| Titre : |
Optimal prediction of PKS: RSO modified alkyd resin polycondensation process using discrete-delayed observations, ANN and RSM-GA techniques |
| Type de document : |
texte imprimé |
| Auteurs : |
Chigozie F. Uzoh, Auteur ; Okechukwu Dominique Onukwuli, Auteur |
| Année de publication : |
2017 |
| Article en page(s) : |
p. 607-620 |
| Note générale : |
Bibliogr. |
| Langues : |
Américain (ame) |
| Catégories : |
Algorithmes génétiques
Analyse multivariée
Plan d'expérience
Polyalkydes
Polycondensation
Réseaux neuronaux (informatique)
Surfaces de réponse (statistique)
|
| Index. décimale : |
667.9 Revêtements et enduits |
| Résumé : |
Alkyd resins are widely used in the paint industry and although they have a long history about 70–100 years, today the developments in alkyds are still welcome and innovations are still needed. Artificial neural network (ANN) and response surface methodology based on a 25−1 fractional factorial design were used as tools for simulation and optimization of the polycondensation process for autooxidative drying alkyd resin from palm kernel stearin: rubber seed oil blend of 70:30 ratio. A feed forward neural network model with Levenberg–Marquardt back propagation training algorithm was adapted to predict the responses (conversion Y1, viscosity Y2, and molecular weight average Y3). The studied input variables were reaction time, temperature, catalyst concentration, oil ratio, and stirring rate. The performance of the RSM and ANN model showed adequate prediction of the responses in terms of the process factors, with MRPD of ±4.47% (Y1), ±2.08% (Y2), ±8.92% (Y3) and ±6.50% (Y1), ±3.31% (Y2), ±10.20% (Y3), respectively. The sensitivity analysis showed that while reaction time is the most effective process parameter, the interaction of the five process variables produced the most significant effect on the studied responses with the overall minimum MSE of 0.079. The optimization task performed using a genetic algorithm linked to the RSM model gave a viable, nondominated optimal response and optimum operating conditions regarding the route to high-quality resin at reduced material and operational costs. Overall, coupled RSM-GA was found to be a better tool for modeling and optimization of the alkyd resin production. |
| Note de contenu : |
- MATERIALS AND METHODS : Materials - Synthesis of alkyd resin from modified palm kernel stearin - System prediction via RSM - Sensitivity analysis and system prediction via artificial neural network (ANN) model
- RESULTS AND DISCUSSION : Predictive model for system response aproximation via RSM - Reaction prediction via ANN - Optimization by coupled RSM-GA technique |
| DOI : |
10.1007/s11998-016-9881-6 |
| En ligne : |
https://link.springer.com/content/pdf/10.1007%2Fs11998-016-9881-6.pdf |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=28569 |
in JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH > Vol. 14, N° 3 (05/2017) . - p. 607-620
Aller sur edunet
Accueil
Optimal prediction of PKS: RSO modified alkyd resin polycondensation process using discrete-delayed observations, ANN and RSM-GA techniques in JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH, Vol. 14, N° 3 (05/2017)
