Data-driven Process Systems Engineering Lab
Optimal design of energy systems using constrained grey-box multi-objective optimization
Computers & Chemical Engineering 2018
The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the presence of multiple conflicting objectives, for which the goal is to generate trade-off compromise solutions, commonly known as Pareto-optimal solutions. We have previously introduced the p-ARGONAUT system, parallel AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, which is designed to optimize general constrained single-objective grey-box problems by postulating accurate and tractable surrogate formulations for all unknown equations in a computationally efficient manner.
Link to Publication