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Homotopy Continuation Enhanced Branch and Bound Algorithms for Process Synthesis Using Rigorous Unit Operation Models
  • Yingjie Ma,
  • Jie Li
Yingjie Ma
The University of Manchester

Corresponding Author:[email protected]

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Jie Li
University of Manchester
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Process synthesis using rigorous unit operation models is highly desirable to identify the most efficient pathway for sustainable production of fuels and value-added chemicals. However, it often leads to a large-scale strongly nonlinear and nonconvex mixed integer nonlinear programming (MINLP) model. In this work, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB-FP and HCBB-RB) where the homotopy continuation method is employed to gradually approach the optimal solution of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB-RB is superior to HCBB-FP due to lower computational effort required for the same locally optimal solution.
15 Sep 2021Submitted to AIChE Journal
19 Sep 2021Submission Checks Completed
19 Sep 2021Assigned to Editor
06 Oct 2021Reviewer(s) Assigned
04 Nov 2021Editorial Decision: Revise Major
04 Jan 20221st Revision Received
04 Jan 2022Submission Checks Completed
04 Jan 2022Assigned to Editor
06 Jan 2022Reviewer(s) Assigned
16 Jan 2022Editorial Decision: Accept