Probability Density Function of Bubble Size Based Reagent Dosage Control for Flotation Process

Zhu, Jianyong; Gui, Weihua; Yang, Chunhua; Liu, Jin Ping; Tang, Yawen

doi:10.1002/asjc.847

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…In reference [21], the feedback controller maintains the probability density function of bubble size at the setpoint through the reagent dosage. In reference [22], the method used the bubble size probability density function to characterize the froth state. The reagent dosage is optimally controlled by minimizing the difference between the current bubble size probability density function and the optimal bubble size probability density function.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Setting Method of Reagent Dosage Based on Time Series Froth Image in Zinc Flotation Process

Tang

Zhang

et al. 2020

Processes

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It is well known that the change of the reagent dosage during the flotation process will cause the froth image to change continuously with time. Therefore, an intelligent setting method based on the time series froth image in the zinc flotation process is proposed. Firstly, the sigmoid kernel function is used to estimate the cumulative distribution function of bubble size, and the cumulative distribution function shape is characterized by sigmoid kernel function parameters. Since the reagent will affect the froth image over a period of time, the time series of bubble size cumulative distribution function is processed by the ELMo model and the dynamic feature vectors are output. Finally, XGBoost is used to establish the nonlinear relationship modeling between reagent dosage and dynamic feature vectors. Industrial experiments have proved the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Intelligent Setting Method of Reagent Dosage Based on Time Series Froth Image in Zinc Flotation Process

Tang

Zhang

et al. 2020

Processes

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“…According to the non‐Gaussianity of bubble size distribution, a single statistic character is incapable of describing the distribution of bubble size. A reagent dosage control method based on the probability density function of bubble size in a copper flotation process was proposed in , in which the relationship between the bubble size and the reagent dosage was built by MLS‐SVM to make the output weight vector track the given weight vector. The results show that this method can produce more recoveries than produced by field workers.…”

Section: Introductionmentioning

confidence: 99%

The Method of Reagent Control Based on Time Series Distribution of Bubble Size in a Gold‐Antimony Flotation Process

Li¹,

Gui²

2017

Asian Journal of Control

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Due to the distribution of bubble size changes with the reagent dosage in a flotation process, a dosage control method based on time series distribution of bubble size during the gold-antimony flotation process is proposed. Firstly, since the flotation conditions cannot be described fully by the features of a single froth image, the concept of cumulative distribution function (CDF) is presented for describing the time series distribution of consecutive multi-frame bubble sizes, and approximated by a third order B-spline function. After that, a method that combines an radial basis function (RBF) neural network with the genetic algorithm (GA-RBF) is used to obtain the optimal CDF of the bubble size, then, a projection pursuit method is employed to reduce the multidimensional weights. Finally, a nonlinear prediction model combining reagent dosage and the projection vector is constructed based on partial least square through spline transformation (PLSS). The error between the output and the given CDF can be optimized by a differential evolution algorithm. The industrial experiment demonstrates the effectiveness of the proposed method.

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“…For example, there is a high superficial gas velocity in the corners of the tank [76]. Therefore, the optimal bubble size, considering that the PDF of the distribution is non-Gaussian, together with the analysis of the sample data and the corresponding literature [25,37,77], is 0.0708 cm. The distribution characteristics of bubble size reveal a skew distribution to the left, which leads to the logical conclusion that the smaller the bubble size, the greater the stability.…”

mentioning

confidence: 99%

Analysis of the Dynamics of Rougher Cells on the Basis of Phenomenological Models and Discrete Event Simulation Framework

Saldaña

Neira

Flores

et al. 2021

Metals

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Due to the increase in the amount of copper sulphide minerals processed through concentration processes and the need to improve the efficiency of these production processes, the development of theoretical models is making an important contribution to generating a better understanding of their dynamics, making it possible to identify the optimal conditions for the recovery of minerals, the impact of the independent variables in the responses, and the sensitivity of the recovery to variations in both the input variables and the operational parameters. This paper proposes a method for modeling, sensitizing, and optimizing the mineral recovery in rougher cells using a discrete event simulation (DES) framework and the fitting of analytical models on the basis of operational data from a concentration pilot plant. A sensitivity analysis was performed for low, medium, and high levels of the operative variables and/or parameters. The outcomes of the modeling indicate that the optimum mineral recovery is reached at medium levels of the flow rate of gas, bubble size, turbulence dissipation rate, surface tension, Reynolds number of bubble, bubble–particle contact angle, superficial gas velocity and gas hold-up in the froth zone. Additionally, the optimal response is reached at maximum levels of particle size and density and at minimum levels of bubble speed, fluid kinematic viscosity and fluid density in the sampled range. Finally, the recovery has an asymptotic behavior over time; however, the optimum recovery depends on an economic analysis, examining the marginalization of the response over time in an operational context.

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Probability Density Function of Bubble Size Based Reagent Dosage Control for Flotation Process

Cited by 6 publications

References 37 publications

Intelligent Setting Method of Reagent Dosage Based on Time Series Froth Image in Zinc Flotation Process

Intelligent Setting Method of Reagent Dosage Based on Time Series Froth Image in Zinc Flotation Process

The Method of Reagent Control Based on Time Series Distribution of Bubble Size in a Gold‐Antimony Flotation Process

Analysis of the Dynamics of Rougher Cells on the Basis of Phenomenological Models and Discrete Event Simulation Framework

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