Prediction of the equilibrium moisture content and specific gravity of thermally modified wood via an Aquila optimization algorithm back-propagation neural network model

The equilibrium moisture content and specific gravity of Uludag fir (Abies bornmüelleriana Mattf.) and hornbeam (Carpinus betulus L.) woods were investigated following heat treatment at different temperatures and times. Two prediction models were established based on the Aquila optimization algorith...

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Bibliographic Details
Published in:Bioresources Vol. 17; no. 3; pp. 4816 - 4836
Main Authors: Chen, Yao, Wang, Wei, Li, Ning
Format: Journal Article
Language:English
Published: Raleigh North Carolina State University 01.08.2022
Subjects:
Abies bornmulleriana
Accuracy
Algorithms
Artificial neural networks
Back propagation
Back propagation networks
Carpinus betulus
Density
Equilibrium
Heat
Heat treatment
Heat treatments
Humidity
Moisture content
Moisture effects
Neural networks
Neurons
Optimization
Optimization algorithms
Prediction models
Process parameters
Propagation
Search algorithms
Specific gravity
Temperature
Water content
ISSN:1930-2126, 1930-2126
Online Access:Get full text
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Summary:The equilibrium moisture content and specific gravity of Uludag fir (Abies bornmüelleriana Mattf.) and hornbeam (Carpinus betulus L.) woods were investigated following heat treatment at different temperatures and times. Two prediction models were established based on the Aquila optimization algorithm back-propagation neural network model. To demonstrate the effectiveness and accuracy of the proposed model, it was compared with a tent sparrow search algorithm-back-propagation network model, a back-propagation network model, and an artificial neural network. The results showed that the Aquila optimization algorithm back-propagation model reduced the root mean square error value of the original back-propagation model by 87% and 97%, respectively, and the decision coefficients (R2) of the equilibrium moisture content and specific gravity were 0.99 and 0.98; as such, the model optimization effect was obvious. Therefore, this paper provides an effective method for the optimization of the process parameters (such as heat treatment time, temperature, and air pressure) in wood heat treatment and related fields.
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ISSN:1930-2126
1930-2126
DOI:10.15376/biores.17.3.4816-4836