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Advances in MINLP to Identify Energy-Efficient Distillation Configurations

Author

Listed:
  • Radhakrishna Tumbalam Gooty

    (Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907)

  • Rakesh Agrawal

    (Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907)

  • Mohit Tawarmalani

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47907)

Abstract

In this paper, we describe the first mixed-integer nonlinear programming (MINLP)-based solution approach that successfully identifies the most energy-efficient distillation configuration sequence for a given separation. Current sequence design strategies are largely heuristic. The rigorous approach presented here can help reduce the significant energy consumption and consequent greenhouse gas emissions by separation processes. First, we model discrete choices using a formulation that is provably tighter than previous formulations. Second, we highlight the use of partial fraction decomposition alongside reformulation-linearization technique (RLT). Third, we obtain convex hull results for various special structures. Fourth, we develop new ways to discretize the MINLP. Finally, we provide computational evidence to demonstrate that our approach significantly outperforms the state-of-the-art techniques.

Suggested Citation

  • Radhakrishna Tumbalam Gooty & Rakesh Agrawal & Mohit Tawarmalani, 2024. "Advances in MINLP to Identify Energy-Efficient Distillation Configurations," Operations Research, INFORMS, vol. 72(2), pages 639-659, March.
    • Handle: RePEc:inm:oropre:v:72:y:2024:i:2:p:639-659
    DOI: 10.1287/opre.2022.2340
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