Sequential Bound Constrained Minimization Method for Solving Complex Process System Optimization Problems

Xiang  Li; Chunyan  Li

doi:10.6180/jase.202607_30.036

Sequential Bound Constrained Minimization Method for Solving Complex Process System Optimization Problems

Xiang Li¹This email address is being protected from spambots. You need JavaScript enabled to view it. and Chunyan Li²

¹School of Engineering, Guangzhou College of Technology and Business, Guangzhou, China

²Library, Yulin Normal University, Yulin, China

Received: December 31, 2023
Accepted: June 2, 2025
Publication Date: January 19, 2026

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202607_30.036

Steady-state optimization of complex process systems in engineering practice is a typical class of large-scale optimization problems. In this paper, we propose a new method, Sequential Bound Constrained Minimization (SBCM) method, for solving such optimization problems. Specifically, by introducing relaxation variables properly into engineering optimization model, the derived penalty function encompasses only penalty items related to equality constraints, excluding those pertaining to bound and inequality constraints. The solution to the original optimization problem is obtained through solving a series of bound constrained subproblems instead of a series of unconstrained subproblems. Furthermore, a modified truncated-Newton method is provided to solve the bound constrained subproblems. Finally, numerical simulations are conducted on two nonlinear optimization problems with varying dimensions and the simulation results demonstrate the stability and effectiveness of the proposed method.

Keywords: Steady-state optimization; penalty function; minimization method; numerical simulation

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