Development of a predictive model to determine the temporal variability in mine feed water quality towards informing and forecasting plant operating strategy – a South African coal mine water treatment plant case study

The feed water quality associated with mine water treatment is typically characterised by a dynamic variability resulting from the fact that the final feed water to the water treatment plant (WTP) can be an amalgamation of water streams emanating from a number of sources. Consequently, the ability to deal with the dynamic nature of the feed water quality towards successful and sustainable mine water treatment goes beyond a proactive approach and requires a systemic, predictive approach. This paper discusses the development of an unsteady state mass balance model on a surface dam located on a coal mine towards predicting the dynamic fluctuations in total dam volume and its total dissolved solids (TDS) concentration in the feed water to a NuWater 20 MLD mobile WTP, comprising chemical conditioning, ultrafiltration and reverse osmosis (RO). The unsteady state mass balance, incorporated water entering the dam via the opencast pits, underground compartments, seasonal rainfall and the RO brine return. Water leaving the dam comprised the feed water to the WTP, partial brine treatment, surface evaporation and seepage. Validation of the model using actual data over an 8-month period showed excellent results. The model showed that without water treatment, the dam would overflow in 218 days. Although the dam's volume could be sustained at the ideal volume by treating 14.2 MLD, its TDS would exceed the maximum environmental limit in 197 days. Consequently, the combination of a 13.2 MLD WTP with a 1 MLD brine treatment plant provided the optimal water treatment strategy to sustainably maintain the dam's TDS concentration and volume within acceptable limits over the 5-year investigation period. This paper demonstrates the importance of using a predictive methodology for forecasting feed water characteristics and as an early warning system for most water treatment systems that are subjected to dynamic conditions.