Optimization of comminution circuit simulations based on genetic algorithms search method

Comminution simulators are extensively used by mineral processing engineers for plant design and optimization purposes. Recently, there had been a great progress in developing new and more powerful optimization methods such as Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Tabu Search Algorithm (TSA). Simulation optimization is required if one wants to find the best steady-state values of important process variables. In this paper, the authors investigated the integration of GA optimization algorithm with a pre-existing grinding circuit simulator called Ball Milling Circuits Simulator (BMCS) in MATLAB™ environment. The BMCS code has been written in ANSI C language and has been validated against real industrial grinding circuit data sets. Various C modules of the BMCS grinding software were restructured under a new single source code file so that it can be imported into MATLAB. Then, a number of input simulation data were identified and selected as possible process variables (e.g., solids flow rate, water addition rate, and number of operating cyclones) which must be optimized in order to achieve a pre-defined process objective (e.g., a specific d 80 of circuit output). The obtained results show that BMCS simulation trials can be successfully optimized by applying evolutionary algorithms via MATLAB toolboxes. This allows the mineral processor to perform automatic repetitive simulations to find the possible solutions of the problem at hand quickly.