Prediction of the pH and the temperature-dependent swelling behavior of Ca2 +-alginate hydrogels by artificial neural networks

Chemical Engineering Science

Volumn 63, Issue 11, 2008, Pages 2913-2919

  Koç, Mehmet Levent ^a   Özdemir, Ülkü ^a   Imren, Dilek ^a  

^a CUMHURIYET UNIVERSITY   (Turkey)

Author keywords

Artificial intelligence; Hydrogels; Neural network; Swelling behavior

Indexed keywords

ALGINATE; NEURAL NETWORKS; PH EFFECTS; REGRESSION ANALYSIS; SWELLING;

ARTIFICIAL NEURAL NETWORK MODELS; SWELLING BEHAVIOR;

HYDROGELS;

ALGINATE; HYDROGELS; NEURAL NETWORKS; PH EFFECTS; REGRESSION ANALYSIS; SWELLING;

EID: 43049175000     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2008.03.012     Document Type: Article

References (58)

1. Arya L.D., Titare L.S., and Kothari D.P. Determination of probabilistic risk of voltage collapse using radial basis function (RBF) network. Electric Power Systems Research 76 (2006) 426-434
2. Bajpai S.K., and Singh S. Analysis of swelling behavior of poly(methacrylamide-co-methacrylic acid) hydrogels and effect of synthesis conditions on water uptake. Reactive & Functional Polymers 66 (2006) 431-440
3. Balas C.E., and Koç L. Reliability analysis of rubble mound breakeaters by neural network model. Journal of Coastal Research SI 39 (2004) 1507-1510
4. Balas C.E., Koç L., and Balas L. Predictions of missing wave data by recurrent neuronets. Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE 130 (2004) 256-265
5. Baş D., and Boyaci I.H. Modeling and optimization II: comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction. Journal of Food Engineering 78 (2007) 846-854
6. Basherr I.A., and Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 43 (2000) 3-31
7. Berens A.R., and Hopfenberg H.B. Diffusion and relaxation in glassy polymer powders: 2. Separation of diffusion and relaxation parameters. Polymer 19 (1978) 489-496
8. Çelikoǧlu H.B. Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling. Mathematical and Computer Modelling 44 (2006) 640-658
9. Chang H.S., Park H., Kelly P., and Robinson J.R. Bioadhesive polymers as platforms for oral controlled drug delivery. 2. Synthesis and evaluation of some swelling, water-insoluble bioadhesive polymers. Journal of Pharmaceutical Sciences 74 (1985) 399-405
10. Chtioui Y., Panigrahi S., and Francl L. A generalized neural network and its application for leaf wetness prediction to forecast plant disease. Chemometrics and Intelligent Laboratory Systems 48 (1999) 47-58
11. Chu Y., Varanasi P.P., Mcglade M.J., and Varanasi S. pH-induced swelling kinetics of polyelectrolyte hydrogels. Journal of Applied Polymer Science 58 (1995) 2161-2176
12. Cigizoglu H.K., and Alp M. Generalized regression neural network in modelling river sediment yield. Advances in Engineering Software 37 (2006) 63-68
13. De S.K., Aluru N.R., Johnson B., Crone W.C., Beebe D.J., and Moore J. Equilibrium welling and kinetics of pH-responsive hydrogels: models, experiments, and simulations. Journal of Microelectromechanical Systems 11 (2002) 544-555
14. De Gersem H., and Hameyer K. Convergence improvement of the conjugate gradient iterative method for finite element simulations. The International Journal for Computation and Mathematics in EEE 20 (2001) 90-97
15. Eiselt P., Yeh J., Latvala R.K., Shea L.D., and Mooney D.J. Porous carriers for biomedical applications based on alginate hydrogels. Biomaterials 21 (2000) 1921-1927
16. Emileh A., Farahani E.V., and Imani M. Swelling behavior, mechanical properties and network parameters of pH- and temperature-sensitive hydrogels of poly(2-dimethyl amino) ethyl methacrylate-co-butyl methacrylate). European Polymer Journal 43 (2007) 1986-1995
17. Gimeno R.M.G., Martinez C.H., Perez R.R., and Cosano G.Z. Modelling the growth of leuconostoc mesenteroides by artificial neural network. International Journal of Food Microbiology 105 (2005) 317-332
18. Harpham C., and Dawson C.W. The effect of different basis functions on a radial basis function network for time series prediction: a comparative study. Neurocomputing 69 (2006) 2161-2170
19. Haug A., Larsen B., and Smidsrod O. Uronic acid sequence in. alginate from different sources. Carbohydrate Research 32 (1974) 217-225
20. Hervas C., and Silva M. Memetic algorithms-based artificial multiplicative neural models selection for resolving multi-component mixtures based on dynamic responses. Chemometrics and Intelligent Laboratory Systems 85 (2007) 232-242
21. Hervas C., Zurera G., Garcia R.M., and Martinez R.M. Optimization of computational neural network for its application to the prediction of microbial growth in foods. Food Science and Technology International 7 (2001) 159-163
22. Hervas C., Estudillo A.C., Silva M., and Serrano J.M. Improving quantification of highly overlapping chromatographic peaks by using product unit neural networks modeled by an evolutionary algorithm. Journal of Chemical Information Model 45 (2005) 894-903
23. Ho Y.S. Second-order kinetic model for the sorption of cadmium onto tree fern: a comparison of linear and non-linear methods. Water Research 40 (2006) 119-125
24. İmren D., Gümüşderelioǧlu M., and Güner A. Synthesis and characterization of dextran hydrogels prepared with chlor- and nitrogen-containing crosslinkers. Journal of Applied Polymer Science 102 (2006) 4213-4221
25. Jang J.S.R., Sun T.S., and Mizutani E. Neuro-Fuzzy and Soft Computing-a Computational Approach to Learn and Machine Intelligence (1997), Prentice-Hall, Englewood Cliffs, NJ
26. Katime I., Veleda J.L., Novoa R., DeApodace E.D., Puig J., and Mendizabal E. Swelling kinetics of poly(acrylamide)/poly(mono-n-alkyl itaconates) hydrogels. Polymer International 40 (1996) 281-286
27. Kim B., La Flamme K., and Peppas N.A. Dynamic swelling behavior of pH-sensitive anionic hydrogels used for protein delivery. Journal of Applied Polymer Science 89 (2003) 1606-1613
28. Kim B., Lee D.W., Park K.Y., Choi S.R., and Choi S. Prediction of plasma etching using a randomized generalized regression neural network. Vacuum 76 (2004) 37-43
29. Kim K.B., Park J.B., Choi Y.H., and Chen G. Control of chaotic dynamical systems using radial basis function network approximators. Information Science 130 (2000) 165-183
30. Lu S., and Hogg B.W. Dynamic nonlinear modelling of power plant by physical principles and neural network. Electrical Power and Energy Systems 22 (2000) 67-78
31. Mohandes M., Balghonaim A., Kassas M., Rehman S., and Halawani T.O. Use of radial basis functions for estimating monthly mean daily solar radiation. Solar Energy 68 (2000) 161-168
32. Moreira G.A., Micheloud G.A., Beccaria A.J., and Goicoechea H.C. Optimization of the bacillus thuringiensis var. Kurstaki HD-1 δ-endotoxins production by using experimental mixture design and artificial neural networks. Biochemical Engineering Journal 35 (2007) 48-55
33. Mundra P., Desai K., and Lele S.S. Application of response surface methodology to cell immobilization for the production of palatinose. Bioresource Technology 98 (2007) 2892-2896
34. Myers R.H., and Montgomery C.D. Response Surface Methodology: Process and Product Optimization Using Designed Experiments (2002), Wiley, Canada
35. Nambiar E.K.K., and Ramamurthy K. Models relating mixture composition to the density and strength of foam concrete using response surface methodology. Cement and Concrete Composites 28 (2006) 752-760
36. Omidian H., Hashemi S.A., Sammes P.G., and Meldrum I. A model for the swelling of superabsorbent polymers. Polymer 39 (1998) 6697-6704
37. Omidvar A.E. Configuring radial basis function network using fractal scaling process with application to chaotic time series prediction. Chaos Solitons and Fractals 22 (2004) 757-766
38. Onwubolu G.C., Davim J.P., Oliveira C., and Cardoso A. Prediction of clad angle in laser cladding by powder using response surface methodology and scatter search. Optics and Laser Technology 39 (2007) 1130-1134
39. Patterson D.W. Artificial Neural Networks Theory and Applications (1996), Prentice Hall International Editions, Korea
40. Peniche C., Cohen M.E., Vazquez B., and SanRoman J. Water sorption of flexible networks based on 2-hydroxyethyl methacrylate triethylenglycol dimethacrylate copolymers. Polymer 38 (1997) 5977-5982
41. Peppas N.A., and Brazel C.S. Mechanisms of solute and drug transport in relaxing, swellable, hydrophilic glassy polymers. Polymer 40 (1999) 3383-3398
42. Quintana J.R., Valderruten N.E., and Katime I. Synthesis and swelling kinetics of poly(dimethylaminoethyl acrylate methyl chloride quaternary-co-itaconic acid) hydrogels. Langmuir 15 (1999) 4728-4730
43. Rastello De Boisseson M., Leonard M., Hubert P., Marchal P., Stequert A., Castel C., Favre E., and Dellacherie E. Physical alginate hydrogels based on hydrophobic or dual hydrophobic/ionic interactions: bead formation structure and stability. Journal of Colloid and Interface Science 273 (2004) 131-139
44. Razavi M.A., Mortazavi A., and Mousavi M. Dynamic modelling of milk ultrafiltration by artificial neural network. Journal of Membrane Science 220 (2003) 47-58
45. Schott H. Swelling kinetics of polymers. Journal of Macromolecular Science Physics 31B (1992) 1-9
46. Schwenker F., Dietrich C., Kestler H.A., Riede K., and Palm G. Radial basis function neural networks and temporal fusion for the classification of bioacoustic time series. Neurocomputing 51 (2003) 265-275
47. Shibayama M., and Tanaka T. Volume phase transition and related phenomena of polymer gels. Advances in Polymer Science 109 (1993) 1-62
48. Siegel, R.A., 1990. pH sensitive gels: swelling equilibria, kinetics and application for drug delivery. Pulsed and Self-Regulated Drug Delivery. CRC Press, pp. 129-157.
49. Stevin H., and Gehrke S.H. Synthesis, equilibrium swelling, kinetics, permeability and applications of environmentally responsive gels. Advances in Polymer Science 110 (1993) 81-144
50. Svozil D., Kvasnicka V., and Pospichal J. Introduction to multilayer feed forward neural net. Chemometrics and Intelligent Laboratory Systems 39 (1997) 43-62
51. Takka S., and Acarturk F. Calcium alginate microparticles for oral administration: I: effect of sodium alginate type on drug release and drug entrapment efficiency. Journal of Microencapsulation 16 (1999) 275-290
52. Valencia L.E.C., Ramírez A.M., Guzman E.A., and García M.E.C. Modelling of the performance of asphalt pavement using response surface methodology-the kinetics of the aging. Building and Environment 42 (2007) 933-939
53. Ventura S., Silva M., Perezbendito D., and Hervas C. Artificial neural networks for estimation of kinetic analytical parameters. Analytical Chemistry 67 (1995) 1521-1525
54. Wang C.J., Li Y., and Hu Z.B. Swelling kinetics of polymer gels. Macromolecules 30 (1997) 4727-4732
55. Yanfeng C., and Min Y. Swelling kinetics and stimuli-responsiveness of poly(DMAEMA) hydrogels prepared by UV-irradation. Radiation Physics and Chemistry 61 (2001) 65-68
56. Yao X., Wang Y., Zhang X., Zhang R., Liu M., Hu Z., and Fan B. Radial basis function neural network-based QSPR for the prediction of critical temperature. Chemometrics and Intelligent Laboratory Systems 62 (2002) 217-225
57. Zhang A., and Zhang L. RBF neural networks for the prediction of building interference effects. Computers and Structures 82 (2004) 2333-2339
58. Zou H.F., Xia G.P., Yang F.T., and Wang H.Y. An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing 70 (2007) 2913-2923