Optimal Modified Starch Content in UF Resin for Glulam Based on Bonding Strength Using Artificial Neural Network

Nazerian, Morteza; Akbarzade, Masood; Ghorbanezdad, Payam; Papadopoulos, Antonios N.; Vatankhah, Elham; Foti, Dafni; Koosha, Mojtaba

doi:10.3390/jcs6100279

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…Additional performance metrics, as outlined in Table 5, further corroborate the efficacy of the ANN model for predicting protein retention through ultrafiltration. Notably, the root mean square error (RMSE) and sum of squared error (SSE) values approaching zero signify a close fit between the observed and predicted values, emphasizing the model's accuracy and reliability in predicting protein retention [37].…”

Section: Prediction Of Protein Retention Bymentioning

confidence: 82%

Optimization of Process Parameters for the Production of Soy Protein by Ultrafiltration Using ANN

John,

Giri,

Subeesh

et al. 2024

Journal of Food Processing and Preservation

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The growing popularity of soy proteins among vegans and vegetarians, owing to their high protein content and widespread availability, has led to scientific studies on its various extraction methods mainly on ultrafiltration. This research employed artificial neural network (ANN) and Box-Behnken design (BBD) methodologies to predict the process parameters of ultrafiltration for the preparation of soy protein. Using BBD, the optimum process parameters of ultrafiltration were identified via the desirability function approach. The optimized permeate flux was 11.13 litres per hour (LPH) and 85.52% protein content in retentate. The identified ideal process parameters for ultrafiltration to achieve maximal protein retention encompassed a 10 kDa membrane module, a transmembrane pressure of 117 kPa (17 PSI), a volume concentration ratio of 3.5, diafiltration set at 1, and a flow rate of 65% of the pump capacity, exhibiting an absolute percent error value of 2.81. Employing these refined process parameters, the predicted value for protein retentate stood at 80.49%. The predictive accuracy of the model achieved an impressive 99.61% for protein retention. The ANN model effectively predicted the optimal ultrafiltration conditions, resulting in maximal protein retention and a protein content accuracy of 96.41% and 99.61%, respectively.

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Section: Prediction Of Protein Retention Bymentioning

confidence: 82%

Optimization of Process Parameters for the Production of Soy Protein by Ultrafiltration Using ANN

John,

Giri,

Subeesh

et al. 2024

Journal of Food Processing and Preservation

View full text Add to dashboard Cite

show abstract

“…As a result, the average values obtained can be used to optimize the preferred ANN model and achieve an optimal combination of the production parameters of a high quality product so that its result can be generalized, not only to laboratory, but also to industrial production and, in practice, some UF resin can be replaced by bio-based adhesives, according to the variables being examined. When replacing some UF resin with starch, Nazerian et al (2022) showed that the ANN application could evaluate the glue line bonding strength in glulam and recommended the application of the modified starch [48]. Optimization of the application of the modified plant protein together with the MUF resin in the production of the polyurethane-core-based sandwich panels indicated that the application of the ANN optimization methods could offer an effective estimate of the mechanical properties of bio-based composites [49].…”

Section: Predicting Mor By Annmentioning

confidence: 99%

An Improved Optimization Model to Predict the MOR of Glulam Prepared by UF-Oxidized Starch Adhesive: A Hybrid Artificial Neural Network-Modified Genetic Algorithm Optimization Approach

et al. 2022

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The purpose of the present article is to study the bending strength of glulam prepared by plane tree (Platanus Orientalis-L) wood layers adhered by UF resin with different formaldehyde to urea molar ratios containing the modified starch adhesive with different NaOCl concentrations. Artificial neural network (ANN) as a modern tool was used to predict this response, too. The multilayer perceptron (MLP) models were used to predict the modulus of rapture (MOR) and the statistics, including the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to validate the prediction. Combining the ANN and the genetic algorithm by using the multiple objective and nonlinear constraint functions, the optimum point was determined based on the experimental and estimated data, respectively. The characterization analysis, performed by FTIR and XRD, was used to describe the effect of the inputs on the output. The results indicated that the statistics obtained show excellent MOR predictions by the feed-forward neural network using Levenberg–Marquardt algorithms. The comparison of the optimal output of the actual values obtained by the genetic algorithm resulting from the multi-objective function and the optimal output of the values estimated by the nonlinear constraint function indicates a minimum difference between both functions.

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Comparative Analysis of ANN-MLP, ANFIS-ACOR and MLR Modeling Approaches for Estimation of Bending Strength of Glulam

2023

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Multiple linear regression (MLR), adaptive network-based fuzzy inference system–ant colony optimization algorithm hybrid (ANFIS-ACOR) and artificial neural network–multilayer perceptron (ANN-MLP) were tested to model the bending strength of Glulam (glue-laminated timber) manufactured with a plane tree (Platanus orientalis L.) wood layer adhered with different weight ratios (WR) of modified starch/urea formaldehyde (UF) adhesive containing different levels of nano-ZnO (NC) used at different levels of the press temperature (Tem) and time (Tim). According to X-ray diffraction (XRD) and stress–strain curves, some changes in the behavior of the product were seen. After selecting the best model through determining statistics such as the determination coefficient (R2) and root mean square error (RMSE), mean absolute error (MAE) and sum of squares error (SSE), the production process was optimized to obtain the highest modulus of rupture (MOR) using the Genetic Algorithm (GA) combined with MLP. It was determined that the MLP had the best accuracy in estimating the response. According to the MLP-GA hybrid, the optimum input values for obtaining the best response include: WR—49.1%, NC—3.385%, Tem—199.4 °C and Tim—19.974 min.

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Optimal Modified Starch Content in UF Resin for Glulam Based on Bonding Strength Using Artificial Neural Network

Cited by 3 publications

References 57 publications

Optimization of Process Parameters for the Production of Soy Protein by Ultrafiltration Using ANN

Optimization of Process Parameters for the Production of Soy Protein by Ultrafiltration Using ANN

An Improved Optimization Model to Predict the MOR of Glulam Prepared by UF-Oxidized Starch Adhesive: A Hybrid Artificial Neural Network-Modified Genetic Algorithm Optimization Approach

Comparative Analysis of ANN-MLP, ANFIS-ACOR and MLR Modeling Approaches for Estimation of Bending Strength of Glulam

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