Data-driven strategies for optimization of integrated chemical plants

Abstract

Operation optimization over large-scale integrated chemical plants is an inherently complex problem. We propose a surrogate-based optimization approach to optimize the operation of an industrial site that addresses both short-term market change and long-term maintenance plans. We develop a platform for automating the simulation and construction of surrogate models with a propagation error mitigation strategy. We are the first to investigate the impact of different levels of abstraction for surrogate models in site-level optimization. We also develop a deterministic, discrete-time optimization model that uses data-driven surrogate models. By optimizing a rolling horizon model with the above optimization model as the underlying model for each planning interval, we show that the plant level of abstraction is the superior approach. We demonstrate how data-driven surrogates can help address site-level process optimization by abstracting the process site network to a level that balances relevant details with tractability.