期刊论文详细信息
Textiles
Application of the Artificial Neural Network (ANN) Approach for Prediction of the Kinetic Parameters of Lignocellulosic Fibers
Francisco M. Monticeli1  Heitor Luiz Ornaghi2  Roberta Motta Neves3 
[1] Departament of Materials and Technology, São Paulo State University (Unesp), School of Engineering, Guaratinguetá 12516-410, SP, Brazil;Department of Materials Engineering, Federal University for Latin American Integration (UNILA), Edifício Comercial Lorivo, Av. Silvio Américo Sasdelli, 1842, Vila A, Foz do Iguaçu 85866-000, PR, Brazil;Post-Graduation Program in Mining, Metallurgical and Materials, Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves, 9500, Setor 4, Prédio 74, Sala 211, Porto Alegre 91501-970, RS, Brazil;
关键词: lignocellulosic fiber;    thermal degradation;    kinetic analysis;    artificial neural network;   
DOI  :  10.3390/textiles1020013
来源: DOAJ
【 摘 要 】

Lignocellulosic fibers are widely applied as reinforcement in polymer composites due to their properties. The thermal degradation behavior governs the maximum temperature at which the fiber can be applied without significant mass loss. It is possible to determine this temperature using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined by using Arrhenius-based equations, and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost and time. Methods to predict thermogravimetric curves can be very useful in the materials science field, and in this sense, mathematical approaches are powerful tools, if well employed. For this reason, in the present study, thermogravimetric curves from curaua fiber were obtained at four different heating rates (5, 10, 20 and 40 °C·min−1) and Vyazovkin kinetic parameters were obtained using free available software. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM) aiming to obtain curves at any heating rate between the minimum and maximum experimental heating rates. Finally, Vyazovkin kinetic parameters were tested again, with the new predicted curves at the heating rates of 7, 15, 30 and 50 °C·min−1. Similar values of the kinetic parameters were obtained compared to the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications.

【 授权许可】

Unknown   

  文献评价指标  
  下载次数:0次 浏览次数:0次