Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks

Özşahin, Şükrü; Murat, Miraç

doi:10.1007/s00107-017-1219-2

Cited by 32 publications

(15 citation statements)

References 31 publications

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“…Neural networks have also been shown to efficiently predict wood intrinsic characteristics such as moisture content (Ozsahin & Murat 2018), basic wood density , higher heating value (Estiati et al 2016), and energy density (Vale et al 2017), for which the aforementioned authors obtained better results than those reported in this study (RMSE% = 1.45%, R = 0.98, AD% = 0.14). However, those authors used basic wood density as a predictive variable.…”

Section: Modeling: Training Of Neural Networkmentioning

confidence: 45%

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Carrijo¹,

Miguel²,

Vale³

et al. 2020

iForest

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The use of artificial intelligence to generate information of the savanna's energy capacity may support sustainable management of those areas. We assessed the efficacy of artificial neural networks (ANNs) combined with satellite data to estimate the energy potential (Pe) for cerradão, a dense savannah-like vegetation type in Brazil. We conducted a forest inventory for measuring dendrometric variables and sampling woody materials and barks in a cerradão area in the state of Tocantins, Brazil. The Pe of cerradão biomass was estimated based on the observed higher calorific power and drier biomass values. Six vegetation indices were retrieved from a RapidEye image and tested for correlation to choose the optimum vegetation index for biomass modeling. The basal area and the Normalized Difference Vegetation Index were used as predictors in the Pe modeling. We estimated an average of 19.234 ± 0.411 GJ ton-1 and 19.878 ± 1.090 GJ ton-1 for higher heating values of the wood species and barks, respectively, and an average Pe of 1022.660 GJ ha-1. The best ANN showed an error of 11.3% by using a structure of two, eight, and one neurons in the input layer, in the hidden layer, and in the output layer, respectively, as well as activation functions of the tangential and sigmoidal types. The validation tests showed no significant difference between the observed and ANNpredicted values. Based on our results, we concluded that Pe can be efficiently predicted by combining ANNs and remotely sensed data, which ultimately is a promising tool for forest sustainable management of the cerrado ecosystems.

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Section: Modeling: Training Of Neural Networkmentioning

confidence: 45%

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Carrijo¹,

Miguel²,

Vale³

et al. 2020

iForest

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“…For instance, in the field of wood drying, Avramidis (2006) [13] predicted the drying rate of wood based on neural network construction model; Zhang Dongyan (2008) [14] constructed a neural network model for predicting wood MC during conventional drying;İlhan Ceylan (2008) [15] used neural network models to study wood drying characteristics; Watanabe (2013Watanabe ( , 2014 [16,17] employed artificial neural network model to predict the final moisture content of Sugi (Cryptomeria japonica) during drying and evaluate the drying stress on the wood surface. Ozsahin (2017) [18] utilized artificial neural networks to successfully predict the equilibrium moisture content and specific gravity of heat-treated wood. The artificial neural networks are widely used in the study of conventional drying characteristics, stress monitoring, and MC prediction of wood [19]; however, the use of neural networks to predict changes in the wood MC during high frequency drying has been rarely studied.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Chai

Chen

Cai

et al. 2018

Forests

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The moisture content (MC) control is vital in the wood drying process. The study was based on BP (Back Propagation) neural network algorithm to predict the change of wood MC during the drying process of a high frequency vacuum. The data of real-time online measurement were used to construct the model, the drying time, position of measuring point, and internal temperature and pressure of wood as inputs of BP neural network model. The model structure was 4-6-1 and the decision coefficient R2 and Mean squared error (Mse) of the training sample were 0.974 and 0.07355, respectively, indicating that the neural network model had superb generalization ability. Compared with the experimental measurements, the predicted values conformed to the variation law and size of experimental values, and the error was about 2% and the MC prediction error of measurement points along thickness direction was within 2%. Hence, the BP neural network model could successfully simulate and predict the change of wood MC during the high frequency drying process.

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“…The experiment results indicated that the model had better generalization ability and higher forecasting accuracy. In recent years, the artificial neural network (ANN) has a widespread application on nonlinear prediction, and some achievements have been made in the prediction of moisture content [9], [10]. Zhang et al [11] used the neural network method to identify the wood drying system and established the neural network model of the wood drying process.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Wood Moisture Content Based on Modified Ant Colony Algorithm to Optimize LSSVM Parameters

Sun

2020

IEEE Access

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Wood moisture content (WMC) is an important technical index used in the wood drying process, and assessing its change accurately and reliably is the key to improving wood drying quality. In order to improve the accuracy and reliability of WMC forecasting, a modeling method is proposed that uses a modified ant colony algorithm (MACA) to optimize the least square support vector machine (LSSVM). The MACA combines the large-step size global search with the small-step size local fine search to obtain the optimal parameter combination automatically and are tested by five standard functions. Then the MACA-LSSVM model is proposed to predict the WMC and compared with back propagation neural network (BP-NN), LSSVM model, and ant colony optimization LSSVM (ACO-LSSVM). The drying data from a small-sized wood drying kiln independently developed by Northeast Forestry University are taken as the samples for analyzing. The results indicate that the root mean square relative error (RMSRE) obtained by the proposed method (MACA-LSSVM) is only 1.82%, which is 0.77%, 0.50%, and 0.20% less than those of the BP-NN, LSSVM, and ACO-LSSVM models. The forecasting time are 0.0070 s, 0.0030 s, and 0.0010 s shorter, respectively. The relative error (RE) and the mean absolute error (MAE) are also lower than those of the latter three models. The MACA-LSSVM shows the characteristics of low computational complexity, fast convergence speed, high prediction accuracy and strong generalization ability, and the prediction effect is ideal. This model can provide the theoretical support for intelligent control of the wood drying process.INDEX TERMS Ant colony algorithm, least square support vector machine, parameter optimization, wood moisture content.

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Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks

Cited by 32 publications

References 31 publications

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Forecasting of Wood Moisture Content Based on Modified Ant Colony Algorithm to Optimize LSSVM Parameters

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