Apply Advanced Process Control to Fine Screening Circuit in Large Mineral Processing Plant

Kapor, Alex (2021) Apply Advanced Process Control to Fine Screening Circuit in Large Mineral Processing Plant. [USQ Project]

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KAPOR Alex dissertation_redacted.pdf

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This research project focuses on improving the control of a fine-ore screening circuit in a large gold processing plant. At this plant, ore is classified across a set of eight vibrating screens in which the undersize material reports to four ball mills and the oversize material is returned for further crushing. While the feed-rates to each individual fine screen and the total oversize return is known, there is a lack of instrumentation to reliably measure individual screen oversize. Inadequate classification of ore is an often-overlooked cause of poor mill performance (Rotich et al. 2016, p. 3889) and by obtaining a better understanding of individual screen performance, the impact on production can be mitigated.

Through the development of an appropriate model, a parameter estimation problem is derived where the efficiency of the eight vibrating screens can be estimated via regression of historical data, allowing the screen oversize to be predicted. The performance of multiple algorithms for parameter estimation were then assessed in terms of accuracy, speed and repeatability. The most appropriate algorithm was selected to form the basis of an online estimation program, developed in Matlab®. The fastest algorithm was a simple least squares fit, though a quadratic programming method was ultimately selected as it allowed bounds to be set on the parameters.

While the implementation performs well over longer timeframes it can be unreliable at shorter intervals when variance in feed is low. Further modification to the algorithm and the introduction of regular system excitation is recommended. A further result of the analysis is the development an additional algorithm to maximise the mill feed based on the oversize model output. This research demonstrates how parameter estimation techniques can be employed using historical and real-time data to form inferential measurement points. While the model needs some refinement, the approach proved to be a viable method with future applicability.

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Item Type: USQ Project
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Supervisors: Hills, Catherine
Qualification: Bachelor of Engineering (Instrumentation, Control and Automation)
Date Deposited: 03 Jan 2023 02:43
Last Modified: 26 Jun 2023 01:33
Uncontrolled Keywords: gold, fine-ore, screening, parameter estimation

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