Predictive Control of the Iron Ore Beneficiation Process Based on the Hammerstein Hybrid Model

Porkuian, Olga; Morkun, Vladimir; Morkun, Natalia; Serdyuk, O.Yu.

doi:10.2478/ama-2019-0036

Cited by 9 publications

(10 citation statements)

References 33 publications

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“…The thickened slurry formed from sediments is removed through the discharge openings and pumped out. The main input signal -a controlled value for automatic stabilization of the thickener -is the density of the thickener overflow slurry (or initial concentration), and the density of the overflow slurry (or initial concentration) is the main signal to optimize the unit [25,26]. Main disturbances of the process are changes in concentration and volume of the feed slurry.…”

Section: Presentation Of Materials and Resultsmentioning

confidence: 99%

Formation of the separation characteristic of ore material thickening based on the model predictive control method

Morkun

Tron

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The aim of the investigation is to form an optimal separation characteristic of beneficiation processes on the basis on operational information on time-varying of their parameters exemplified by the process of thickening ore raw materials. Methods of research. In the research work, the following methods are used: analysis of scientific research and practical experience; statistics methods and the probability theory for estimation of experiment results; methods of analytical synthesis and numerical simulation; methods of model predictive control for developing control algorithms of the thickening process; numerical simulation methods for synthesizing and analyzing a mathematical model. The scientific novelty of the investigation is in finding optimal values of the control horizon and the prediction horizon in terms of quality control for a single-channel system of model predictive control of ore raw material thickening. Practical significance involves development of methods and software for determining the control horizon and the prediction horizon values of the single-channel system of model predictive control of the process of ore raw material thickening that are optimal from the point of view of quality control, this enabling optimization of separation characteristics of ore raw material thickening. Results To form a separation characteristic of the process of ore raw material thickening based on model predictive control for the single-channel control system of the thickening process, satisfactory control results are achieved by setting the control horizon equal to one interval. For this value, the quadratic control error does not exceed 0.1452-0.1474. A further increase in the prediction horizon is not feasible since it does not allow significant reduction of the quadratic control error. At the same time, the value of 3-5 intervals is sufficient for prediction horizons. These values are determined by an increase in computational complexity of prediction by 10-20 intervals, which causes a slight decrease in the quadratic control error.

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Section: Presentation Of Materials and Resultsmentioning

confidence: 99%

Formation of the separation characteristic of ore material thickening based on the model predictive control method

Morkun

Tron

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…The first unit of the process line -usually a ball mill of wet grinding [19] -is fed with raw materials of 𝑄 0 (Р) volume with certain qualitative characteristics 𝜉 ̅ 0 . These characteristics include [23,26]: distribution of raw materials by size; distribution of Fetot and Femag content by size classes. Intermediate concentration product produced at the output of the first technological unit is a raw material for the next unit or it is partially returned to the previous unit for additional processing.…”

Section: Methodsmentioning

confidence: 99%

“…The resulting separation characteristic of ore materials with a working point in the optimal cut-point is an essential indicator of improving the technological line of ore concentration [21,22]. In technological flows of the ore concentration line at various stages, critically different types of ore materials in terms of their physicalmechanical and chemical-mineralogical characteristics are subjected to concentration; it is therefore expedient to investigate into the formation of a separation characteristic of technological concentration processes based on operating data on dynamics of their parameters [5,20,26].…”

Section: Introductionmentioning

confidence: 99%

Modelling of Iron Ore Processing in Technological Units Based on the Hybrid Approach

Morkun

Tron

Zymohliad

2022

Acta Mechanica Et Automatica

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The process line of concentrating iron ore materials is considered as a sequence of connected concentration units, some of which partially return ore materials to the previous unit. The output product of the final concentration unit in the process line is the end product of the whole line. Characteristics of ore, such as distribution of ore particles by size and distribution of iron content by size classes, are considered. Processing of iron ore materials by process units (a cycle, a scheme) is characterised by a separation characteristic – namely the function of extracting elementary fractions depending on physical properties of ore particles. The results of fraction analysis of ore samples in different points of the process line provide an experimental definition of separation characteristics and numerical values of the Rosin–Rammler equation factors. To identify dependencies that cannot be analytically described, the hybrid approach accompanied by the Takagi–Sugeno fuzzy models, in accompaniment with triangular membership functions determining fuzzy sets in preconditions, are used. To identify fuzzy sets in rule preconditions, triangular membership functions are used. Introduction of a-priori data on iron ore concentration as constraints for model parameters is a promising trend of further research, since it enables increased accuracy of identification despite limited availability of experimental data.

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“…Also, the computational burden is more in using the LMPC for nonlinear process control. These are a few reasons many researchers started focusing on NMPC design in many nonlinear dynamic process applications [7][8]. Block-oriented model identification with the Hammerstein model structure is a popular parameter estimation approach [9].…”

Section: Introductionmentioning

confidence: 99%

Least Square Support Vector Machines Laguerre Hammerstein Model Identification and Non-linear Model Predictive Controller Design for pH Neutralization Process

Akshay

Subbulekshmi

2023

Gazi University Journal of Science

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Predictive Control of the Iron Ore Beneficiation Process Based on the Hammerstein Hybrid Model

Cited by 9 publications

References 33 publications

Formation of the separation characteristic of ore material thickening based on the model predictive control method

Formation of the separation characteristic of ore material thickening based on the model predictive control method

Modelling of Iron Ore Processing in Technological Units Based on the Hybrid Approach

Least Square Support Vector Machines Laguerre Hammerstein Model Identification and Non-linear Model Predictive Controller Design for pH Neutralization Process

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