Abstract
This paper presents a framework to solve the constrained black-box simulation optimization problem that arises from the optimal energy-efficient design of single-mixed refrigerant natural gas liquefaction process using reliable process simulator. Kriging surrogate model is used to introduce simple, computationally inexpensive, and effective algebraic formulations with reliable derivatives to the black-box objective and constraints functions. The algebraic surrogate optimization problem is embedded into a nonlinear programming (NLP) model in General Algebraic Modeling System (GAMS). The NLP problem is solved using efficient multi-start gradient-based optimization with CONOPT local solver to determine a candidate of decision variables for which the true functions are calculated in the rigorous simulation. The single-mixed refrigerant process is analyzed considering one-to-three-stage expansion and phase separation to assess potential energy savings. The present approach results show that more expansion stages can provide energy savings from 12.02 to 14.70 % comparing two-stage and three-stage expansion system with single-stage. This optimization framework is more effective and consistent than Particle Swarm Optimization and Genetic Algorithm given the same budget of simulation evaluations for the considered simulation optimization problems, resulting in 13.57 to 53.26 % of energy savings.
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