Hybrid non-linear model predictive control of a run-of-mine ore grinding mill circuit

Botha, Stefan; Roux, J.D. le; Craig, I.K.

doi:10.1016/j.mineng.2018.04.016

Cited by 28 publications

(17 citation statements)

References 43 publications

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“…An HMPC controller using a GA to solve for the optimal control action (similar to [18]) is implemented to determine optimal lockdown levels for different policy scenarios. The controlled variable is the active number of confirmed infectious cases: Q as given by (4).…”

Section: Controller Designmentioning

confidence: 99%

Optimized Lockdown Strategies for Curbing the Spread of COVID-19: A South African Case Study

2020

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To curb the spread of COVID-19, many governments around the world have implemented tiered lockdowns with varying degrees of stringency. Lockdown levels are typically increased when the disease spreads and reduced when the disease abates. A predictive control approach is used to develop optimized lockdown strategies for curbing the spread of COVID-19. The strategies are then applied to South African data. The South African case is of interest as the South African government has defined five distinct levels of lockdown, which serves as a discrete control input. An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios. Scenarios considered include how to flatten the curve to a level that the healthcare system can cope with, how to balance lives and livelihoods, and what impact the compliance of the population to the lockdown measures has on the spread of COVID-19. The main purpose of this article is to show what the optimal lockdown level should be given the policy that is in place, as determined by the closed-loop feedback controller.

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Section: Controller Designmentioning

confidence: 99%

Optimized Lockdown Strategies for Curbing the Spread of COVID-19: A South African Case Study

2020

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“…(3) The paper introduces an allocation method of the attribute weights, based on NSGA-II, to make it efficacious and precise to multiple case solutions. (4) The method proposed in this paper was experimented in a dressing plant and achieved satisfactory results, which provides experience for subsequent studies.…”

Section: Introductionmentioning

confidence: 82%

“…With the development of new intelligent modeling and control methods, many intelligent modeling methods have been successfully implemented in the modeling process of grinding processing. Recently, the advanced control methods based on model predictive control [2][3][4] or supervisory control [5,6] for the grinding process have been discussed. It is worth noting that the results mentioned above are stringently based on the mathematical models of the system.…”

Section: Introductionmentioning

confidence: 99%

Optimal-Setting for Ore and Water Feeding in Grinding Process Based on Improved Case-Based Reasoning

et al. 2021

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The grinding product particle size is the most crucial operational index of mineral grinding processes. The size and consistency of the product directly affects the subsequent dressing and sintering. In this paper, a novel expert system is proposed for guiding the operating variables to keep the product stable with the wildly varying ore properties. First, case-based reasoning (CBR) is introduced to describe the whole grinding process with the historical data and expert experience. Second, the generative adversarial network (GAN) is employed to extend the raw data to enhance the flexibility of CBR. Moreover, the weights of different features in CBR is optimized by improved non-dominated sorting genetic algorithm II (NSGA-II). Finally, the proposed method is validated by a set of actual data collected from a Chinese dressing plant. The experimental result demonstrates the effectiveness of the proposed method.

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“…The cluster of cyclones is modeled as a single classifier. If needed, the model can be expanded into separate smaller cyclones as in Botha et al [25]. However, the goal here is simply to calculate the total water, solids, and fines split at the cluster.…”

Section: Cyclone Cluster Modelmentioning

confidence: 99%

Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods

et al. 2021

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With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling.

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Hybrid non-linear model predictive control of a run-of-mine ore grinding mill circuit

Cited by 28 publications

References 43 publications

Optimized Lockdown Strategies for Curbing the Spread of COVID-19: A South African Case Study

Optimized Lockdown Strategies for Curbing the Spread of COVID-19: A South African Case Study

Optimal-Setting for Ore and Water Feeding in Grinding Process Based on Improved Case-Based Reasoning

Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods

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