(23a) Constrained Grey-Box Multi-Objective Optimization Framework for Optimal Design of Energy Systems
AIChE Spring Meeting and Global Congress on Process Safety
Monday, March 27, 2017 - 1:30pm to 2:00pm
Derivative-Free Optimization (DFO) methods are commonly utilized for the optimization of models that lack the closed-form equations or models that strongly rely on input-output data. We have previously introduced the constrained grey-box optimization algorithm called ARGONAUT  that couples tractable surrogate approximations, which accurately represent any unknown correlations, with the state-of-the art Mixed-Integer Nonlinear Programming (MINLP) global optimization solver ANTIGONE.  In this work, we further expand the existing algorithm to handle mixed-integer programming and multi-objective optimization problems, and test the proposed framework on a case study based on the energy system design for commercial buildings such as a supermarket.  We provide solutions to two cases; (a) optimal design based on the single-objective economic behavior or the environmental impact (b) optimal design based on the multi-objective design criteria, simultaneous optimization of economic and environmental behavior. We demonstrate that our framework enables optimization of expensive simulation-based models under multiple competing objectives in a computationally efficient way. The results are presented in the form of Pareto-frontier, compare favorably to the model-based solution in .
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