Work

Generalized optimization framework for synthesis of thermally coupled distillation columns

Year: 2025

Type: article

Abstract: In this article, a generalized optimization framework is proposed for the synthesis of thermally coupled distillation systems within an equation‐oriented environment. The proposed framework consists of three components: an efficient superstructure representation, a novel mathematical formulation, and the associated solution algorithm, encompassing a broad range of alternatives. The mathematical model is developed using conditional statements to activate specific sets of equations, effectively addressing existing zero‐flow issues. The synthesis problem is formulated as a Mixed Integer Nonlinear Programming problem, which is optimized using our previously developed Feasible Path‐Based Branch and Bound method, coupled with an improved Sequential Quadratic Programming algorithm. The computational studies demonstrate that the proposed optimization framework successfully solves complex benchmark problems for separating zeotropic multicomponent mixtures within reasonable computational time with good convergence performance from easily selected starting points. The optimal configuration generated leads to a reduction in total annualized cost ranging from 3.5% to 45%.

Source: AIChE Journal

DOI: 10.1002/aic.18776

Authors: Chao Liu, Yingjie Ma, Jie Li

Institutions: University of Manchester, Massachusetts Institute of Technology

Language: English

Topic: Process Optimization and Integration

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,940

Funder: University of Manchester