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Ball Mill

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Tianyou Chai – 1st expert on this subject based on the ideXlab platform

  • Modeling load parameters of Ball Mill using frequency spectral features based on Hilbert vibration decomposition
    2014 IEEE International Conference on Information and Automation (ICIA), 2014
    Co-Authors: Jian Tang, Tianyou Chai

    Abstract:

    Load parameters inside the Ball Mill is one of the key factors that affect grinding production ratio and production quantity of the grinding process directly. The Ball Mill produces soundly mechanical vibration and acoustical signals. Many methods have been applied to measure them. In this paper, a new frequency spectral feature of Hilbert vibration decomposition (HVD) based soft sensor approach is proposed. Sub-signals with different physical interpretation are obtained with HVD technology. Different frequency spectral features of these subsignals are selected using fast Fourier transform (FFT) and mutual information (MI), which are fed into kernel partial least squares (KPLS) for constructing soft sensor model of the Mill load parameters. Experimental results on a laboratory Ball Mill show that the pulp density can be effective measured using the proposed method.

  • Modeling and Simulation of Whole Ball Mill Grinding Plant for Integrated Control
    IEEE Transactions on Automation Science and Engineering, 2014
    Co-Authors: Shaowen Lu, Ping Zhou, Tianyou Chai

    Abstract:

    This paper introduces the development and implementation of a Ball Mill grinding circuit simulator, NEUSimMill. Compared to the existing simulators in this field which focus on process flowsheeting, NEUSimMill is designed to be used for the test and verification of grinding process control system including advanced control system such as integrated control. The simulator implements the dynamic Ball Mill grinding model which formulates the dynamic responses of the process variables and the product particle size distribution to disturbances and control behaviors as well. First principles models have been used in conjunction with heuristic inference tools such as fuzzy logic and artificial neural networks: giving rise to a hybrid intelligent model which is valid across a large operating range. The model building in the simulator adopts a novel modular-based approach which is made possible by the dynamic sequential solving approach. The simulator can be initiated with connection to a real controller to track the plant state and display in real-time the effect of various changes on the simulated plant. The simulation model and its implementation is verified and validated through a case of application to the design, development, and deployment of optimal setting control system.

  • Modeling Load Parameters of Ball Mill in Grinding Process Based on Selective Ensemble Multisensor Information
    IEEE Transactions on Automation Science and Engineering, 2013
    Co-Authors: Jian Tang, Tianyou Chai, Wen Yu, Li-jie Zhao

    Abstract:

    Due to complex dynamic characteristics of the Ball Mill system, it is difficult to measure load parameters inside the Ball Mill. It has been noticed that the traditional single-model and ensemble-model based soft sensor approaches demonstrate weak generalization power. Also, Mill motor current, feature subsets of the shell vibration and acoustical frequency spectra contain different useful information. To achieve better solutions and overcome these problems mentioned above, a selective ensemble multisource information approach is proposed in this paper. Only the useful feature subsets of vibration and acoustical frequency spectra are portioned and selected. Some modeling techniques, such as fast Fourier transform (FFT), mutual information (MI), kernel partial least square (KPLS), brand and band (BB), and adaptive weighting fusion (AWF), are combined effectively to model the Mill load parameters. The simulation is conducted using real data from a laboratory-scale Ball Mill. The results show that our proposed approach can effectively fusion the shell vibration, acoustical and Mill motor current signals with improved model generalization.

Rico Thorwirth – 2nd expert on this subject based on the ideXlab platform

  • fast ligand and solvent free copper catalyzed click reactions in a Ball Mill
    ChemInform, 2011
    Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Andreas Wild, Ulrich S Schubert

    Abstract:

    The Huisgen cycloaddition proceeds highly selective under mild conditions and very short reaction times employing a planetary Ball Mill together with fused quartz sand as a Milling auxiliary.

  • fast ligand and solvent free copper catalyzed click reactions in a Ball Mill
    Chemical Communications, 2011
    Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Andreas Wild, Ulrich S Schubert

    Abstract:

    A new, ligand- and solvent-free method for the Huisgen 1,3-dipolar cycloaddition (click reaction) was developed using a planetary Ball Mill. Besides various alkynes and azides, a propargyl functionalized polymer was converted by Mill clicking. Moreover, it was possible to carry out a clickpolymerization in a Ball Mill.

  • switchable selectivity during oxidation of anilines in a Ball Mill
    Chemistry: A European Journal, 2010
    Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Franziska Bernhardt, Jila Asghari

    Abstract:

    A solvent-free method for the direct oxidation of anilines to the corresponding azo and azoxy homocoupling products by using a planetary Ball Mill was developed. Various oxidants and grinding auxiliaries were tested and a variety of substituted anilines were investigated. It was possible to form chemoselectively either azo, azoxy, or the nitro compounds from reaction of aromatic anilines. The selectivity of the solvent-free reaction is switchable by applying a combination of oxidant and grinding auxiliary. Furthermore, a comparison with other methods of energy input (microwave, classical heating, and ultrasound) highlighted the advantages of the Ball Mill approach and its high energy efficiency.

Li-jie Zhao – 3rd expert on this subject based on the ideXlab platform

  • Modeling Load Parameters of Ball Mill in Grinding Process Based on Selective Ensemble Multisensor Information
    IEEE Transactions on Automation Science and Engineering, 2013
    Co-Authors: Jian Tang, Tianyou Chai, Wen Yu, Li-jie Zhao

    Abstract:

    Due to complex dynamic characteristics of the Ball Mill system, it is difficult to measure load parameters inside the Ball Mill. It has been noticed that the traditional single-model and ensemble-model based soft sensor approaches demonstrate weak generalization power. Also, Mill motor current, feature subsets of the shell vibration and acoustical frequency spectra contain different useful information. To achieve better solutions and overcome these problems mentioned above, a selective ensemble multisource information approach is proposed in this paper. Only the useful feature subsets of vibration and acoustical frequency spectra are portioned and selected. Some modeling techniques, such as fast Fourier transform (FFT), mutual information (MI), kernel partial least square (KPLS), brand and band (BB), and adaptive weighting fusion (AWF), are combined effectively to model the Mill load parameters. The simulation is conducted using real data from a laboratory-scale Ball Mill. The results show that our proposed approach can effectively fusion the shell vibration, acoustical and Mill motor current signals with improved model generalization.

  • Ball Mill Load State Recognition Based on Kernel PCA and Probabilistic PLS-ELM
    Applied Mechanics and Materials, 2012
    Co-Authors: Li-jie Zhao, De Cheng Yuan, Jian Tang

    Abstract:

    Operating condition recognition of Ball Mill load is important to improve product quality, decrease energy consumption and ensure the safety of grinding process. A probabilistic one-against-one (OAO) multi-classification method using partial least square-based extreme learning machine algorithm (PLS-ELM) is proposed to identify the operating state of Ball Mill. The feature of shell vibration spectrum is extracted using KPCA. PLS-ELM model is applied to enhance the reliability and accuracy of the operating conditions identification of the Ball Mill load. Posterior probability of each class using Bayesian decision theory is defined as a measure as classification reliability. Classification results of the experimental Ball Mill shown that the accuracy and stability of the proposed method outperform ELM, PLS-ELM and KPCA-ELM model.

  • Operating condition recognition in Ball Mill based on discriminant PLS
    2010 Second Pacific-Asia Conference on Circuits Communications and System, 2010
    Co-Authors: Hui Xiao, Li-jie Zhao, Xiao-kun Diao

    Abstract:

    Operating condition recognition of a Ball Mill is an important part in grinding process. The load status can only be determined according to expert’s experiences. If common operating conditions (such as under-load, best-load, and over-load) are not monitored and handled promptly and accurately, the quality of the grinding product may deteriorate or even the grinding production may come to a stop. This paper estimates the power spectral density of the input vibration and acoustic signals using Welch’s averaged modified periodogram method of spectral estimation. Unsupervised Fuzzy C-Means classification and Partial Least Squares for Discrimination (DPLS) are used to build operating status model for the Ball Mill. Experimental results shown recognition capability is enhanced, the false alarming rate is decreased.