Robust Optimization of Nonlinear Processes under Uncertainty

过程系统设计、运行和控制中的许多重要问题和挑战可以典型地提出为非线性优化问题。迄今为止，大多数针对此类问题的最优决策工具主要基于确定性数学模型，其中假设模型中的所有参数值都是精确已知的。然而，在实践中，数学模型仅仅是对真实系统的近似描述，诸如未来需求、价格、设备损耗和工艺条件等参数都具有很大的不确定性。事实证明，忽视这种不确定性可能会导致严重的性能损失、成本增加和能源/环境损失。我们建议发展鲁棒、局部和全局优化方法，以有效解决存在不确定性的非线性优化问题。根据其性质，不确定性可以用静态/主动或被动的方式来解释。将研究两个重要的工业应用，并将开发的方法应用于不确定条件下过程系统的集成设计、优化和控制。

Many important questions and challenges in process systems design, operation and control can be typically posed as nonlinear optimization problems. To date, most optimal decision making tools for such problems are mainly based on deterministic mathematical models, where all parameter values in the model are assumed to be known precisely. In practice, however, mathematical models are merely approximate descriptions of the real system, and parameters such as future demands, prices, equipment wearout and process conditions are subject to significant uncertainty. It has been frequently shown that disregarding such uncertainty can lead to severe performance losses, increased costs, and energy/environmental penalties. We propose to develop robust, local and global optimization methods for the efficient solution of such nonlinear optimization problems in the presence of uncertainties. Depending on their nature, uncertainties can be accounted for in a static/proactive or reactive way. Two important industrial applications will be investigated and the developed methods will be applied for the integrated design, optimization and control of process systems under uncertainty.