Dynamic Model of Semi-autogenous (SAG) Mill System

Yuwen, Cheng; Liu, Shuai; Sun, Bo

doi:10.1109/icca.2019.8899651

Cited by 1 publication

(1 citation statement)

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“…This input parameters are controlled in order to control the weight of the SAG mill, the power draw and other SAG mill throughput contributing factors. The SAG mill grinding process is a closed-circuit process that is increasingly complex to control effectively and efficiently [3]. The complexity around the SAG mill is due to unmeasured disturbance factors, process delays and the coupling and interaction between process variables.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Performance Evaluation of a Model Predictive Controller for A Semi-Autogenous (Sag) Mill

Shende,

Simon

2024

Adv. Sci. Technol. Res. J.

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This paper investigates the implementation of a model-based predictive control (MPC) strategy to improve the performance of a semi-autogenous grinding (SAG) mill in a uranium mineral processing plant. The SAG mill, crucial in crushing and grinding uranium ore to the desired size, is currently managed using conventional proportionalintegral-derivative (PID) controllers. However, to enhance production efficiency and control over the SAG mill's variables, this paper suggests the adoption of MPC. The proposed MPC controller is developed using a neural network (NN) model of the SAG mill, created in MATLAB with data collected over 21 days. The effectiveness of the MPC controller is assessed by contrasting its response with that of the real-time operator control. This comparison utilizes tools like MATLAB and the RSlinx remote server for accessing OPC real-time data. Findings reveal that the MPC controller exhibits a quicker reaction to alterations in the SAG mill's process outputs and proficiently regulates crucial outputs such as Mill mass, ensuring that the manipulated variables stay within their designated limits. Unlike operator control, which is slower and adjusts one variable at a time, the MPC approach can maximize the mill's throughput rate without impacting the ore feed rate. This demonstrates the MPC controller's superior ability to optimize SAG mill operations efficiently.

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Section: Introductionmentioning

confidence: 99%