Kriging-assisted constrained optimization of single-mixed refrigerant natural gas liquefaction process
Lucas Francisco dos Santos, Caliane Bastos Borba Costa, José Antonio Caballero, Mauro A.S.S. Ravagnani · Chemical Engineering Science · 2021
This paper presents a surrogate-assisted framework to solve constrained black-box optimization problems, which is applied to the optimal single-mixed refrigerant natural gas liquefaction processes design. This approach uses kriging models as cheap-to-evaluate, reliable surrogates of the process-simulator-dependent black-box functions of the optimization problem. From kriging prediction and estimated error, the probability of feasible improvement acquisition function is derived and optimized to search forfeasible and improving candidates to perform local search in the penalized simulation. The kriging-assisted global search scheme is efficient to find promising candidates for local refinement, and it improves previous results that used only local optimization by 3.36 and 5.32%. The surrogate model reduces the time to evaluate the functions from 0.9933 ± 0.0815 to 3.515e-4 ± 8.353e-5 s. The kriging approximation mean squared errors assessed in a k -fold cross-validation is 7.271e-7 ± 1.297e-7 for the objective function and 0.3099 ± 0.0856 for the constraint.