This work demonstrates the optimization of the industrial scale chlorobenzene process, which continuously produces multiple products and includes a multiphase reaction with bubble column reactors (BCRs). The trust region filter (TRF) method is applied to carry out the demand‐based optimization of large chlorobenzene process with high‐fidelity BCR models. The TRF method uses surrogate models that substitute the high‐fidelity BCR models in the process model, and avoids the direct implementation of high‐fidelity models, which leads to a large and intractable optimization problem. The surrogate models are constructed based on basis functions that apply first order corrections from the gradients of high‐fidelity models. Different basis functions, CSTR and linear models, are studied in this work. As a result, the usage of CSTR models for the basis function leads to fewer function evaluations of the high‐fidelity model because CSTR model is a reasonable approximation of the high‐fidelity models and an initial guess of the optimization problem. Also, the TRF with surrogate models successfully provides an optimal solution of the high‐fidelity process model with few iterations and function evaluations of the high‐fidelity model itself. From the comparison with a low‐fidelity CSTR model, the solution with the TRF presents more accurate results. The surrogate approaches also make a smooth transition from low‐ to high‐fidelity models in process development. We apply this approach to a demand‐based optimization that integrates nontrivial business options, including optimal shortage of customer demands for profitable operation.

中文翻译：

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基于信任区策略的高保真和替代反应器模型的基于需求的氯苯工艺优化

这项工作证明了工业规模氯苯工艺的优化，该工艺可连续生产多种产品，并包括与鼓泡塔反应器（BCR）的多相反应。信赖域过滤器（TRF）方法用于通过高保真BCR模型对大型氯苯工艺进行基于需求的优化。TRF方法使用替代模型替代流程模型中的高保真BCR模型，并且避免了直接执行高保真模型，从而导致了庞大而棘手的优化问题。替代模型是基于基函数构造的，这些基函数应用了高保真模型的梯度中的一阶校正。在这项工作中研究了不同的基函数，CSTR和线性模型。结果是，由于CSTR模型是高保真模型的合理近似，而且是对优化问题的初步猜测，因此将CSTR模型用于基本函数会导致对高保真模型的功能评估减少。同样，具有替代模型的TRF可以通过很少的迭代和对高保真模型本身的功能评估，成功地提供高保真过程模型的最佳解决方案。通过与低保真CSTR模型的比较，采用TRF的解决方案可提供更准确的结果。替代方法还可以在过程开发中从低保真模型平稳过渡到高保真模型。我们将这种方法应用于基于需求的优化，该优化集成了非平凡的业务选择，包括最佳短缺的客户需求以实现有利可图的运营。