Trade-Off Curves for Performance Optimization in a Crushing Plant

Operational flexibility in an aggregate production process is required to adapt to changes in customer demands. Excessive demand for a particular product fraction can lead to operational alteration wherein re-crushing of the existing larger-sized product fraction is necessary. The choice of re-crushing existing product fractions results in feed condition changes to the crusher. One common approach to producing the desired product is by varying the operation settings of a crusher in a crushing plant. However, knowledge of differences in operational performance for changing feed conditions in the circuit is required. This potentially leads to a problem of performance optimization based on the desired target product, available feed material and capability of the crusher. The paper presents an application of a multi-objective optimization method to generate multiple operational settings for the dynamic change in the operation condition in a crushing plant. Controlled experimental survey data with varying feed conditions are used to calibrate the crusher model using an unconstrained optimization problem solved using a gradient-based algorithm (Quasi-Newton method). Trade-off curves between various performance indicators of the crushing plant using a dynamic simulation platform are generated using multi-objective optimization using a non-gradient-based algorithm (genetic algorithm). The results of the application can help the operators and plant managers to make proactive decisions to steer the operation of the crushing plant towards the desired needs of the operation.