A novel robust optimization framework is proposed to address general nonlinear problems in process design. Local linearization is taken with respect to the uncertain parameters around multiple realizations of the uncertainty, and an iterative algorithm is implemented to solve the problem. Furthermore, the proposed methodology can handle different categories of problems according to the complexity of the problems. First, inequality-only constrained optimization problem as studied in most existing robust optimization methods can be addressed. Second, the proposed framework can deal with problems with equality constraint associated with uncertain parameters. In the final case, we investigate problems with operation variables which can be adjusted according to the realizations of uncertainty. A local affinely adjustable decision rule is adopted for the operation variables (i.e., an affine function of the uncertain parameter). Different applications corresponding to different classes of problems are used to demonstrate the effectiveness of the proposed nonlinear robust optimization framework. © 2017 American Institute of Chemical Engineers AIChE J, 2017

中文翻译：

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非线性鲁棒优化的过程设计

提出了一种新颖的鲁棒优化框架来解决过程设计中的一般非线性问题。针对不确定性的多个实现周围的不确定性参数进行局部线性化，并采用迭代算法来解决该问题。此外，所提出的方法可以根据问题的复杂性来处理不同类别的问题。首先，可以解决大多数现有的鲁棒优化方法中研究的仅不等式约束优化问题。其次，提出的框架可以处理与不确定性参数相关的等式约束问题。最后，我们研究可根据不确定性实现进行调整的操作变量问题。对于操作变量（即，不确定参数的仿射函数）采用局部仿射可调整决策规则。对应于不同类别问题的不同应用被用来证明所提出的非线性鲁棒优化框架的有效性。©2017美国化学工程师学会AIChE的Ĵ，2017年