Advances in MINLP to Identify Energy-Efficient Distillation Configurations

Radhakrishna Tumbalam Gooty (), Rakesh Agrawal () and Mohit Tawarmalani ()
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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

Operations Research, 2024, vol. 72, issue 2, 639-659

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.

Keywords: Optimization; multicomponent distillation; fractional program; reformulation-linearization technique (RLT); piecewise relaxation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:72:y:2024:i:2:p:639-659

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