Advances in Simultaneous Strategies for Dynamic Process Optimization

Following on the popularity of dynamic simulation for process systems, dynamic optimization has been identi ed as an important task for key process applications. In this study, we present an improved algorithm for simultaneous strategies for dynamic optimization. This approach addresses two important issues for dynamic optimization. First, an improved nonlinear programming strategy is developed based on interior point methods. This approach incorporates a novel lter-based line search method as well as preconditioned conjugate gradient method for computing search directions for control variables. This leads to a signi cant gain in algorithmic performance. On a dynamic optimization case study, we show that nonlinear programs (NLPs) with over 800,000 variables can be solved in less than 67 CPU minutes. Second, we address the problem of moving nite elements through an extension of the interior point strategy. With this approach we develop a reliable and eÆcient algorithm to adjust elements to track optimal control pro le breakpoints and to ensure accurate state and control pro les. This approach is also demonstrated on a dynamic optimization for two distillation columns.