Development and comparison of neural network based soft sensors for online estimation of cement clinker quality.

The online estimation of process outputs mostly related to quality, as opposed to their belated measurement by means of hardware measuring devices and laboratory analysis, represents the most valuable feature of soft sensors. As of now there have been very few attempts for soft sensing of cement clinker quality which is mostly done by offline laboratory analysis resulting at times in low quality clinker. In the present work three different neural network based soft sensors have been developed for online estimation of cement clinker properties. Different input and output data for a rotary cement kiln were collected from a cement plant producing 10,000 tons of clinker per day. The raw data were pre-processed to remove the outliers and the resulting missing data were imputed. The processed data were then used to develop a back propagation neural network model, a radial basis network model and a regression network model to estimate the clinker quality online. A comparison of the estimation capabilities of the three models has been done by simulation of the developed models. It was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models.

[1]  Emilio Marengo,et al.  Modeling of the polluting emissions from a cement production plant by partial least-squares, principal component regression, and artificial neural networks. , 2006, Environmental science & technology.

[2]  B. Hanumantha Rao,et al.  Artificial neural network models for predicting soil thermal resistivity , 2008 .

[3]  N. K. Roy,et al.  Polymer property prediction and optimization using neural networks , 2006, IEEE Trans. Neural Networks.

[4]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[5]  Biswajit Samanta,et al.  Radial Basis Function Network for Ore Grade Estimation , 2010 .

[6]  Silvia Scarpetta,et al.  On-line learning in RBF neural networks: a stochastic approach , 2000, Neural Networks.

[7]  Zheng Fang,et al.  LS-SVR-Based Soft Sensor Model for Cement Clinker Calcination Process , 2010, 2010 International Conference on Measuring Technology and Mechatronics Automation.

[8]  Ronald K. Pearson,et al.  Outliers in process modeling and identification , 2002, IEEE Trans. Control. Syst. Technol..

[9]  Canan Özgen,et al.  Artificial Neural Network Estimator Design for the Inferential Model Predictive Control of an Industrial Distillation Column , 2004 .

[10]  Hao Ye,et al.  Nonlinear time series modeling and prediction using RBF network with improved clustering algorithm , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[11]  J. Sola,et al.  Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .

[12]  Dale Schuurmans,et al.  Automatic basis selection techniques for RBF networks , 2003, Neural Networks.

[13]  S Linko,et al.  Neural networks as 'software sensors' in enzyme production. , 1997, Journal of biotechnology.

[14]  Y. A. Liu,et al.  Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network and Model-Based Process Simulation , 2002 .

[15]  Wei Jiang,et al.  On-line outlier detection and data cleaning , 2004, Comput. Chem. Eng..

[16]  S. Chehreh Chelgani,et al.  Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks , 2010 .

[17]  A. M. Elsharkwy,et al.  Comparing classical and neural regression techniques in modeling crude oil viscosity , 2001 .

[18]  R. K. Pearson,et al.  Exploring process data , 2001 .

[19]  Li Peisheng,et al.  Prediction of grindability with multivariable regression and neural network in Chinese coal , 2005 .

[20]  Sanghee Kwon,et al.  Modeling of plasma process data using a multi-parameterized generalized regression neural network , 2009 .

[21]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[22]  Haralambos Sarimveis,et al.  A Fast and Efficient Algorithm for Training Radial Basis Function Neural Networks Based on a Fuzzy Partition of the Input Space , 2002 .

[23]  Shie-Yui Liong,et al.  An ANN application for water quality forecasting. , 2008, Marine pollution bulletin.

[24]  Lúcia Valéria Ramos de Arruda,et al.  A neuro-coevolutionary genetic fuzzy system to design soft sensors , 2008, Soft Comput..

[25]  Luiz Augusto da Cruz Meleiro,et al.  ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process , 2009, Comput. Chem. Eng..

[26]  A. Mohammadi,et al.  Use of Artificial Neural Networks for Estimating Water Content of Natural Gases , 2007 .

[27]  Alex Arenas,et al.  Neural virtual sensor for the inferential prediction of product quality from process variables , 2002 .

[28]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in modelling river sediment yield , 2006, Adv. Eng. Softw..

[29]  Ozgur Kisi,et al.  Comparison of different ANN techniques in river flow prediction , 2007 .

[30]  Karlene A. Kosanovich,et al.  Improving the prediction capability of radial basis function networks , 1998 .

[31]  Zhizhong Mao,et al.  Application of Genetic Algorithm Combined with BP Neural Network in Soft Sensor of Molten Steel Temperature , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[32]  Stephen A. Billings,et al.  Radial basis function network configuration using genetic algorithms , 1995, Neural Networks.

[33]  Sten Bay Jørgensen,et al.  A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..

[34]  Anthony T. C. Goh,et al.  SOIL LABORATORY DATA INTERPRETATION USING GENERALIZED REGRESSION NEURAL NETWORK , 1999 .

[35]  Ligang Zheng,et al.  Monitoring NOx Emissions from Coal Fired Boilers Using Generalized Regression Neural Network , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[36]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition , 2006 .