Grinding Mill Circuits - A Survey of Control and Economic Concerns

Wei, Donghui; Craig, I.K.

doi:10.3182/20080706-5-kr-1001.00171

Cited by 3 publications

(2 citation statements)

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“…System identification of real-world processes is imperative for developing effective process control strategies and precise fault detection and diagnosis methods. A benefit of such identification is enhanced plant stability and performance, which can be achieved by integrating the model with advanced model-based process control, outperforming traditional control methods [7], [35], [40], [43].…”

Section: Introductionmentioning

confidence: 99%

“…One of the primary challenges for modeling (and control) an SAG mill process is the insufficient measurements available to estimate the necessary states and parameters in the model. In general, the number of real-time measurements available is significantly lower than the size of the state vector that needs to be measured [43]. In [4]- [6], the problem of inferential modeling, state estimation, and model validation of an SAG mill is addressed using the extended Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements

Cedeño,

Coronel,

Orellana

et al. 2024

IEEE Access

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In this paper, we have proposed a new paradigm for modeling of SAG mills. Typically, important parameters found in the modeling of such processes are described as state-space system model rather than unknown parameters. Here, we propose to estimate the system model using the maximum likelihood approach. Additionally, we propose using a new measurement that has not been considered in other modeling approaches. The benefits of our proposal are illustrated via numerical simulations. The results demonstrate that incorporating this new measurement within the framework of maximum likelihood estimation improves the accuracy of estimating the unknown parameters.

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