scholarly journals Application of the Artificial Neural Network (ANN) Approach for Prediction of the Kinetic Parameters of Lignocellulosic Fibers

Textiles ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 258-267
Author(s):  
Heitor Luiz Ornaghi ◽  
Roberta Motta Neves ◽  
Francisco M. Monticeli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kinetic Parameters ◽  
Heating Rate ◽  
Maximum Temperature ◽  
Heating Rates ◽  
Science Field ◽  
Lignocellulosic Fibers ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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.

scholarly journals Application of the Artificial Neural Network (ANN) Approach for Prediction of the Kinetic Parameters of Lignocellulosic Fibers

2021 ◽  
Author(s):  
Heitor Luiz Ornaghi Júnior ◽  
Roberta Motta Neves ◽  
Francisco Maciel Monticeli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kinetic Parameters ◽  
Heating Rate ◽  
Maximum Temperature ◽  
Heating Rates ◽  
Science Field ◽  
Lignocellulosic Fibers ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Lignocellulosic fibers are widely applied as composite reinforcement due to their properties. The thermal degradation behavior determines the maximum temperature in which the fiber can be applied without significant mass loss. It is possible to determine these temperatures using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined 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, curaua TG curves were obtained at three different heating rates (5, 10, 20, and 40 °C.min-1) and Vyazovkin kinetic parameters were obtained. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM). Curves at any heating rate between the minimum and maximum experimental heating rates were obtained with high reliability. Finally, Vyazovkin kinetic parameters were tested again with the new curves showing similar kinetic parameters from 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.

Artificial neural network (ANN) prediction of kinetic parameters of (CRFC) composites

2008 ◽  
Vol 44 (2) ◽  
pp. 656-663 ◽  
Author(s):  
E.M. Bezerra ◽  
M.S. Bento ◽  
J.A.F.F. Rocco ◽  
K. Iha ◽  
V.L. Lourenço ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kinetic Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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