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

Akyüz, İlker; Özşahin, Şükrü; Tiryaki, Sebahattin; Aydın, Aytaç

doi:10.1007/s10098-017-1342-0

Cited by 14 publications

(5 citation statements)

References 29 publications

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“…MAPE is one of the most important performance criteria. In the literature, many researchers have determined the robustness of different models by using this performance criterion (Bardak et al 2016, Akyüz et al 2017. The MAPE values were 2.856% for the training set and 8.556% for the testing set.…”

Section: Resultsmentioning

confidence: 99%

“…The ANN approach has been widely employed in wood science to model input-output relationships. ANN applications to wood science include analyzing moisture in wood (Avramidis and Wu 2007), predicting fracture toughness (Samarasinghe et al 2007), classifying wood veneer defects (Castellani and Rowlands 2008), wood recognition (Khalid et al 2008), optimization of process parameters in oriented strand board manufacturing (Özşahin 2012(Özşahin , Ozsahin 2013, predicting the bonding strength of wood joints (Bardak et al 2016), determination of optimum power consumption in wood machining (Tiryaki et al 2016), prediction of formaldehyde emission (Akyüz et al 2017), and prediction of surface roughness and adhesion strength of wood (Özşahin and Singer 2019). These studies have shown that the ANN approach produces highly successful results.…”

Section: Introductionmentioning

confidence: 99%

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The use of an artificial neural network for predicting the gloss of thermally densified wood veneers

Özşahin¹,

Singer

2021

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In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The use of an artificial neural network for predicting the gloss of thermally densified wood veneers

Özşahin¹,

Singer

2021

BALT FOR

Self Cite

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“…A main problem with this approach was that it used the concentration from a previous time step to estimate the emission rate for the current time step because the current concentration is unknown. To improve the modeling accuracy for predicting time-resolved concentrations, we developed an approach to use hourly air exchange rate as a surrogate for the concentration in the bulk room air, which has been used in previous studies [ 14 , 38 , 39 , 40 ].…”

Section: Methodsmentioning

confidence: 99%

“…Those data-drive approaches were expanded for predicting formaldehyde emission rate and/or concentrations for certain materials in the chamber or a whole building in recent studies. Akyüz et al presented an implantation of artificial neural networks (ANN) for modeling the formaldehyde emission from particleboard based on manufacturing variables, including wood-glue moisture content, density of board, and pressing temperature [ 39 ]. Ouaret et al developed an approach using Fourier transform and two nonlinear model: threshold autoregressive (TAR) and Chaos dynamics models to forecast the formaldehyde concentration 12 h ahead in a regularly occupied office with diurnal pattern [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

A Time-Varying Model for Predicting Formaldehyde Emission Rates in Homes

Zhao

Walker

Sohn

et al. 2022

IJERPH

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Recent studies have succeeded in relating emissions of various volatile organic compounds to material mass diffusion transfer using detailed empirical characteristics of each of the individual emitting materials. While significant, the resulting models are often scenario specific and/or require a host of individual component parameters to estimate emission rates. This study developed an approach to estimate aggregated emissions rates based on a wide number of field measurements. We used a multi-parameter regression model based on previous mass transfer models to predict formaldehyde emission rate for a whole dwelling using field-measured, time-resolved formaldehyde concentrations, air exchange rates, and indoor environmental parameters in 63 California single-family houses built between 2011 and 2017. The resulting model provides time-varying formaldehyde emission rates, normalized by floor area, for each study home, assuming a well-mixed mass balance transport model of the home, and a well-mixed layer transport model of indoor surfaces. The surface layer model asserts an equilibrium concentration within the surface layer of the emitted materials that is a function of temperature and RH; the dwelling ventilation rate serves as a surrogate for indoor concentration. We also developed a more generic emission model that is suitable for broad prediction of emission for a population of buildings. This model is also based on measurements aggregated from 27 homes from the same study. We showed that errors in predicting household formaldehyde concentrations using this approach were substantially less than those using a traditional constant emission rate model, despite requiring less unique building information.

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“…For exemple, Bardak et al (2016), Palacios et al (2018), Andre et al (2008), Bardak (2018), and Watanabe et al (2015) used ANN methods to model the mechanical properties of the particleboard and fiberboard. Akyuz et al (2017) also examined a similar method to model the formaldehyde emission.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mechanical and Flame Retardant Properties of Medium Density Fiberboard Using Artificial Neural Network

Nazerian

Shirzaii

Mohebbigargari

et al. 2020

CERNE

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

Cited by 14 publications

References 29 publications

The use of an artificial neural network for predicting the gloss of thermally densified wood veneers

The use of an artificial neural network for predicting the gloss of thermally densified wood veneers

A Time-Varying Model for Predicting Formaldehyde Emission Rates in Homes

Evaluation of Mechanical and Flame Retardant Properties of Medium Density Fiberboard Using Artificial Neural Network

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