Modeling of glycolysis of flexible polyurethane foam wastes by artificial neural network methodology

2015 ◽  
Vol 64 (9) ◽  
pp. 1111-1120 ◽  
Author(s):  
Niusha Hekmatjoo ◽  
Zahed Ahmadi ◽  
Faramarz Afshar Taromi ◽  
Babak Rezaee ◽  
Farkhondeh Hemmati ◽  
...  
Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1514
Author(s):  
Morteza Nazerian ◽  
Fateme Naderi ◽  
Ali Partovinia ◽  
Antonios N. Papadopoulos ◽  
Hamed Younesi-Kordkheili

The present study evaluates and compares predictions on the performance and the approaches of the response surface methodology (RSM) and the artificial neural network (ANN) so to model the bending strength of the polyurethane foam-cored sandwich panel. The effect of the independent variables (formaldehyde to urea molar ratio (MR), sandwich panel thickness (PT) and the oxidized protein to melamine-urea-formaldehyde synthesized resin weight ratio (WR)) was examined based on the bending strength by the central composite design of the RSM and the multilayer perceptron of the ANN. The models were statistically compared based on the training and validation data sets via the determination coefficient (R2), the root mean squares error (RMSE), the absolute average deviation (AAD) and the mean absolute percentage error (MAPE). The R2 calculated for the ANN and the RSM models was 0.9969 and 0.9960, respectively. The models offered good predictions; however, the ANN model was more precise than the RSM model, thus proving that the ANN and the RSM models are valuable instruments to model and optimize the bending properties of the sandwich panel.


Author(s):  
V. B. Essien ◽  
Christian A. Bolu ◽  
Imhade P. Okokpujie ◽  
Joseph Azeta

The prediction and application of Polyurethane Foam in developing roofing sheets cannot be over-emphasized when considering the environmental changes coursed by thermal radiation. This paper presents an artificial neural network application to model and predict the indoor temperature resistance of polyurethane (PU) roofing in residential buildings. The study employed a data logger to measure the indoor and outdoor temperatures for three simulation environments (i.e., morning, afternoon, and evening) for two hours each. Furthermore, the authors employed the Levenberg-Marquardt algorithm to transform and predict the indoor temperature obtained in the residential building's polyurethane roofing house. The result shows that the PU roofing system could absorb the heat and reduce the house model's temperature with 6.9% in the morning, afternoon 15.8%, and 6.8% in the evening when compared with the temperature outdoor environment. The ANN was also able to train, test, and validate the experimental temperature results with 92.86%, 93.92%, and 95%, respectively. The mean square error and a testing error occurs at 0.1707 and 0.1689. Therefore, this study concluded that ANN's application in predicting the thermal insulation material such as the PU roofing system is highly efficient and will increase the manufacturer's performance evaluation. It has also created significant awareness of the community in employing the PU roofing system for residential buildings, which will reduce the rate of energy consumption in buildings.


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.


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