The Experts below are selected from a list of 86598 Experts worldwide ranked by ideXlab platform
Tianyou Chai - One of the best experts on this subject based on the ideXlab platform.
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Modeling load parameters of Ball Mill using frequency spectral features based on Hilbert vibration decomposition
2014 IEEE International Conference on Information and Automation (ICIA), 2014Co-Authors: Jian Tang, Tianyou ChaiAbstract: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.
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Modeling and Simulation of Whole Ball Mill Grinding Plant for Integrated Control
IEEE Transactions on Automation Science and Engineering, 2014Co-Authors: Shaowen Lu, Ping Zhou, Tianyou ChaiAbstract: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.
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Modeling Load Parameters of Ball Mill in Grinding Process Based on Selective Ensemble Multisensor Information
IEEE Transactions on Automation Science and Engineering, 2013Co-Authors: Jian Tang, Tianyou Chai, Wen Yu, Li-jie ZhaoAbstract: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.
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multiple models and neural networks based decoupling control of Ball Mill coal pulverizing systems
Journal of Process Control, 2011Co-Authors: Tianyou Chai, Lianfei ZhaiAbstract:Abstract Using a Ball Mill coal-pulverizing system as a motivating/application example, a class of complex industrial processes is investigated in this paper, which has strong couplings among loops, high nonlinearities and time-varying dynamics under different operation conditions. Focusing on such processes, an intelligent decoupling control method is developed, where the effects of nonlinearities are dealt with by neural network compensations and coupling effects are handled by specifically designed decoupling compensators, while the effect of time-varying dynamics is treated by a switching mechanism among multiple models. The stability and convergence of the closed-loop system are analyzed. The proposed method has been applied to the Ball Mill coal-pulverizing systems of 200 MW units in a heat power plant in China. Application results show that the system outputs are maintained in desired scopes, the electric energy consumption per unit coal has been reduced by 10.3%, and the production rate has been increased by 8%.
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experimental analysis of wet Mill load based on vibration signals of laboratory scale Ball Mill shell
Minerals Engineering, 2010Co-Authors: Jian Tang, Li-jie Zhao, Junwu Zhou, Tianyou ChaiAbstract:Abstract Real-time measurement of the Mill load is the key to improve the production capacity and energy efficiency for the grinding process. In this paper, experimental analysis of the wet Mill load based on the vibration signals of the laboratory-scale Ball Mill shell is presented. A series of experiments are conducted to investigate the vibration characteristics corresponding to different grinding conditions such as dry grinding, wet grinding and water grinding. The power spectral density of the vibration signals is systematically interpreted. Experimental results show that the rheological properties of the pulp affect the amplitude and frequency of the vibration signal. The most important conclusion is that the frequency range of the shell vibration of the laboratory wet Mill can be divided into three parts, namely natural frequency band, main impact frequency band and secondary impact frequency band. Finally, soft-sensor models between vibration signal and Mill operating parameters of Mill load are established using genetic algorithm-partial least square (GA-PLS) technology. After more work on industry scale Ball Mill is done, the soft-sensor modeling based on the Mill shell vibration for operating parameters of Mill load will improve the performance of the Ball Mill in the grinding process.
Rico Thorwirth - One of the best experts on this subject based on the ideXlab platform.
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fast ligand and solvent free copper catalyzed click reactions in a Ball Mill
ChemInform, 2011Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Andreas Wild, Ulrich S SchubertAbstract: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.
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fast ligand and solvent free copper catalyzed click reactions in a Ball Mill
Chemical Communications, 2011Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Andreas Wild, Ulrich S SchubertAbstract: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.
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switchable selectivity during oxidation of anilines in a Ball Mill
Chemistry: A European Journal, 2010Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Franziska Bernhardt, Jila AsghariAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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Modeling Load Parameters of Ball Mill in Grinding Process Based on Selective Ensemble Multisensor Information
IEEE Transactions on Automation Science and Engineering, 2013Co-Authors: Jian Tang, Tianyou Chai, Wen Yu, Li-jie ZhaoAbstract: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.
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Ball Mill Load State Recognition Based on Kernel PCA and Probabilistic PLS-ELM
Applied Mechanics and Materials, 2012Co-Authors: Li-jie Zhao, De Cheng Yuan, Jian TangAbstract: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.
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Operating condition recognition in Ball Mill based on discriminant PLS
2010 Second Pacific-Asia Conference on Circuits Communications and System, 2010Co-Authors: Hui Xiao, Li-jie Zhao, Xiao-kun DiaoAbstract: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.
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experimental analysis of wet Mill load based on vibration signals of laboratory scale Ball Mill shell
Minerals Engineering, 2010Co-Authors: Jian Tang, Li-jie Zhao, Junwu Zhou, Tianyou ChaiAbstract:Abstract Real-time measurement of the Mill load is the key to improve the production capacity and energy efficiency for the grinding process. In this paper, experimental analysis of the wet Mill load based on the vibration signals of the laboratory-scale Ball Mill shell is presented. A series of experiments are conducted to investigate the vibration characteristics corresponding to different grinding conditions such as dry grinding, wet grinding and water grinding. The power spectral density of the vibration signals is systematically interpreted. Experimental results show that the rheological properties of the pulp affect the amplitude and frequency of the vibration signal. The most important conclusion is that the frequency range of the shell vibration of the laboratory wet Mill can be divided into three parts, namely natural frequency band, main impact frequency band and secondary impact frequency band. Finally, soft-sensor models between vibration signal and Mill operating parameters of Mill load are established using genetic algorithm-partial least square (GA-PLS) technology. After more work on industry scale Ball Mill is done, the soft-sensor modeling based on the Mill shell vibration for operating parameters of Mill load will improve the performance of the Ball Mill in the grinding process.
Bernd Ondruschka - One of the best experts on this subject based on the ideXlab platform.
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fast ligand and solvent free copper catalyzed click reactions in a Ball Mill
ChemInform, 2011Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Andreas Wild, Ulrich S SchubertAbstract: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.
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fast ligand and solvent free copper catalyzed click reactions in a Ball Mill
Chemical Communications, 2011Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Andreas Wild, Ulrich S SchubertAbstract: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.
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switchable selectivity during oxidation of anilines in a Ball Mill
Chemistry: A European Journal, 2010Co-Authors: Rico Thorwirth, Achim Stolle, Bernd Ondruschka, Franziska Bernhardt, Jila AsghariAbstract: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.
Yanbin Zhang - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear prediction model for ventilation of Ball Mill pulverizing system
2016 35th Chinese Control Conference (CCC), 2016Co-Authors: Yiwei Yuan, Gangquan Si, Yanbin Zhang, Shiliang ZhangAbstract:Ventilation is an important parameter affecting the quality and the efficiency of Ball Mill pulverizing system. This paper proposed a nonlinear prediction model for ventilation. The proposed method adopted nonlinear partial least squares(PLS) with the back-propagation neural network(BPNN) as an inner function. First, PLS extracts the latent variables of the input and output to eliminate the collinearity, and nonlinear relation between each pair of latent variables are constructed with BPNN. Furthermore, the proposed model is compared with models based on the least squares support vector machine, the back-propagation neural network and the kernel partial least squares algorithms. The root-mean-squared error of prediction and root-mean-square error of cross validation are introduced to evaluate the effectiveness of the models. The data of ventilation adopted in the model is obtained from the real Ball Mill pulverizing system. The experimental results verified that the back-propagation neural network based partial least squares model has the best performance in predicting ventilation of pulverizing system.
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A Hybrid Controller of Self-Optimizing Algorithm and ANFIS for Ball Mill Pulverizing System
2007 International Conference on Mechatronics and Automation, 2007Co-Authors: Gangquan Si, Yanbin ZhangAbstract:For Ball Mill pulverizing system of the thermal power plant, a hybrid controller of self-optimizing algorithm and adaptive neuro-fuzzy inference system(ANFIS) is proposed. In order to keep the Ball Mill pulverizing system working at the optimum point all along, the self-optimizing algorithm is presented. The self-optimization algorithm can automatically find out the extreme point and adjust the control set values in time. The adaptive neuro-fuzzy inference system, which integrates the advantages of the neural network and the fuzzy control, uses the learning ability of the neural network to optimize the membership functions and fuzzy logic rules of fuzzy control. Such combined framework makes fuzzy control more systematic and less relying on expert knowledge. Simulations results verify that the controller can control the Ball Mill pulverizing system effectively and has higher control quality.