Framework for Designing Flexible Steady State and Dynamic Chemical Processes

摘要：卢克E。Achenie机构：本研究旨在开发新的数学算法和相关软件，用于系统地确定化学过程（CP）的最佳设计裕度。 这些设计裕度保证了CP在操作阶段灵活性。 灵活的CP能够可靠地操作，尽管在CP的设计期间使用不准确的数学过程模型。 从经济、安全和环境的角度来看，CP的灵活性是化学过程设计中非常重要的问题。 影响灵活性的模型不确定性主要有三类：（a）模型结构不确定性，（B）参数不确定性（由于在存在测量不确定性的情况下确定模型参数而引入），以及（c）操作不确定性。 在这项工作中，PI关注（B）和（c）。传统上，通过使用基于经验和经验的超设计系数来考虑灵活性问题。 过度设计因素本质上是经验性的，往往导致资源使用成本高、效率低。 该项目更广泛的影响是开发方法，减少在工业设计中使用经验性过度设计因素的需要。 具体而言，以下问题将得到解决：（一）估计的CP的灵活性（即CP的能力，以维持一个可接受的性能水平，即使外部和内部因素发生变化，以不可预测的方式在某些已知的界限），和（ii）CP优化下的不确定性在设计阶段。

ABSTRACTPI: Luke E. Achenie Institution: University of ConnecticutProposal Number: 0097936This research aims at developing novel mathematical algorithms and associated software for systematic determination of optimal design margins for chemical processes (CP). These design margins guarantee that flexibility of the CP at the operation stage. A flexible CP is able to operate reliably in spite of the use of inaccurate mathematical process models during the design of the CP. The flexibility of a CP is a very important concern in the design of chemical processes, from economic, safety and environmental points of view. Model uncertainties that impact flexibility are primarily of three kinds: (a) model structure uncertainty, (b) parametric uncertainty (introduced as a result of determining model parameters in the presence of measurement uncertainty), and (c) operational uncertainty. In this work, the PIs are concerned with (b) and (c).Flexibility issues have traditionally been considered through the use of over-design factors, which are based on rules of thumb and experience. Over-design factors are inherently empirical and often lead to costly and inefficient use of resources. The broader impact of this project is to develop methodologies that will reduce the need to use empirical over-design factors in industrial design. Specifically, the following problems will be addressed: (i) estimation of the flexibility of the CP (i.e. the CP's ability to sustain an acceptable level of performance even when external and internal factors change in an unpredictable fashion within certain known bounds), and (ii) CP optimization under uncertainty at the design stage.