Browsing by Author "Siwella, Mollin"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- ItemProcess monitoring with economic performance functions : feasibility assessment for milling circuits(Stellenbosch : Stellenbosch University, 2017-12) Siwella, Mollin; Auret, Lidia; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.ENGLISH SUMMARY: Milling circuits are operated with a throughput maximisation objective, consistent with achieving a liberation index that promotes optimal mineral recovery in downstream flotation. To meet this objective, this study investigates the feasibility of developing and using economic performance functions to monitor milling circuits. To this end, industrial and simulation cases were studied. The industrial case study investigated the development of a reliable economic performance function (EPF) that relates controlled variable behaviour to a profit function. To this end, regression models were developed between key controlled variables (mill load, mass pull and particle size) and each of two profit functions, i.e. mineral recovery and financial profit. Subsequently, the development and industrial implementation of a simple and convenient process monitoring tool that assists operators and engineers to make operational decisions based on economic performance predictions was to be assessed. Since fault conditions are unfavourable for profitable operation, the simulation case study assessed the feasibility of fault detection with EPFs. Three faults i.e. increased ore hardness, poor steel ball quality and a load cell drift were simulated and the economic impact of each fault was investigated. Results for the industrial case study showed difficulty to develop a reliable EPF with industrial data. All but four EPF models were poorly fit and showed an adjusted R-squared value below a selected threshold of 0.6. However, the four identified models were characterised with poor predictions of plant test data. Consequently, the results were not useful for developing a process monitoring tool that could be implemented at the industrial operation. A number of factors such as data quality, data pre-treatment and the model structure used in the study influenced industrial case study results. To mention a few of these factors, results suggested robust model predictive controller action that rendered data variability insufficient for EPF development purposes. Indications of poorly structured data due to measurement lags, and disturbances in data sequences from faulty data treatment were additional limitations that influenced the quality of results obtained. Moreover, a low sampling frequency for assay composite samples may have contributed to missing in-between shift events. Simulation case study results showed degraded economic performance at fault inception for two (increased ore hardness and load cell drift) of the simulated faults, to suggest opportunity for fault detection with EPFs. Significance test results pointed to at least one difference in the economic performance indices (EPIs) for the simulated faults. Although not fully explored in this study, significance test results suggested opportunity for fault prioritisation with the EPI that allows decisions about the corrective action to be made based on the severity of the impact on economic performance. Since reliable EPF development was the main limitation in this study, a comparative assessment in a different operation with well-structured data is recommended. Fault prioritisation with the EPI may also be an area of interest for future work. However, the shortcomings identified in some of the simplifying assumptions made when deriving the EPI will need to be addressed.