An adaptive sampling surrogate model building framework for the optimization of reaction systems

Abstract

Many industrial engineering problems involve complex formulations and are assisted by simulation tools. Although these tools provide highly accurate solutions, they may not be suitable for large scale problems and for optimization applications. Looking for alternatives to complex formulations that often lead to convergence issues and to time consuming solutions, the use of surrogate modeling for reaction systems is addressed herein. We propose a novel adaptive sampling algorithm that iteratively explores the solution space and incorporates ideas from adaptive sampling, trust region methods, and successive linear programming approaches. The surrogates are iteratively embedded into optimization problems to check feasibility and to collect insights to the following adaptive sampling iteration. The methodology is applied to a reaction system network and the surrogates are built to predict the reactor outputs. The adaptive sampling algorithm builds highly accurate surrogates that can be embedded into the reaction system optimization leading to near optimal solutions.