Stable Dynamic Optimization Strategies for Large-Scale Chemical Processes

Biegler CTS-9729075过程优化策略在过去十年中得到了广泛的应用。 这些已被应用于新的化学工艺的设计，在石化过程中的过程单元和工厂的实时优化，并用于与化学工艺的可操作性和灵活性相关的复杂分析。 绝大多数的这些应用程序已经为稳态过程模型，由代数方程组描述。 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普遍，基于方程的动态过程模型已经构建了多达100，000个微分代数方程（DAE）。 动态过程仿真的成功导致了对能够容易地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仿真。 其中包括当前优化策略的概念局限性。 这些对于高度约束或具有不稳定动态模式的DAE模型非常重要。 后者在包括放热反应器和反应蒸馏的反应和反应分离系统中通常是真实的。 PI正在计划开发一种用于动态优化的同步配方，其中包括基于边界值配方的稳定大规模分解。 将考虑新的非线性规划，检测不稳定模式的策略，利用问题结构进行大规模分解，并采用领先的边缘方法来处理大型不等式约束。 这些战略的发展将得到与专门从事非线性规划和DAE解决方案算法的应用数学家的研究合作的帮助。 这些策略的组合将导致大规模的动态优化策略，可以应用于目前在工业应用中考虑的动态仿真模型。 所开发的方法将通过反应工程和反应蒸馏中的大规模工业应用进行验证。

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.