Stable Dynamic Optimization Strategies for Large-Scale Chemical Processes

比格勒CTS-9729075工艺优化策略在过去十年中得到了广泛应用。它们已被应用于新的化工工艺的设计，石油化工工艺中的工艺单元和装置的实时优化，以及与化工工艺的可操作性和灵活性有关的复杂分析。这些应用中的绝大多数都是用代数方程系统描述的稳态过程模型。与此同时，功能强大的大规模动态仿真工具的开发也变得越来越广泛，基于方程的动态过程模型已经使用多达10万个微分-代数方程(DAE)来构建。动态过程模拟的成功导致了对易于处理由微分代数方程(DAE)描述的过程模型的优化工具的需求。然而，由于多种原因，动态优化策略的发展滞后于模拟。其中包括当前优化策略的概念限制。这些对于受高度约束或动态模式不稳定的DAE模型非常重要。后者通常适用于反应和反应分离系统，包括放热反应器和反应蒸馏。PI正在计划开发动态优化的同时公式，其中包括基于边值公式的稳定的大规模分解。将考虑新的非线性规划、检测不稳定模式、利用问题结构进行大规模分解以及采用前沿和边缘方法来处理大型不等式约束集的策略。这些策略的开发将得到与专门研究非线性规划和DAE求解算法的应用数学家的研究合作的帮助。这些策略的结合将导致大规模的动态优化策略，这些策略可以应用于目前工业应用中考虑的动态仿真模型。开发的方法将在反应工程和反应蒸馏的大规模工业应用中得到验证。

Biegler CTS-9729075 Strategies for process optimization have seen widespread applications over the past decade. These have been applied in the design of new chemical processes, real-time optimization of process units and plants in petrochemical processes, and for sophisticated analyses related to the operability and flexibility of chemical processes. The vast majority of these applications have been for steady state process models, described by systems of algebraic equations. Simultaneously, development of powerful, large-scale dynamic simulation tool are also becoming widespread and equation based dynamic process models have been constructed with up to 100,000 differential-algebraic equations (DAE). The success of dynamic process simulation has led to the demand for optimization tools that deal readily with process models described by differential algebraic equations (DAEs). However, development of dynamic optimization strategies has lagged that of simulation for a number of reasons. Among these are conceptual limitations of current optimization strategies. These become important for DAE models that are highly constrained or have unstable dynamic modes. The latter are often true in reactive and reactive-separation systems including exothermic reactors and reactive distillation. The PI is planning on developing a simultaneous formulation for dynamic optimization that includes a stable large-scale decomposition based on boundary value formulations. Novel nonlinear programming, strategies that detect unstable modes, exploit problem structure for large scale decomposition and adapt leading, edge methods for the treatment of large sets of inequality constraints will be considered. The development of these strategies will be aided by research collaborations with applied mathematicians that specialize in nonlinear programming and DAE solution algorithms. The combination of these strategies will lead to large-scale dynamic optimization strategies that can be applied to dynamic simulation models currently considered in industrial applications. The methods developed will be validated by large scale industrial applications in reaction engineering and reactive distillation.