Feedforward–feedback predictive control of a simulated flotation bank

Hodouin, D.; Bazin, C.; Gagnon, Éric; Flament, Frédéric

doi:10.1016/s0032-5910(99)00217-x

Cited by 19 publications

(8 citation statements)

References 5 publications

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“…These are applied to the model of a rougher flotation bank presented by Hodouin et al (2000). In addition to the model predictive control, a study on rule-based control is also included.…”

Section: Discrete Step Response Models Of a Rougher Flotation Bankmentioning

confidence: 99%

“…3. Responses of the concentrate and tailings grades to step changes in the aeration rate, collector addition rate, feed rate and feed grade Table 1 Transfer function models of the flotation process (according to Hodouin et al (2000) …”

Section: Discrete Step Response Models Of a Rougher Flotation Bankmentioning

confidence: 99%

“…Discrete step response models have been reproduced from the models developed by Hodouin et al (2000). Next, an evaluation of the economical performance of the flotation circuit at the Inmet Mining Co. Pyhäsalmi mine is carried out based on the use of the mechanistic flotation models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of speed and accuracy of on-line elemental analysis on flotation control performance

Remes¹,

Saloheimo

Jämsä‐Jounela³

2007

Minerals Engineering

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In this paper the control performance of the flotation process is evaluated as a function of the measurement accuracy and sampling frequency of an on-stream analyzer. First, the performance of rule-based control and model predictive control (MPC) strategies is studied using discrete flotation models and the respective performance indices. Next, the net smelter return (NSR) is calculated for varying sampling rate and accuracy combinations using the PI controllers-based control strategy, mechanistic flotation models and the industrial process data as input. The control and economical performance of the process declines strongly when the sampling cycle is increased. The results also indicate that the speed of on-line analysis has a significant effect on the production economics, calculated as the average net smelter return.

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“…These are applied to the model of a rougher flotation bank presented by Hodouin et al (2000). In addition to the model predictive control, a study on rule-based control is also included.…”

Section: Discrete Step Response Models Of a Rougher Flotation Bankmentioning

confidence: 99%

Section: Discrete Step Response Models Of a Rougher Flotation Bankmentioning

confidence: 99%

See 1 more Smart Citation

Effect of speed and accuracy of on-line elemental analysis on flotation control performance

Remes¹,

Saloheimo

Jämsä‐Jounela³

2007

Minerals Engineering

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“…They are listed briefly below: -Model predictive SISO control (mature) in Suichies, Leroux, and Dechert (1998); -Multivariable control: model predictive feedback control (Hodouin et al, 1993a), including predictive feedforward (Hodouin, Gagnon, & Bazin, 2000); Ding and Gustafsson (1999); for column flotation (del Villar et al 1999a, b;Desbiens, Hodouin, & Mailloux, 1998b); mature multivariable level control in Hulbert (1995); -Non-linear control using mathematical models (Desbiens, del Villar, & Milot, 1998a;Benaskeur & Desbiens, 1999); -Adaptive control (Desbiens et al, 1994a, b); Sbarbaro (1999); Supervisory expert control: Cipriano, Medina, Martinez, and Gonzales (1991), and for the flotation columns: McKay and Ynchausti (1996), Bergh, Yianatos, and Cartes (1996); Bergh and Yianatos (1999); -Fuzzy control: Carvalho andDurão (1999a, 2000); Hirajima et al (1991) ;Osorio, Perez-Correa and Cipriano (1999); -Artificial Neural Nets: Cotez and Durã0 (1995), and Durão and Cortez (1995); -Adaptive genetic algorithm: Karr (1996); -Integration of grinding and flotation control and optimization: Bascur (1991), Pulkkinen, Ylinen, Jämsä-Jounela, and Järvensivu (1993); Sosa-Blanco et al (2000).…”

Section: Flotation Processmentioning

confidence: 99%

State of the art and challenges in mineral processing control

Hodouin

Jämsa-Jounela²,

Carvalho

et al. 2000

IFAC Proceedings Volumes

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The objective of process control in the mineral industry is to optimise the recovery of the valuable minerals, while maintaining the quality of the concentrates delivered to the metal extraction plants. The paper presents a survey of the control approaches for ore size reduction and mineral separation processes. The present limitations of the measurement instrumentation are discussed, as well as the methods to upgrade the information delivered by the sensors. In practice, the overall economic optimisation goal must be hierarchically decomposed into simpler control problems. Model-based and AI methods are reviewed, mainly for grinding and flotation processes, and classified as mature, active or emerging.

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“…In recent years, the reagent dosage control has attracted great interest of both academic and industrial researchers. In Hodouin et al (2000) a feedforward and feedback prediction control algorithm was developed to control the reagent dosage. The reagent addition is determined according to ore amount and property by using a feedforward control strategy, and then the dosage is moderately adjusted by feedback control.…”

Section: Introductionmentioning

confidence: 99%

Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation

Zhu

Gui

Yang

et al. 2014

Control Engineering Practice

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As an effective measurement indicator of bubble stability, bubble size structure is believed to be closely related to flotation performance in copper roughing flotation. Moreover, reagent dosage has a very important influence on the bubble size structure. In this paper, a novel reagent dosage predictive control method based on probability density function (PDF) of bubble size is proposed to implement the indices of roughing circuit. Firstly, the froth images captured in the copper roughing are segmented by using a two-pass watershed algorithm. In order to characterize bubble size structure with non-Gaussian feature, an entropy based B-spline estimator is hence investigated to depict the PDF of the bubble size. Since the weights of B-spline are interrelated and related to the reagent dosage, a multi-output least square support vector machine (MLS-SVM) is applied to establish a dynamical relationship between the weights and the reagent dosage. Finally, an entropy based optimization algorithm is proposed to determine reagent dosage in order to implement tracking control for the PDF of the output bubble size. Experimental results can show the effectiveness of the proposed method.

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Feedforward–feedback predictive control of a simulated flotation bank

Cited by 19 publications

References 5 publications

Effect of speed and accuracy of on-line elemental analysis on flotation control performance

Effect of speed and accuracy of on-line elemental analysis on flotation control performance

State of the art and challenges in mineral processing control

Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation

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