Abstract
The integrated design and control (IDC) framework is becoming increasingly important for systematic design of flexible manufacturing and energy systems. Recent advances in computing and derivative-free optimization have enabled more tractable solution methods for complex IDC problems that involve, e.g., multi-period dynamics, the presence of high-variance and non-stationarity probabilistic uncertainties, and mixed-integer control/scheduling decisions. Parallelly, developments in techno-ecological synergy (TES) have allowed co-design of industrial and environmental systems that have been shown to lead to win-win solutions in terms of the economy, ecological, and societal benefits. In this work, we propose to combine the IDC and TES frameworks to more accurately capture the real-time interactions between process systems and the surrounding natural resources (e.g., forests, watersheds). Specifically, we take advantage of (multi-scale) model predictive control to close the loop on a realistic high-fidelity simulation of the overall TES system. Since this closed-loop simulation is computationally expensive, we propose to solve the resulting design problem using a data-efficient constrained Bayesian optimization method. We demonstrate that the new perspective offered by the proposed TES-IDC framework leads to robust win-win solutions that can more effectively handle uncertainty in future disturbances compared to technology-only solutions on a chloralkali manufacturing unit built in an urban forest.