Actas de congresos
Artificial Neural Networks Associated To Calorimetry To Preview Polymer Composition Of High Solid Content Emulsion Copolymerizations
Registro en:
0780390482; 9780780390485
Proceedings Of The International Joint Conference On Neural Networks. , v. 4, n. , p. 2237 - 2242, 2005.
10.1109/IJCNN.2005.1556249
2-s2.0-33750138797
Autor
Giordani D.S.
Dos Santos A.M.
Krahenbuhl M.A.
Lona L.M.F.
Institución
Resumen
Artificial Neural Networks (ANN) have demonstrated to be powerful tools to model non linear systems, such as high solid content latexes produced by emulsion polymerisation. This system has a great importance in the polymeric industry, essentially for environmental reasons, since they usually have water as continuous phase. In order to propose technical and economically feasible alternatives to control polymeric structure, this work is aimed to develop a new methodology based on artificial neural networks associated with calorimetry to preview polymeric structure. The designed artificial neural networks presented excellent results when tested with process condition variations as well as when they were submitted to test concerning to the variation on the proportion of monomers in the latex formulation. Hence, it was possible to conclude that artificial neural networks, associated to calorimetry, lead to an efficient method to preview the polymer composition in emulsion copolymerizations. © 2005 IEEE. 4
2237 2242 Asua, J.M., Urretabizkaia, A., Arzamendi, G., Unzué, M.J., (1994) J. Polym. Sci.: Part A: Polym. Chem., 32, p. 1779 El-Aasser, M.S., Leiza, J.R., Sudol, E.D., (1997) J. Appl. Polym. Sci., 64, p. 1797 Lovell, P.A., El-Aasser, M.S., (1999) Emulsion Polymerization and Emulsion Polymers, , Ed. John Wiley and Sons, New York McKenna, T.F., Othman, S., Fevotte, G., Santos, A.M., Hamnmouri, H., (1998) DECHEMA Monographien, 134, p. 567. , Wiley-VCH, Berlin Zeaiter, J., Gomes, V.G., Romagnoli, J.A., Barton, G.W., Inferential conversion monitoring and control in emulsion polymerisation through calorimetric measurements (2002) Chemical Engineering Journal, 89, p. 37 Vieira, R.A.M., Embiruçu, M., Sayer, C., Pinto, J.C., Lima, E.L., Control strategies for complex chemical processes. Applications in polymerization processes (2003) Computers and Chemical Engineering, 27, p. 1307 Vicente, M., Leiza, J.R., Asua, J.M., Maximizing production and polymer quality (MWD and composition) in emulsion polymerization reactors with limited capacity of heat removal (2003) Chemical Engineering Science, 58, p. 215 Zorzetto, L.F.M., Maciel Filho, R., Wolf-Maciel, M.R., Process modelling development through artificial neural networks and hybrid models (2000) Computers and Chemical Engineering, 24, p. 1355 Fernandes, F.A.N., Lona, L.M.F., Development of polymer resins using neural networks (2002) Polímeros: Ciência e Tecnologia, 12 (3), p. 164 Zhang, Z., Friedrich, K., Artificial neural networks applied to polymer composites: A review (2003) Composites Science and Technology., 63, p. 1 Haykin, S., (1999) Neural Networks - A Comprehensivefoundation -2nd. Ed., , New York: Macmillan College Publishing Company Ramirez-Beltran, N.D., Jackson, H., Application of neural networks to chemical process control (1999) Computers & Industrial Engineering, 37, p. 387 Yu, D.L., Gomm, J.B., Implementation of neural network predictive control to a multivariable chemical reactor (2003) Control Engineering Practice, 11 (11), p. 1315 Laugier, S., Richon, D., Use of artificial neural networks for calculating derived thermodynamic quantities from volumetric property data (2003) Fluid Phase Equilibria, 210, p. 247 Boillereaux, L.A., Cadet, C.B., Le Bail, A., Thermal properties estimation during thawing via real-time neural network learning (2003) Journal OfFood Engineering, 57, p. 17 Svozil, D., Kvasničva, V., Pospíchal, J., (1997) Chemometrics and Intelligent Laboratory Systems, 39, p. 43 Dubé, M.A., Penlidis, A., A systematic approach to the study of multicomponent polymerization kinetics - The butyl acrylate /methyl methacrylate/vinyl acetate example: 1. Bulk Polymerization (1995) Polymer, 36, p. 587 Févotte, G., Barudio, J., Guillot, J., An adaptive inferential measurement strategy for on-line monitoring of conversion in polymerization processes (1996) Thermochimica Acta, 289, p. 223 Garson, G.D., Interpreting neural network connection weights (1991) Artificial Inteligence Expert, 6, p. 47 Othman, N., Santos, A.M., Févotte, G., McKenna, T.F., Evaluation of emulsion polymerisation kinetics using non-linear state estimator (2000) Macromol. Symp., 150, pp. 109-114