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Conveyor belts serve as the primary mode of ore transportation in mineral processing plants. Feeders, comprised of shorter conveyors, regulate the material flow from silos to longer conveyor belts by adjusting their velocity. This velocity manipulation is facilitated by automatic controllers that gauge the material weight on the conveyor using scales. However, due to positioning constraints of these scales, a notable delay ensues between measurement and the adjustment of the feeder speed. This dead time poses a significant challenge in control design, aiming to prevent oscillations in material levels on the conveyor belt. This paper contributes in two key areas: firstly, through a simulation-based comparison of various control techniques addressing this issue across diverse scenarios; secondly, by implementing the Smith predictor solution in an operational plant and contrasting its performance with that of a single PID controller. Evaluation spans both the transient flow rate during step change setpoints and a month-long assessment. The experimental results reveal a notable increase in production by 355 t/h and a substantial reduction in flow rate oscillations on the conveyor belt, evidenced by a 55% decrease in the standard deviation.

In the mineral sector, many processes use water for ore beneficiation processes. A lack of sensing or control of water content can lead to operational problems in various mineral processing operations, especially in ore transport. Current instrumentation systems are either slow or inaccurate. Therefore, a novel bench system was developed to address this gap by achieving a fast response time and improved accuracy. The developed instrument measures the ore moisture by using the real-dual-frequency method (RDFM) to assess the ore’s electrical conductivity and relative permittivity. Additionally, it takes into account the bulk density, the bench chamber level, and the compress torque. All these variables are used to create a tiny machine-learning (TinyML) model that evaluates the ore’s moisture with a low time response. This process is done while the ore sample is compressed to reduce air bubbles inside the samples and improve measurement. Experiments were performed using the bench system in a mining company’s physical analysis laboratory. The instrument was utilized to measure the moisture content in the ore, leading to the development of a dataset used to train and validate various tree-based tinyML models. The results indicate that ore compression enhances accuracy and that decision trees are effective for estimating moisture with a quicker response time.

Crushing is a critical operation in mineral processing, and its efficient performance is vital for minimizing energy consumption, maximizing productivity, and maintaining product quality. However, due to variations in feed material characteristics and safety constraints, achieving the intended circuit performance can be challenging. In this study, a centralized control strategy based on a finite state machine (FSM) is developed to improve the operations of an iron ore crushing circuit. The aim is to increase productivity by manipulating the closed-side-setting (CSS) of cone crushers and the speed of an apron feeder while considering intermediate storage silo levels and cone crusher power limits, as well as product quality. A dynamic simulation was conducted to compare the proposed control strategy with the usual practice of setting CSS to a constant value. Four scenarios were analyzed based on variations in bond work index (BWI) and particle size distribution. The simulation results demonstrate that the proposed control strategy increased average productivity by 6.88% and 48.77% when compared to the operation with a constant CSS of 38 mm and 41 mm, respectively. The proposed strategy resulted in smoother oscillation without interlocking, and it maintained constant flow rates. This ultimately improved circuit reliability and predictability, leading to reduced maintenance costs.

Wet High-Intensity Magnetic Separators (WHIMSs) have become well established in mineral processing operations by efficiently separating particles based on their magnetic properties. While the existing literature extensively discusses WHIMS performance under varying operational conditions, there is a notable absence of attention to the degradation of coils over time and methods for its detection. In this paper, we address this gap by proposing a novel approach to detect coil degradation in WHIMSs, enabling timely maintenance interventions to maintain optimal performance. Through experimental analysis in an industrial environment, we show the significant effect of coil deterioration on WHIMS efficiency. We also introduce a real-time monitoring method using current measurements in coil sets. This method provides a practical solution for identifying and addressing coil degradation, helping to improve maintenance practices and sustain operational efficiency in mineral processing facilities. The experiments were conducted in a Brazilian iron ore processing company in Itabira, Minas Gerais.

Grinding operations, especially those involving semi-autogenous mills, account for a significant portion of energy use in mineral processing. In this work, we describe the application of an advanced regulatory control strategy in a copper plant aimed at improving energy efficiency through automation. The system combines cascade and feedforward control structures to attenuate variations in the mill load, a key factor influencing energy consumption and process stability. The control scheme was integrated into the plant’s existing automation infrastructure and evaluated through a three-month industrial trial. By shifting from manual to automatic regulation of the feed rate, the plant reduced the influence of process disturbances and maintained more consistent operation. The automated system achieved a 5.84% reduction in specific energy consumption and a 1.90% increase in productivity. These results demonstrate the potential of enhanced regulatory control to deliver measurable performance gains with minimal changes to existing operations.

In this paper, we present a cost-effective system for monitoring and controlling alkaline electrolyzers, intending to improve hydrogen gas production on a laboratory scale. Our work includes two main innovations. Firstly, we suggest an approach to calibrate a standard air flow meter to accurately measure the flow of hydrogen-rich gas from electrolyzers, improving measurement accuracy while keeping costs low. Secondly, we introduce a unique cascade control method to manage hydrogen-rich gas production in the electrolyzer, ensuring precise control over gas flow rates. By combining affordable, energy-efficient devices with a PI control system, we achieve efficient gas production through electrolysis, replacing manual control approaches. Experimental results confirm the effectiveness of our cascade control method, demonstrating stable operation with minimal errors. These results provide a foundation for further research into control strategies to enhance the performance of electrolytic cells.

High water usage is necessary while ore passes through the many stages of a mineral processing plant. However, a dewatering system filters the final ore pulp product to remove the water, which is reutilized in the previous processes. This step is fundamental to reducing the fresh new water consumption. Usually, several tanks, pumps, and filters form a dewatering system—any failure or shutdowns from those components disbalance the pulp flow. The waste of many tons of water and ore products for a tailing dam is the worst consequence of a mass disbalance in a dewatering system. This paper proposes an advanced regulatory control strategy composed of cascade and override loops for a dewatering system. The main purpose is to increase the production period, even under filter failure and changes in the inlet pulp characteristics. This control strategy is evaluated using a digital model of a large-scale Brazilian iron ore processing plant. Two scenarios are investigated: the simultaneous failure of two filters and disturbances in the flow and density of the thickener. The simulation results show that the proposed control strategy could extend the period of operation of the dewatering plant under failures in the disc filters and reject significant disturbances. For the considered simulation period, the proposed solution increases the time to overflow by 72% when compared to the previous control strategy. Thus, it is possible to avoid the waste of approximately 2448.36 tons of ore pulp that would be sent to the tailings dam.

This paper reports the calibration and validation of a cone crusher model using industrial data. Usually, there are three calibration parameters in the condensed breakage function; by contrast, in this work, every entry of the lower triangular breakage function matrix is considered a calibration parameter. The calibration problem is cast as an optimization problem based on the least squares method. The results show that the calibrated model is able to fit the validation datasets closely, as seen from the low values of the objective function. Another significant advantage of the proposed approach is that the model can be calibrated on data that are usually available from industrial operation; no additional laboratory tests are required. Calibration and validation tests on datasets collected from two different mines show that the calibrated model is a strong candidate for use in various dynamic simulation applications, such as control system design, equipment sizing, operator training, and optimization of crushing circuits.

In the mineral sector, many processes use water for ore beneficiation processes. A lack of sensing or control of water content can lead to operational problems in various mineral processing operations, especially in ore transport. Current instrumentation systems are either slow or inaccurate. Therefore, a novel bench system was developed to address this gap by achieving a fast response time and improved accuracy. The developed instrument measures the ore moisture by using the real-dual-frequency method (RDFM) to assess the ore’s electrical conductivity and relative permittivity. Additionally, it takes into account the bulk density, the bench chamber level, and the compress torque. All these variables are used to create a tiny machine-learning (TinyML) model that evaluates the ore’s moisture with a low time response. This process is done while the ore sample is compressed to reduce air bubbles inside the samples and improve measurement. Experiments were performed using the bench system in a mining company’s physical analysis laboratory. The instrument was utilized to measure the moisture content in the ore, leading to the development of a dataset used to train and validate various tree-based tinyML models. The results indicate that ore compression enhances accuracy and that decision trees are effective for estimating moisture with a quicker response time.