Delay Compensation in a Feeder-Conveyor System Using the Smith Predictor: A Case Study in an Iron Ore Processing Plant

Sensors (Basel). 2024 Jun 14;24(12):3870. doi: 10.3390/s24123870.

Abstract

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.

Keywords: Smith predictor; conveyor belt; dead time; feeder; mining; process control.

Grants and funding

Thiago A. M. Euzébio’s work has been partially funded by the project Investigação e experimentação tecnológica para infraestrutura de aplicações em plataformas de sensoriamento inteligente supported by CENTRO DE COMPETÊNCIA EMBRAPII VIRTUS EM HARDWARE INTELIGENTE PARA INDÚSTRIA, with financial resources from the PPI HardwareBR of the MCTI grant number 055/2023, signed with EMBRAPII. Thiago A. M. Euzébio was also funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) through grant 306394/2021-9. The APC was funded by Helmholtz-Zentrum Dresden-Rossendorf.