Effects of Surrogate Hybridization and Adaptive Sampling for Simulation-Based Optimization

Process simulators are essential for modeling of complex processes; however, optimization of expensive models remains challenging due to lack of equations, simulation cost, and lack of convergence guarantees. To tackle these challenges, surrogate modeling and surrogate-based optimization methods have been proposed. Most commonly, surrogates are treated as black-box models, while recently hybrid surrogates have gained popularity. In this work, we assess two main methodologies: (a) optimization of surrogates trained using a set of fixed <i>a priori</i> samples using deterministic solvers, and (b) adaptive sampling-based optimization, which leverages surrogate predictions to guide the search process. Across both methods, we systematically compare the effect of black-box versus hybrid surrogates, that utilize a "model-correction" architecture combining different fidelity data. Through mathematical benchmarks with up to ten dimensions, and two engineering case studies for process design of an extractive distillation simulation model and an adsorption simulation model, we present the effects of sampling quantity, dimensionality, formulation, and hybridization on solution convergence, reliability, and CPU efficiency. Our results show that hybrid modeling improves surrogate robustness and reduces solution variability with fewer samples, though it increases optimization costs. Additionally, adaptive sampling methods are more efficient and consistent than fixed-sampling surrogate strategies, even across different sampling and dimensionality scenarios.