Integrated scheduling and dynamic optimization of complex batch processes with general network structure using a generalized benders decomposition approach

Yunfei Chu, Fengqi You*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

We address the integration of scheduling and dynamic optimization for batch chemical processes. The processes can have complex network structures, allowing material splitting and mixing. The integrated problem is formulated as a mixed-integer dynamic optimization problem where a continuous-time scheduling model is linked to the dynamic models via processing times, processing costs, and batch sizes. To reduce the computational complexity, we develop a tailored and efficient decomposition method based on the framework of generalized Benders decomposition by exploiting the special structure of the integrated problem. The decomposed master problem is a scheduling problem with variable processing times and processing costs, as well as the Benders cuts. The primal problem comprises a set of separable dynamic optimization problems for the processing units. By collaboratively optimizing the process scheduling and the process dynamics, the proposed method substantially improve the overall economic performance of the batch production compared with the conventional sequential method which solves the scheduling problem and the dynamic optimization problems separately. In comparison with the simultaneous method which solves the integrated problem by a general-purpose MINLP solver directly, the proposed method can reduce computational times by orders of magnitude.

Original languageEnglish (US)
Pages (from-to)7867-7885
Number of pages19
JournalIndustrial and Engineering Chemistry Research
Volume52
Issue number23
DOIs
StatePublished - Jun 12 2013

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

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