A feasible path-based branch and bound algorithm for strongly nonconvex MINLP problems

Abstract

In this paper, a feasible path-based branch and bound (B&B) algorithm is proposed to solve mixed-integer nonlinear programming problems with highly nonconvex nature through integration of the previously proposed hybrid feasible-path optimisation algorithm and the branch and bound method. The main advantage of this novel algorithm is that our previously proposed hybrid steady-state and time-relaxation-based optimisation algorithm is employed to solve a nonlinear programming (NLP) subproblem at each node during B&B. The solution from a parent node in B&B is used to initialize the NLP subproblems at the child nodes to improve computational efficiency. This approach allows circumventing complex initialisation procedure and overcoming difficulties in convergence of process simulation. The capability of the proposed algorithm is illustrated by several process synthesis and intensification problems using rigorous models.

Integrated process design and control of divided‐wall distillation columns using molecular tracking

“…5 A drawback to this reduction, however, is the increased complexity in the design of thermally coupled distillation columns due to the reduced number of degrees of freedom compared to other conventional schemes. 6 This makes the optimal design and control of this unit operation challenging 7 due to the integration of tasks and phenomena.…”