An optimisation approach to investigate quality control of a product coal stockpile

Abstract

For any coal beneficiation group to reach the full financial potential in coal production, optimal coal cleaning processes are of great importance. The heterogenic nature of coal is the main constraint in producing a constant and accurate coal quality, meeting the client‟s requirements. Aside from the heterogenic nature of coal, inefficient quality control on coal product lines also contributes to a decrease in potential profit. Eliminating causes for inefficient quality control on a semi-soft coking coal production line is the focus of the investigation. The current quality control strategy applied to a coking coal production line under investigation includes an operator using a trial and error method to manage the average ash quality on the coking coal stockpile. In order to reach a predefined ash accumulation set point, the operator is responsible for the manual adjustment of separation densities in five dense medium cyclones. The set point along with several other stockpile properties are calculated using a stockpile building management system, integrating all the appropriate on-line and off-line data from different data repositories. This control strategy among other process inconsistencies contributes to a sub-optimal quality control. The main objective of the project is to investigate the benefits in replacing the manual quality control strategy with an optimised decision support solution able accommodate the operator with optimised SBS outputs to control the coking coal quality more efficiently and with higher throughput. The performance of the optimised solution created, is compared to the performance of the current quality control system. The optimisation solution has the ability to control the ash accumulation around a set point with a smaller variance compared to the current control system. However, the lower throughput in some instances highlights inaccuracies within the optimisation solution. Measurements that are more representative will increase the performance of the optimisation solution.