Advances in simultaneous strategies for dynamic process optimization

Lorenz T. Biegler*, Arturo M. Cervantes, Andreas Wächter

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

363 Scopus citations


Following on the popularity of dynamic simulation for process systems, dynamic optimization has been identified 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 filter-based line search method as well as preconditioned conjugate gradient method for computing search directions for control variables. This leads to a significant 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 finite elements through an extension of the interior point strategy. With this strategy we develop a reliable and efficient algorithm to adjust elements to track optimal control profile breakpoints and to ensure accurate state and control profiles. This is demonstrated on a dynamic optimization for two distillation columns. Finally, these algorithmic improvements allow us to consider a broader set of problem formulations that require dynamic optimization methods. These topics and future trends are outlined in the last section.

Original languageEnglish (US)
Pages (from-to)575-593
Number of pages19
JournalChemical Engineering Science
Issue number4
StatePublished - Feb 14 2002


  • Collocation
  • Dynamic optimization
  • Interior point
  • Moving finite elements
  • Nonlinear programming

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering


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