We propose a novel computational framework for the robust optimization of highly nonlinear, non‐convex models that possess uncertainty in their parameter data. The proposed method is a generalization of the robust cutting‐set algorithm that can handle models containing irremovable equality constraints, as is often the case with models in the process systems engineering domain. Additionally, we accommodate general forms of decision rules to facilitate recourse in second‐stage (control) variables. In particular, we compare and contrast the use of various types of decision rules, including quadratic ones, which we show in certain examples to be able to decrease the overall price of robustness. Our proposed approach is demonstrated on three process flow sheet models, including a relatively complex model for amine‐based CO₂ capture. We thus verify that the generalization of the robust cutting‐set algorithm allows for the facile identification of robust feasible designs for process systems of practical relevance.

中文翻译：

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过程系统工程中非线性鲁棒优化的通用割集方法

我们提出了一种新颖的计算框架，用于对参数数据具有不确定性的高度非线性，非凸模型进行鲁棒优化。所提出的方法是鲁棒割集算法的推广，可以处理包含不可移动的等式约束的模型，过程系统工程领域中的模型经常遇到这种情况。此外，我们提供了决策规则的一般形式，以利于求助于第二阶段（控制）变量。特别是，我们比较和对比了各种决策规则（包括二次决策​​）的使用，在某些示例中我们证明了这些决策能够降低总体健壮性。我们的建议方法在三种工艺流程模型中得到了证明，其中包括一个相对复杂的基于胺的CO ₂模型捕获。因此，我们验证了鲁棒的割集算法的一般性可以轻松识别具有实际意义的过程系统的鲁棒可行设计。