Surrogate model optimization: a comparison case study with pooling problems of CO2 point sources

Abstract

In this work, we present a benchmark study to leverage the implementation of surrogate models (SMs) withinmathematical optimization problems for the integration of carbon capture technologies within an industrialcomplex, focusing on the pooling of CO₂ streams to enhance efficiency and reduce capture costs. The SMs arebuilt using data from rigorous process simulations in Aspen Plus, with each data point generated by solvingequation-oriented optimization problems. We evaluate five different SMs approaches: Automated Learning ofAlgebraic Models for Optimization (ALAMO), Kriging, Radial Basis Functions (RBFs), Polynomials, and ArtificialNeural Networks (ANNs). We assess their accuracy, computational efficiency, and optimization performance.In “one-shot” optimization, ALAMO is the most computationally efficient, while Kriging requires the highestCPU time and may struggle with convergence. To improve solution reliability, we incorporate a trust-region filter(TRF) solution strategy. Within this framework, Kriging and ANN achieve the fastest convergence (two iterations),while ALAMO offers a good balance between efficiency and reliability. The RBF SM presents high accuracyon training data; however, it requires more iterations, increasing computational demand.