Derived heuristics based consistent opti

Material flow in a chemical processing plant often follows complicated control laws and involve plant capacity constraints. Importantly, the process involves discrete scenarios which when modeled 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 fix-ups and indirect ways of representing the problem to suit the restrictions associated with classical methods. In a particular gold processing plant optimization problem, we demonstrate these facts by showing results from MATLAB’s well-known fmincon routine. Thereafter, we develop a customized evolutionary optimization procedure which is capable of handling all complexities offered by the problem. 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 paper, we demonstrate the development and usage of derived heuristics in a practical problem and show their importance in a quick convergence of the overall algorithm.