Skip to main content
Convergence Depth Control for Process System Optimization
Industrial & Engineering Chemistry Research (2007)
  • Kexin Wang, Zhejiang University
  • Zhijiang Shao, Zhejiang University
  • Zhengjiang Zhang, Zhejiang University
  • Zhiqiang Chen, Zhejiang University
  • Xueyi Fang, Zhejiang University
  • Zhou Zhou, Zhejiang University
  • Xi Chen, Zhejiang University
  • Jixin Qian, Zhejiang University
Convergence and solution time are important considerations in process system optimization. Another nontrivial task is the definition of termination criteria. However, setting the convergence tolerance is difficult and bewildering for users. Observed behaviors of algorithms when solving many optimization problems include tardiness in deciding convergence or failure of the optimization, and incapability of giving approximate solutions as they fail to converge. Here, we propose convergence depth control (CDC) for process system optimization. It is designed to take advantage of the achievement estimation of the optimization process to discover the proper time to terminate the optimization algorithm. Criteria based on CDC prefer to provide an approximate solution with acceptable optimality. Achievability and rationality of the criteria have been analyzed. To demonstrate the effectiveness of this method, we apply the Reduced-Hessian Successive Quadratic Programming (RSQP) algorithm with convergence depth control and with traditional convergence criteria, respectively, to problems from the CUTE test set, the distillation sequence in ethylene production, and catalyst mixing problem in COPS collection. Numerical results of the comparison show significant advantages of convergence depth control.
Publication Date
Citation Information
Kexin Wang, Zhijiang Shao, Zhengjiang Zhang, Zhiqiang Chen, et al.. "Convergence Depth Control for Process System Optimization" Industrial & Engineering Chemistry Research Vol. 46 Iss. 23 (2007)
Available at: