Data-driven decision-focused surrogate modeling
This work addresses real-time computational bottlenecks in chemical engineering optimization, offering an incremental improvement over existing surrogate modeling techniques.
The paper tackles the challenge of solving nonlinear optimization problems in real-time by learning convex surrogate models that minimize decision prediction error, demonstrating significantly higher data efficiency and accuracy compared to standard methods in chemical process optimization.
We introduce the concept of decision-focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real-time settings. The proposed data-driven framework seeks to learn a simpler, e.g. convex, surrogate optimization model that is trained to minimize the decision prediction error, which is defined as the difference between the optimal solutions of the original and the surrogate optimization models. The learning problem, formulated as a bilevel program, can be viewed as a data-driven inverse optimization problem to which we apply a decomposition-based solution algorithm from previous work. We validate our framework through numerical experiments involving the optimization of common nonlinear chemical processes such as chemical reactors, heat exchanger networks, and material blending systems. We also present a detailed comparison of decision-focused surrogate modeling with standard data-driven surrogate modeling methods and demonstrate that our approach is significantly more data-efficient while producing simple surrogate models with high decision prediction accuracy.