Integrated Scheduling and Dynamic Optimization of Sequential Batch Processes with Online Implementation

Yunfei Chu, Fengqi You*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

An efficient decomposition method to solve the integrated problem of scheduling and dynamic optimization for sequential batch processes is proposed. The integrated problem is formulated as a mixed-integer dynamic optimization problem or a large-scale mixed-integer nonlinear programming (MINLP) problem by discretizing the dynamic models. To reduce the computational complexity, we first decompose all dynamic models from the integrated problem, which is then approximated by a scheduling problem based on the flexible recipe. The recipe candidates are expressed by Pareto frontiers, which are determined offline by using multiobjective dynamic optimization to minimize the processing cost and processing time. The operational recipe is then optimized simultaneously with the scheduling decisions online. Because the dynamic models are encapsulated by the Pareto frontiers, the online problem is a mixed-integer programming problem which is much more computationally efficient than the original MINLP problem, and allows the online implementation to deal with uncertainties.

Original languageEnglish (US)
Pages (from-to)2379-2406
Number of pages28
JournalAICHE Journal
Volume59
Issue number7
DOIs
StatePublished - Jul 1 2013

Keywords

  • Decomposition
  • Integrated scheduling and dynamic optimization
  • Mixed-integer dynamic optimization
  • Mixed-integer nonlinear programming
  • Multiobjective dynamic optimization

ASJC Scopus subject areas

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering(all)

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