An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process

• Published in: Clean technologies and environmental policy Vol. 19; no. 5; pp. 1449 - 1458
• Main Authors: Akyüz, İlker, Özşahin, Şükrü, Tiryaki, Sebahattin, Aydın, Aytaç
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2017; Springer Nature B.V
• Subjects: Air temperature; Aldehydes; Artificial neural networks; Buildings; Carbon; Chemicals; Construction industry; Consumption; Data processing; Density; Earth and Environmental Science; Emission analysis; Environment; Environmental Economics; Environmental Engineering/Biotechnology; Environmental policy; Evaporation; Exposure; Formaldehyde; Indoor air quality; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; Manufacturing; Mathematical models; Moisture content; Networks; Neural networks; Organic compounds; Original Paper; Particle board; Pressing; Process parameters; Sustainable Development; Temperature effects; VOCs; Volatile organic compounds; Wood products; Formaldehyde emission; Particleboard; Neural networks; Prediction; Indoor air
• ISSN: 1618-954X; 1618-9558
• DOI: 10.1007/s10098-017-1342-0

Volatile organic compounds refer to a large class of carbon-based chemicals capable of evaporating easily into the air at room temperature. Formaldehyde is one of the best known volatile organic compounds, and long-term exposure to formaldehyde emission from wood-based building products in indoor air may cause many adverse health effects. This paper presents an implementation of artificial neural networks for modeling the formaldehyde emission from particleboard as a wood-based product based on wood-glue moisture content, density of board and pressing temperature, with the experimental data collected from Petinarakis and Kavvouras (Wood Res 51(1):31–40, 2006 ). With the constructed model, formaldehyde emission of particleboard could be predicted successfully, and the intermediate formaldehyde emission values not obtained from experimental investigation could be predicted for different combinations of manufacturing parameters. The results proved that the artificial neural network is a promising technique in predicting the formaldehyde emission from particleboard. In this regard, the findings of this study will help the manufacturing industries in obtaining the intermediate values of the formaldehyde emission without performing further experimental activity. The model thus may save time, reduce the consumption of experimental materials and design costs.