The quantification of water usage in a South African platinum refinery using various water accounting methods

A dissertation submitted to the Faculty of Engineering and Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, Johannesburg 2018South Africa is the darling of the platinum world with majority of the global platinum reserves being located in its backyard. Despite boasting extensive platinum mining activity, South Africa contrastingly has limited water resources. Additional pressure is placed on existing water resources due to climate change, poor water infrastructure and greater urbanisation. Hence water management in the mining sector, particularly the platinum mining sector is of great significance. Platinum precious metal refineries are often neglected in terms of water related studies as they are comparatively smaller than other components involved in platinum production, such as platinum mines, hence the significance of this study as a means to increase awareness about platinum PMRs. Accurate accounting of water usage in mining operations is necessary if water is to be effectively managed and minimised. Two water accounting methods were employed to evaluate water usage in a South African platinum precious metal refinery, namely the Water Accounting Framework and Water Footprint Network method. Flowrates and rainfall data were provided by the refinery, whilst evaporation data was obtained from the South African Department of Water and Sanitation. This information along with the appropriate assumptions was used to generate a comprehensive water account for the refinery. The Water Accounting Framework found the volume of the total water inputs into the refinery to be 48.51 ML/year and the total volume of water outputs from the refinery is about 0.99 of the volume of the total inputs. The Water Footprint Network method found the total water footprint to be 49086.07 m3 /year or 49.09 ML /year, comparable to the results of the Water Accounting Framework. The total water footprint was equivalent to the blue water footprint. The total product water footprint of the refinery being valued at 1.20 m3/kg PGM was found to be greater than that of base metal refineries. After viable recommendations were taken into consideration the total product water footprint was reduced by 25%.XL201

Long short term memory water quality predictive model discrepancy mitigation through genetic algorithm optimisation and ensemble modeling

A predictive long short-term memory (LSTM) model developed on a particular water quality dataset will only apply to the dataset and may fail to make an accurate prediction on another dataset. This paper focuses on improving LSTM model tolerance by mitigating discrepancies in model prediction capability that arises when a model is applied to different datasets. Two predictive LSTM models are developed from the water quality datasets, Baffle and Burnett, and are optimised using the metaheuristic genetic algorithm (GA) to create hybrid GA-optimised LSTM models that are subsequently combined with a linear weight-based technique to develop a tolerant predictive ensemble model. The models successfully predict river water quality in terms of dissolved oxygen concentration. After GA-optimisation, the RMSE values of the Baffle and Burnett models decrease by 42.42% and 10.71%, respectively. Furthermore, two ensemble models are developed from the GA-hybrid models, namely the average ensemble and the optimal weighted ensemble. The GA-Baffle RMSE values decrease by 5.05% for the average ensemble and 6.06% for the weighted ensemble, and the GA-Burnett RMSE values decrease by 7.84% and 8.82%, respectively. When tested on unseen and unrelated datasets, the models make accurate predictions, indicating the applicability of the models in domains outside the water sector. The consistent and similar performance of the models on any dataset illustrates the successful mitigation of discrepancies in the predictive capacity of individual LSTM models by the proposed ensemble scheme. The observed model performance highlights the datasets on which the models could potentially make accurate predictions.In part by the Department of Science and Innovation-Council for Scientific and Industrial Research (DSI-CSIR)-Inter-bursary Support Programme; and in part by the National Research Foundation (NRF), South Africa.https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639Electrical, Electronic and Computer Engineerin