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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)

Keyword

Multi-Objective Optimization

Individual Discipline Feasible

Multi-Disciplinary Optimization

Simulations

Modelling

Comminution

Mineral Processing

Genetic Algorithm

Publication and Content Type

kon (subject category)

ref (subject category)