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.

大型综合化工厂的运营优化是一个固有的复杂问题。我们提出了一种基于代理的优化方法来优化工业场地的运营，以应对短期市场变化和长期维护计划。我们开发了一个平台，用于自动模拟和构建具有传播错误缓解策略的代理模型。我们是第一个研究代理模型的不同抽象级别在站点级别优化中的影响的人。我们还开发了一个使用数据驱动的代理模型的确定性离散时间优化模型。通过使用上述优化模型作为每个计划间隔的基础模型来优化滚动水平模型，我们表明工厂抽象级别是更好的方法。