Water Sorption Isotherms of Dates: Modeling Using GAB Equation and Artificial Neural Network Approaches

Myhara, R.M.; Sablani, Shyam S.; Al-Alawi, S.M.; Taylor, Mark S.

doi:10.1006/fstl.1998.0492

Cited by 46 publications

(33 citation statements)

References 13 publications

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“…The R results are very similar to those in the references consulted, which range from 0.90 to 0.99 in the training process (Myhara et al, 1998;Hernández-Pérez et al, 2004;Peng et al, 2007;Diamantopoulou, 2005;Avramidis and Iliadis, 2005a).…”

Section: Resultssupporting

confidence: 83%

Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Fernández¹,

Esteban²,

Palacios³

et al. 2008

For. syst.

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The physical properties (specific gravity, moisture content, thickness swelling and water absorption) and mechanical properties (internal bond strength, bending strength and modulus of elasticity) were determined on 93 Spanish-manufactured standard particleboards of different thicknesses selected randomly at the end of the production process. The testing methods of the corresponding European standards (EN) were used, except in the case of the thickness swelling and absorption tests, for which the Spanish UNE standard was used. The thickness and the values obtained for the physical properties were entered into an artificial neural network in order to predict the mechanical properties of the board. The fit was compared with the usual multivariate regression models. The use of a neural network made it possible to obtain the values of bending strength, modulus of elasticity and internal bond strength of the boards utilizing the known data, not only of thickness, moisture content and specific gravity, but also of thickness swelling and water absorption. The neural network proposed is much better adapted to the observed values than any of the multivariate regression models obtained.Key words: wood-based panels, physico-mechanical properties, ANN, regression fit, predictive model. ResumenPredicción de propiedades mecánicas del tablero de partículas estándar mediante una red neuronal artificial y comparación con un modelo de regresión multivariante Se han determinado las propiedades físicas (densidad, humedad, hinchazón en espesor y absorción de agua) y mecánicas (tracción perpendicular a las caras, resistencia a flexión y módulo de elasticidad) de 93 tableros de partículas estándar de diferentes espesores, de fabricación española, elegidos aleatoriamente a la salida del proceso de producción, utilizando los métodos de ensayo recogidos en las normas EN correspondientes, excepto en los ensayos de hinchazón y absorción que se ha utilizado norma UNE (española). El espesor y los valores obtenidos de las propiedades físicas han sido introducidos en una red neuronal artificial (RNA) para predecir las propiedades mecánicas del tablero. El ajuste se ha comparado con los habituales modelos de regresión multivariante. La utilización de una red neuronal ha permitido obtener los valores de resistencia a flexión, módulo de elasticidad y resistencia a la tracción perpendicular a las caras de los tableros de partículas a través de los datos conocidos, no sólo de espesor, humedad y densidad sino también de hinchazón en espesor y absorción de agua. La red neuronal propuesta tiene una adecuación a los valores experimentales muy superior a cualquiera de los modelos de regresión multivariante obtenidos.Palabras clave: tableros derivados de la madera, propiedades físico-mecánicas, RNA, ajuste por regresión, modelo predictivo.

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Section: Resultssupporting

confidence: 83%

Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Fernández¹,

Esteban²,

Palacios³

et al. 2008

For. syst.

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“…They mentioned that ANN is suitable method to describe the adsorption process. In the scientific literature, there are a lot of papers regarding to ANN modeling on optimization of adsorption process (Myhara, Sablani, Al-Alawi, & Taylor, 1998;Aber, Daneshvar, Soroureddin, Chabok, & AsadpourZeynali, 2007;Yetilmezsoy & Demirel, 2008;Kumar & Porkodi, 2009;Çelekli & Geyik, 2011).…”

Section: Ann Modeling Of Adsorption By Ptpomentioning

confidence: 99%

Untitled

Donut¹,

Çavaş²

2017

Turk. J. Fish. Aquat. Sci.

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Importance of the artificial intelligence in the chemical processes has been increased in the recent studies. Although biosorption is widely studied topic in chemistry, modelling of biosorption data is based on very old equations. However, use of artificial intelligence in the biosorption based studies can give important clues to researchers. For this purpose, the biosorption of tetracycline by using Posidonia oceanica from the Mediterranean Sea was studied in this study. According to classical evaluation, the data were well in line with pseudo-second order kinetic and Langmuir's isotherm. In the artificial neural network modelling, the best back propagation algorithm, optimum number of hidden neuron and optimum training:validation:testing ratio were found as Bayesian Regulation, 16 and 70:10:20, respectively. In conclusion, P. oceanica based marine waste can be used in the development of high performance biosorbents for environmental pollutants. However, it should not be forgotten that P.oceanica is a threatened species; therefore, only dead leaves accumulated in recreational area should be collected and evaluated based on the permissions of governmental authorities. The results also exhibited that artificial neural network can also be used in the modelling of the biosorption data in which it helps scientists to estimate biosorption ratio correctly under various conditions.

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“…Fang et al also applied the ANN modelling to predict the physical properties of ground wheat in 1998 [18] while Hussain and Rahman, in 1999, predicted the thermal conductivity of fruits and vegetables with the application of ANN modelling [6]. Similarly, ANN modelling was applied by Myhara et al in 1998 for the prediction of isotherms of dates [11], Ni and Gunasekaran in 1998 [7] and Xie and Xiong in 1999 [13] also applied ANN modelling differently for the prediction of food quality. Recently, Sablani and Shayya in 2001 applied ANN modelling for the prediction of heat penetration parameters in Stumbo's method of thermal process calculations [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs)

Afis¹

2014

ABB

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Water Sorption Isotherms of Dates: Modeling Using GAB Equation and Artificial Neural Network Approaches

Cited by 46 publications

References 13 publications

Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Untitled

Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs)

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