Prediction of Critical Desalination Parameters Using Radial Basis Functions Networks

Prediction of critical desalination parameters (recovery and salt rejection) of two distinct processes based on real operational data is presented. The proposed method utilizes the radial basis function network using data clustering and histogram equalization. The scheme involves center selection and shape adjustment of the localized receptive fields. This algorithm causes each group of radial basis functions to adapt to regions of the clustered input space. Networks produced by the proposed algorithm have good generalization performance on prediction of non-linear input–output mappings and require a small number of connecting weights. The proposed method was used for the prediction of two different critical parameters for two distinct Reverse Osmosis (RO) plants. The simulation results indeed confirm the effectiveness of the proposed prediction method.

[1]  Gordon F. Leitner.  Economic feasibility of the reverse osmosis process for seawater desalination , 1987 .

[2]  M. Abdel-Jawad,et al.  Economics of seawater desalination by reverse osmosis , 1994 .

[3]  Sanjeev S. Tambe,et al.  Neural networks for the identification of MSF desalination plants , 1995 .

[4]  Sukhan Lee,et al.  A Gaussian potential function network with hierarchically self-organizing learning , 1991, Neural Networks.

[5]  Philip D. Wasserman,et al.  Advanced methods in neural computing , 1993, VNR computer library.

[6]  Anthony N. Barrett.  Computer vision and image processing , 1990 .

[7]  M. E. El-Hawary.  Artificial neural networks and possible applications to desalination , 1993 .

[8]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[9]  Bernard Widrow,et al.  Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[10]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[11]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[12]  M. A. Darwish,et al.  Technical and economical comparison between large capacity MSF and RO desalting plants , 1989 .

[13]  Steven J. Nowlan,et al.  Maximum Likelihood Competitive Learning , 1989, NIPS.

[14]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[15]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[16]  Fred J. Taylor,et al.  Digital Filter Design Handbook , 1983 .

[17]  Rajesh N. Davé,et al.  Robust clustering methods: a unified view , 1997, IEEE Trans. Fuzzy Syst..

[18]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[19]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

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

[21]  Thomas G. Dietterich,et al.  Improving the Performance of Radial Basis Function Networks by Learning Center Locations , 1991, NIPS.