as a free and open-source software package. Through a case study on the water–gas shift reaction for hydrogen production, we show that sampling domain reduction reduces the required number of sampling points significantly and improves the accuracy of the surrogate model.