Comparative Study of Optimization Schemes in Mineral Processing Simulations

"Modelling and simulations for mineral processing plants have been successful in replicating and predicting predefined scenarios of an operating plant. However, there is a need to explore and increase the potential of such simulations to make them attractive for users. One of the tools to increase the attractiveness of the simulations is through applying optimization schemes. Optimization schemes, applied on mineral processing simulations, can identify non-intuitive solutions for a given problem. The problem definition itself is subjective in nature and is dependent on the purpose of the operating plant. The scope of this paper is to demonstrate two optimization schemes: Multi-Objective Optimization (MOO) using a Genetic Algorithm (GA) and Multi-Disciplinary Optimization (MDO) using an Individual Discipline Feasible (IDF) approach. A two stage coarse comminution plant is used as a case plant to demonstrate the applicability of the two optimization schemes. The two schemes are compared based on the problem formulations, types of result and computation time. Results show that the two optimization schemes are suitable in generating solutions to a defined problem and both schemes can be used together to produce complementary results. INTRODUCTION The comminution process for a mineral processing plant consists of multiple crushing and classification stages and aims to produce fine material to liberate ore from the minerals. The modelling and simulation of such processes are well established using static process models (King, 2001; Napier-Munn et al., 1996). A recent development in the dynamic modelling for such processes has shown further increase in fidelity compared to the static process models (Asbjörnsson, 2015). The developed simulations can be utilized to design, operate and control mineral processing plants based on the requirements of users (Bhadani et al., 2017). Typically, the requirements in the operation of the comminution process are conflicting in nature, as for instance, reduction of power draw by process units, maximization of production of fine materials, and increasing utilization of the process units. These conflicting requirements leads to a Multi-Objective Optimization (MOO) problem to operate a mineral processing plant. The MOO problems are normally referred to as non-linear problems and its fundamental understanding with respect to process behaviour is difficult to achieve."