scholarly journals Prediction and Performance Investigation of Polyurethane Foam as Thermal Insulation Material for Roofing Sheet Using Artificial Neural Network

2021 ◽  
Vol 82 (1) ◽  
pp. 113-125
Author(s):  
V. B. Essien ◽  
Christian A. Bolu ◽  
Imhade P. Okokpujie ◽  
Joseph Azeta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Polyurethane Foam ◽  
Thermal Insulation ◽  
Residential Buildings ◽  
Insulation Material ◽  
Indoor Temperature ◽  
Thermal Insulation Material ◽  
Artificial Neural ◽  
Roofing System

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.

scholarly journals Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

2019 ◽  
Vol 9 (6) ◽  
pp. 1088 ◽  
Author(s):  
Changhyuk Kim ◽  
Jung-Yoon Lee ◽  
Moonhyun Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Residential Buildings ◽  
Dynamic Stiffness ◽  
Test Results ◽  
High Rise ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sound Reduction ◽  
Reduction Capacity

High-rise residential buildings are constructed in countries with high population density in response to the need to utilize small development areas. As many high-rise buildings are being constructed, issues of floor impact sound tend to occur in buildings. In general, resilient materials are implemented between the slab and the finishing mortar to control the floor impact sound. Various mechanical properties of resilient materials can affect the floor impact sound. To investigate the impact sound reduction capacity, various experimental tests were conducted. The test results show that the floor impact sound reduction capacity has a close relationship with the dynamic stiffness of resilient materials. A total of six different kinds of resilient materials were loaded under four loading conditions. The test results show that loading time, loading, and material properties influence the change in dynamic stiffness. Artificial neural network (ANN) technique was implemented to obtain the responses between the deflection and dynamic stiffness. Three different algorithms were considered in the ANN models and the trained results were analyzed based on the root mean square error. The feasibility of using the ANN technique was verified with a high and consistent level of accuracy.

scholarly journals A NILM method for cooling load disaggregation based on artificial neural network

2019 ◽  
Vol 111 ◽  
pp. 05020 ◽  
Author(s):  
Ziwei Xiao ◽  
Jiaqi Yuan ◽  
Wenjie Gang ◽  
Chong Zhang ◽  
Xinhua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Management ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Building Energy Management ◽  
Load Disaggregation ◽  
Artificial Neural ◽  
Cooling Loads

The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.

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 ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Polyurethane Foam ◽  
Flexible Polyurethane Foam ◽  
Artificial Neural ◽  
Flexible Polyurethane

scholarly journals Experimental Study on Polyurethane Foam as Thermal Insulation Material

1966 ◽  
Vol 1966 (120) ◽  
pp. 236-245
Author(s):  
Shiro Watanabe ◽  
Akira Kamimura ◽  
Yoshiyuki Izutsu ◽  
Masaru Ishida
Keyword(s):  
Experimental Study ◽  
Polyurethane Foam ◽  
Thermal Insulation ◽  
Insulation Material ◽  
Thermal Insulation Material

scholarly journals Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1514
Author(s):  
Morteza Nazerian ◽  
Fateme Naderi ◽  
Ali Partovinia ◽  
Antonios N. Papadopoulos ◽  
Hamed Younesi-Kordkheili
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Polyurethane Foam ◽  
Sandwich Panel ◽  
Bending Strength ◽  
Validation Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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.

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