Studies on the application of different ANNs to predict permeate flux in rotating disk membrane modules: A case study with MATLAB™

References

• Ho , W. S. and Sirkar , K. K. 1992 . Membrane Handbook , New York : Van Nostrand Reinhold .
   Google Scholar
• Reed , B. E. , Viadero , W. L. R. and Young , J. 1997 . Treatment of oily wastes using high-shear rotary ultrafiltration . J. Env. Eng. , 123 : 1234 – 1242 .
   Web of Science ®Google Scholar
• Bhattacharjee , S. , Datta , S. and Bhattacharjee , C. 2006 . Studies on the fractionation of $-lactoglobulin from casein whey using ultrafiltration and ion-exchange membrane chromatography . J. Membr. Sci. , 275 : 141 – 150 .
   Web of Science ®Google Scholar
• Petala , M. D. and Zouboulis , A. I. 2006 . Vibratory shear enhanced processing membrane filtration applied for the removal of natural organic matter from surface waters . J. Membr. Sci. , 269 : 1 – 14 .
   Web of Science ®Google Scholar
• Pharoah , J. G. , Djilali , N. and Vickers , G. W. 2000 . Fluid mechanics and mass transfer in centrifugal membrane separation . J. Membr. Sci. , 176 : 277 – 289 .
   Google Scholar
• Culkin , B. , Plotkin , A. and Monroe , M. 1998 . Solve membrane fouling problems with high shear filtration . Chem. Eng. Prog. , 94 : 29 – 33 .
   Web of Science ®Google Scholar
• Ding , L. , Al-Akoum , O. , Abraham , A. and Jaffrin , M. Y. 2002 . Milk protein concentration by ultrafiltration with rotating disk modules . Desalination , 144 : 307 – 311 .
   Google Scholar
• Blatt , W. F. , Dravid , A. , Michael , A. S. and Nelsen , L. 1970 . “ Solute polarization and cake formation in membrane ultrafiltration: causes, consequences and control techniques ” . In Membrane Science and Technology , 47 – 97 . New York : Plenum Press .
   Google Scholar
• Wijmans , J. G. , Nakao , S. and Smolders , C. A. 1984 . Flux limitation in ultrafiltration: osmotic pressure model and gel layer model . J. Membr. Sci. , 20 : 115 – 124 .
   Web of Science ®Google Scholar
• Danes , F. E. , Boriou , B. and Poyen , S. 1990 . Effects of diffusion and osmosis on flux decline during ultrafiltration with total rejection on an unstirred batch cell . J. Membr. Sci. , 50 : 177 – 187 .
   Web of Science ®Google Scholar
• Henriques , J. , Gil , P. , Dourado , A. and Duarte-Ramos , H. 1999 . Application of a recurrent neural network in online modelling of real-time systems , 1 – 6 . Greece : Orthodox Academy of Crete .
   Google Scholar
• Jeng , C. C. and Yang , I. C. 2004 . Practical implementation of backpropagation networks in a low-cost pc cluster . Neural Inform. Process. , 4 : 33 – 37 .
   Google Scholar
• Chen , S. , Cowan , C. F. N. and Grant , P. M. 1991 . Orthogonal least squares learning algorithm for radial basis function networks . IEEE Trans. Neural Networks , 2 : 302 – 309 .
   PubMed Web of Science ®Google Scholar
• Hopfield , J. J. and Tank , D. W. 1986 . Computing with neural circuits . Science , 233 : 625 – 633 .
   PubMed Web of Science ®Google Scholar
• Rumelhart , D. E. , Hinton , G. E. and Williams , R. J. 1986 . Learning internal representations by error propagation , Cambridge , MA : MIT Press .
   Google Scholar
• Samad , T. Back-propagation improvements based on heuristic arguments . Proc. International Joint Conference on Neural Networks . pp. 565 – 568 . Washington
   Google Scholar
• Miniani , A. A. and Williams , R. D. Acceleration of backpropagation through learning rate and momentum adaptation . Proc. International Joint Conference on Neural Networks . pp. 676 – 679 . San Diego , CA
   Google Scholar
• Jacobs , R. A. 1988 . Increased rates of convergence through learning rate adaptation . Neural Networks , 1 : 295 – 308 .
   Web of Science ®Google Scholar
• Salvetti , A. and Wilamowski , B. M. 1994 . “ Introducing Stochastic Process within the Backpropagation Algorithm for Improved Convergence ” . In Artificial Neural Networks in Engineering 13 – 16 . St. Louis , Missouri
   Google Scholar
• Al-Falou , A. A. and Trummer , D. 2003 . Identifiability of recurrent neural networks . Vienna University of Technology, Econometric Theory , 19 : 812 – 828 .
   Google Scholar
• Elman , J. L. 1990 . Finding structure in time . Cog. Sci. , 14 : 179 – 211 .
   Web of Science ®Google Scholar
• Galan-Marin , G. , Merida-Casermeiro , E. and Munoz-Pere , J. 2003 . Modelling the competitive Hopfield Networks for the maximum clique problem . Comp. Ops. Res. , 30 : 603 – 624 .
   Google Scholar
• Hopfield , J. J. 1982 . Neural networks and physical systems with emergent collective computational abilities , 2554 – 2558 . Proc. Nat. Acad. Sci. USA .
   Google Scholar
• Hopfield , J. J. 1984 . Neurons with graded response have collective computational properties like those of two-state neurons , 3088 – 3092 . Proc. Nat. Acad. Sci. USA .
   Google Scholar
• Tank , D. W. and Hopfield , J. J. 1987 . Collective computation in neuron like circuits . Sci. Am. , 257 : 104 – 114 .
   PubMed Web of Science ®Google Scholar
• Smith , K. A. , Abramson , D. and Duke , D. 2003 . Hopfield neural networks for timetabling: formulations, methods, and comparative results . Comp. Ind. Eng. , 44 : 283 – 305 .
   Web of Science ®Google Scholar
• Chen , S. , Billings , S. A. and Luo , W. 1989 . Orthogonal least squares methods and their application to non-linear system identification . Int. J. Control , 50 : 1873 – 1896 .
   Web of Science ®Google Scholar
• Cheryan , M. 1998 . Ultrafiltration and Microfiltration Handbook , Lancaster , PA : Technomic Publications .
   Google Scholar
• Gao , P. , Zhang , L. , Cheng , K. and Zhang , H. 2007 . A new approach to performance analysis of a seawater desalination system by an artificial neural network . Desalination , 205 : 147 – 155 .
   Google Scholar