The Experts below are selected from a list of 8715 Experts worldwide ranked by ideXlab platform

Chunhua Yang - One of the best experts on this subject based on the ideXlab platform.

  • reagent dosage control for the antimony Flotation Process based on froth size pdf tracking and an index predictive model
    Journal of Mining Science, 2019
    Co-Authors: Chunhua Yang, Jianqi Li
    Abstract:

    A reagent dosage hybrid control strategy for the antimony Flotation Process is proposed in this work. This strategy consists of two parts: reagent dosage tracking control based on a froth size probability density function (PDF) and reagent dosage compensation control based on a distributed-machine vision predictive model. The proposed method was tested on a gold-antimony Flotation Process, and it improved tailings qualification rate, and reduced the tailings standard deviation. This method also efficiently accounts for the influence of disturbances on the Flotation system and improves the stability and effectiveness of the Flotation system.

  • reagent dosages control based on bubble size characteristics for Flotation Process
    Iet Control Theory and Applications, 2016
    Co-Authors: Yongping He, Chunhua Yang
    Abstract:

    The reagent dosages can directly affect the concentrate grade and recovery in the mineral Flotation Process. For many years, the dosage has mainly been controlled by human observation of froth image features, especially the bubble size. However, the reagent dosage control based on human experience can easily cause fluctuation of the performance index, which may result in wasted ore resources and chemical reagents. In this paper, a control method of reagent dosages based on bubble size characteristics is proposed. By combining the distribution features of the bubble size, the estimation method of the probability density function of the bubble size was introduced, as was the method of determining the optimal width of the kernel function based on the maximum entropy. The error of the output bubble size PDF and the optimal bubble size PDF was used as the performance indicator to transform the control of the reagent dosages into optimization of seeking the minimum performance indicator. In the Process of selecting the best individual in every generation of the DE algorithm, the constraint of the reagent cost is taken into consideration. The experimental results showed the effectiveness of the proposed method.

  • Intelligent predictive control of cleaning Flotation Process based on froth texture features
    Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014
    Co-Authors: Chunhua Yang, Jianqi Li
    Abstract:

    Cleaning Process is the key stage affecting the final concentrate grade of mineral Flotation. The operation parameters are normally adjusted manually by operators to achieve the optimal control of the concentrate grade. However, this method is of great subjectivity, randomness and uncertainty. Therefore, an intelligent predictive control method for the cleaning Process based on the froth texture features is proposed in this paper. Firstly, the features of cleaner Flotation froth are analyzed, and the significant features are expressed with texture features by using the color co-occurrence matrix. Then an improved online prediction model is constructed and optimized by rolling optimization with the differential evolution algorithm, in order to achieve the optimal control of the pulp level. The validation of industrial data shows the effectiveness of the proposed method in bauxite cleaning Flotation Process, which is applied to stabilize the Flotation Process and concentrate grade.

  • Online prediction of concentrate grade in Flotation Process based on PCA and improved BP neural networks
    Proceedings of the 29th Chinese Control Conference, 2010
    Co-Authors: Yalin Wang, Wenjun Ou, Chunhua Yang
    Abstract:

    According to the difficulty of online measure of concentrate grade during mineral Flotation Process, an online prediction method for concentrate grade based on PCA and improved BP neural networks is proposed. Firstly, bubble characteristics are extracted from real-time obtained images by means of digital image Process technology and their relationships to concentrate grade are analyzed. Secondly, some principal components are extracted through PCA algorithm from these characteristics. Finally, an improved BP neural networks algorithm is adopted to construct prediction model which takes the concentrate grade data collected by offline assay as the training objectives. The experimental results demonstrate that the proposed method can effectively predict Flotation concentrate grade.

  • CDC - Nonparametric density estimation of bubble size distribution for monitoring mineral Flotation Process
    Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009
    Co-Authors: Chunhua Yang, Canhui Xu, Jianjiang Du
    Abstract:

    The mineral separation efficiency of Flotation Process depends very much on the surface properties of feed ore and addition of chemical reagents. Machine vision based analysis of froth appearance is considered as an indication of Flotation performance. Bubble structure obtained by watershed segmentation scheme is used to determine the amount of reagent. To explore bubble size distribution, nonparametric wavelet thresholding estimator is introduced to approximate the output Probability Density Function (PDF). With the aim of tracking the output PDFs to a target distribution shape, the output PDF model is therefore transformed into the dynamic weight coefficients model which allows a predicted reagent addition profile to be identified for controlling the Flotation Process.

Jie-sheng Wang - One of the best experts on this subject based on the ideXlab platform.

  • feed forward neural network soft sensor modeling of Flotation Process based on particle swarm optimization and gravitational search algorithm
    Computational Intelligence and Neuroscience, 2015
    Co-Authors: Jie-sheng Wang
    Abstract:

    For predicting the key technology indicators (concentrate grade and tailings recovery rate) of Flotation Process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the Flotation Process.

  • Application of Rough Set Algorithm Based on Fuzzy Clustering in Flotation Process System
    2006 6th World Congress on Intelligent Control and Automation, 2006
    Co-Authors: Yong Zhang, Li Wang, Jie-sheng Wang
    Abstract:

    Time-varying, strong coupling, nonlinearity and uncertainty are important characteristics in Flotation Process. A method to realize intelligent control with rough set algorithm based on fuzzy clustering is proposed. Rough set method is just suitable for discrete data. To deal with this problem, fuzzy clustering algorithm is introduced into Processing procedure. Fuzzy clustering was used to discretize data. Sequentially the discrete attribute table was gained. And then analysis and reduction of the characteristics of Flotation data were accomplished by rough set theory, thereby deducing the control regulation simplified. Using this method solves the problem that it is not very precise by manual manipulation in practice

  • ISNN (2) - Application of RBF neural networks based on a new hybrid optimization algorithm in Flotation Process
    Advances in Neural Networks - ISNN 2006, 2006
    Co-Authors: Yong Zhang, Jie-sheng Wang
    Abstract:

    An inferential estimation strategy of quality indexes of Flotation Process based on principal component analysis (PCA) and radial basis function neural network (RBFNN) is proposed. Firstly, the Process prior knowledge and PCA method are used to simplify the networks’ input dimension and to choose the secondary variables. Then a new hybrid optimization algorithm of RBFNN is developed. The algorithm includes simplified rival penalized competitive learning method (SRPCL) to make an adaptive clustering of networks’ input pattern and recursive least squares method (LSM) with forgetting factor to update networks’ weights. The simulation results show that this inference estimation strategy has high predictive accuracy in Flotation Process.

Mahdi Gharabaghi - One of the best experts on this subject based on the ideXlab platform.

  • a review on electrochemical behavior of pyrite in the froth Flotation Process
    Journal of Industrial and Engineering Chemistry, 2017
    Co-Authors: Hossein Moslemi, Mahdi Gharabaghi
    Abstract:

    Abstract Metal sulfides are usually semiconductor and cause electrochemical reactions. This phenomenon plays an important role in sulfide Flotation. Pyrite as the most abundant sulfide mineral is often associated with valuable sulfide minerals, coal and gold. It is very important to study its electrochemical behavior in the Flotation Process. This review focuses on researches carried out over the past several decades that have studied electrochemical Processes associated with pyrite occurring during Flotation. The mechanism of Processes such as oxidation, activation, depression, and interactions of activated and non-activated surfaces with collectors as well as factors affecting them are described. Moreover, the effect of electrochemical conditions during grinding on the Flotation Process is also discussed. It has been found that moderately oxidizing conditions are favorable for collector-less Flotation of pyrite while strongly reducing or oxidizing potentials lead to its depression. Increasing the electrochemical potential not only has a deleterious effect on the activation of pyrite by copper, but also facilitates its depression by depressants. In the case of the adsorption of xanthate whether on activated or non-activated surfaces, a great increase or decrease in the potential has adverse effects and it is necessary to optimize the electrochemical conditions. Various factors such as pH, solid percentage, particle size distribution, Flotation time, type and concentration of reagents and oxygen content as well as grinding conditions can affect the intensity of these electrochemical interactions. It is proposed that further researches using advanced chemical analysis techniques are needed to understand the electrochemical Processes involved in Flotation systems.

Natalia Morkun - One of the best experts on this subject based on the ideXlab platform.

  • Flotation Process Optimization Using High-Energy Ultrasound Frequency Control
    2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), 2019
    Co-Authors: Vladimir Morkun, Natalia Morkun, Andrey Pikilnyak
    Abstract:

    The paper describes a method, which allows controlling the composition of iron ore pulp solid and gas phases and forming the desired gas bubble size distribution function, which corresponds to the pulp solid particle size distribution in the Flotation Process using high-energy ultrasound of a given frequency. To form an automatic control of the ultrasound exposure frequency, the algorithm, which automatically corrects the combinations of the output frequencies of the inverter voltage depending on the direction of the effective values of the current and the frequency of the output voltage is used. This allows adjusting the voltage-controlled oscillator to the resonant frequency of the converter. The control system is based on measuring the effective value of the current of the voltage generator, which is tuning the frequency of the voltage-controlled oscillator, which is generating a rectangular voltage wave with a fixed maximum value, which displays the output voltage of the inverter. To simplify the calculations, an adaptive fuzzy controller was used in the model of the digital frequency control system.

  • The gas bubble size distribution control formation in the Flotation Process
    Metallurgical and Mining Industry, 2014
    Co-Authors: Vladimir Morkun, Natalia Morkun, Andrey Pikilnyak
    Abstract:

    © Metallurgical and Mining Industry, 2014. A method for the effective control of the pulp gas phase composition in the Flotation Process using dynamic effects of high energy ultrasound on the base of phased array technology and determination of its parameters are described.

  • the gas bubble size distribution control formation in the Flotation Process
    2014
    Co-Authors: Vladimir Morkun, Natalia Morkun
    Abstract:

    A method for the effective control of the pulp gas phase composition in the Flotation Process using dynamic effects of high energy ultrasound on the base of phased array technology and determination of its parameters are described.

  • iron ore Flotation Process control and optimization using high energy ultrasound
    2014
    Co-Authors: Vladimir Morkun, Natalia Morkun
    Abstract:

    The paper describes method allowing to effectively control the composition of iron ore pulp solid and gas phases to form the desired gas bubble size distribution function, which would coincide with the pulp solid particle size distribution in the Flotation Process using high-energy ultrasound.

Zhang Yong - One of the best experts on this subject based on the ideXlab platform.

  • Application of expert control method to Flotation Process
    Control and Decision, 2020
    Co-Authors: Zhang Yong, Wang Jie-sheng
    Abstract:

    An expert optimization control method on cation Flotation Process is suggested for the Process complexity, the mathematical modal uncertainty and the high requirements of control quality. The expert system possesses certain ability of self-learning and self-organization. The proposed rule representation can naturally and completely denote the knowledge of Flotation Process. It is easy to maintain the knowledge database by this method. The industrial application indicates that the proposed expert control method can satisfy the request of floatation Process control.

  • Soft-sensor Modeling for Economy and Technology Indexes in Flotation Process
    Control Engineering of China, 2020
    Co-Authors: Zhang Yong, Wang Jie-sheng
    Abstract:

    The nonlinear and time-varying characteristics make Flotation Process very difficult to build a soft-sensor model.To solve this problem,a soft-sensor method based on the radial basis function neural network is suggested,which is used to estimate un-measurable signals that are important for the Flotation Process control in order to improve system performance.The principal component analysis method is incorporated into the neural network,which not only solves the linear correlation of the input,but also simplifies the network structure and improves the network training speed.The simulation result shows that the presented on-line soft-sensor modeling method is effective and accurate.

  • Intelligent control method and application of cation reverse Flotation Process
    Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527), 2002
    Co-Authors: Zhang Yong
    Abstract:

    According to the complexity of the controlled object, uncertainty of its mathematical model and high requirements of its control quality, this paper introduces an intelligent control method for the cation reverse Flotation Process. With this method, the system can satisfy the technological requirements.