Derived heuristics-based consistent optimization of material flow in a gold processing plant. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Myburgh, Christie; Deb, Kalyanmoy
• Is Part Of: Engineering optimization. Volume 50:Issue 1(2018)
• Page Start: 1
• Page End: 18
• Publication Date: 2018-01-02
• Subjects: Derived heuristics -- genetic algorithms -- real-world application -- constrained optimization -- knowledge discovery; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2017.1296436
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: Material flow in a chemical processing plant often follows complicated control laws and involves plant capacity constraints. Importantly, the process involves discrete scenarios which when modelled in a programming format involves if–then–else statements. Therefore, a formulation of an optimization problem of such processes becomes complicated with nonlinear and non-differentiable objective and constraint functions. In handling such problems using classical point-based approaches, users often have to resort to modifications and indirect ways of representing the problem to suit the restrictions associated with classical methods. In a particular gold processing plant optimization problem, these facts are demonstrated by showing results from MATLAB®'s well-known fmincon routine. Thereafter, a customized evolutionary optimization procedure which is capable of handling all complexities offered by the problem is developed. Although the evolutionary approach produced results with comparatively less variance over multiple runs, the performance has been enhanced by introducing derived heuristics associated with the problem. In this article, the development and usage of derived heuristics in a practical problem are presented and their importance in a quick convergence of the overall algorithm is demonstrated.