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\begin{document}

\title{Homotopy Continuation Enhanced Branch and Bound Algorithms for Process
Synthesis Using Rigorous Unit Operation Models}



\author[1]{Yingjie Ma}%
\author[2]{Jie Li}%
\affil[1]{The University of Manchester}%
\affil[2]{University of Manchester}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Figures.docx`  available at \url{https://authorea.com/users/439870/articles/540680-homotopy-continuation-enhanced-branch-and-bound-algorithms-for-process-synthesis-using-rigorous-unit-operation-models}

\textbf{Hosted file}

\verb`Tables.docx`  available at \url{https://authorea.com/users/439870/articles/540680-homotopy-continuation-enhanced-branch-and-bound-algorithms-for-process-synthesis-using-rigorous-unit-operation-models}

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