Time delay neural network modeling for particle size in SAG mills

Abstract Particle size is a very important variable in semi-autogenous grinding processes. It is desirable to measure the variable efficiently or even predict its variations in advance. In this paper, the time delay neural network model is developed to predict the feed particle size of a semi-autogenous grinding mill, and the Levenberg–Marquardt algorithm is used to train the network. Results show that the model predicted values fit well with the industrial operating data. The proposed model can predict the particle size in advance and allow adequate time to take corrective actions during abnormal operations, and therefore provide a great advantage in monitoring and control of the industrial processes.

[1]  O. Nelles.  Nonlinear System Identification , 2001 .

[2]  G. May,et al.  Hybrid neural network modeling of anion exchange at the interfaces of mixed anion III-V heterostructures grown by molecular beam epitaxy , 2005, IEEE Transactions on Semiconductor Manufacturing.

[3]  José Luis Salazar,et al.  Dynamic modelling and simulation of semi-autogenous mills , 2009 .

[4]  G.S. May,et al.  Neural network-based real-time malfunction diagnosis of reactive ion etching using in situ metrology data , 2004, IEEE Transactions on Semiconductor Manufacturing.

[5]  Bo-Suk Yang,et al.  A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting , 2010, Expert Syst. Appl..

[6]  S. Gaikwad,et al.  Hybrid neural network multivariable predictive controller for handling abnormal events in processing applications , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[7]  C. Özgen Karacan,et al.  Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks , 2008 .

[8]  Georgina Stegmayer,et al.  Neural Networks applied to wireless communications , 2006, IFIP AI.

[9]  Chun-Cheng Lin.  Analysis of unpredictable intra-QRS potentials in signal-averaged electrocardiograms using an autoregressive moving average prediction model. , 2010, Medical engineering & physics.

[10]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[11]  Jonathan Seville,et al.  Using intelligent software to predict the effects of formulation and processing parameters on roller compaction , 2008 .

[12]  Peter Wolschann,et al.  Comparison between two types of Artificial Neural Networks used for validation of pharmaceutical processes , 2009 .

[13]  Stephan Siegmann,et al.  Neural network modelling of the inductively coupled RF plasma synthesis of silicon nanoparticles , 2008 .

[14]  Cheng Hua Li,et al.  An efficient document classification model using an improved back propagation neural network and singular value decomposition , 2009, Expert Syst. Appl..

[15]  C. Wöhler,et al.  A TIME-DELAY NEURAL NETWORK ALGORITHM FOR REAL-TIME PEDESTRIAN RECOGNITION , 1998 .

[16]  Habib Benali,et al.  A nonlinear identification method to study effective connectivity in functional MRI , 2010, Medical Image Anal..

[17]  Yasar Becerikli,et al.  Modeling and prediction with a class of time delay dynamic neural networks , 2007, Appl. Soft Comput..

[18]  Eduardo Morgado Belo,et al.  Application of time-delay neural and recurrent neural networks for the identification of a hingeless helicopter blade flapping and torsion motions , 2005 .

[19]  Mahmoud Omid,et al.  Development of pistachio sorting system using principal component analysis (PCA) assisted artificial neural network (ANN) of impact acoustics , 2010, Expert Syst. Appl..

[20]  Y. Wang,et al.  Analysis and modeling of multivariate chaotic time series based on neural network , 2009, Expert Syst. Appl..

[21]  Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring , 2007, Comput. Ind. Eng..

[22]  H. Brian Hwarng,et al.  A simple neural network for ARMA(p,q) time series , 2001 .

[23]  Dominique Bertrand,et al.  Evaluation of the transition matrix for comminuting pea seeds in an impact mill using a linear neural network , 1999 .

[24]  Jules Thibault,et al.  Development of a softsensor for particle size monitoring , 1996 .

[25]  Mohammad Bagher Tavakoli,et al.  Modified Levenberg-Marquardt Method for Neural Networks Training , 2007 .

[26]  Manuel A. Duarte,et al.  Control of grinding plants using predictive multivariable neural control , 2001 .

[27]  Gary S. May,et al.  Real-time control of reactive ion etching using neural networks , 2000 .

[28]  Chin-Teng Lin,et al.  A Space-Time Delay Neural Network for Motion Recognition and its Application to Lipreading , 1999, Int. J. Neural Syst..

[29]  Qi Li,et al.  Constrained model predictive control in ball mill grinding process , 2008 .