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Recent Innovations in Chemical Engineering

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ISSN (Print): 2405-5204
ISSN (Online): 2405-5212

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Research Article

Predictive Modeling and Optimization of Plywood Drying: An Artificial Neural Network Approach

In Press, (this is not the final "Version of Record"). Available online 08 April, 2024
Author(s): Darío Guamán-Lozada*, María José Tobar Heredia, Mayra Zambrano-Vinueza, Roister Alexis Pesantes Ortiz, Marlon Moscoso-Martínez and Paul Marcelo Manobanda Pinto
Published on: 08 April, 2024

DOI: 10.2174/0124055204304381240403085107

Price: $95

Abstract

Introduction: This investigation delves into the optimization of the plywood drying process through the development of predictive models for output moisture content (MC_Out) and waviness. It focuses on bridging the gap in current methodologies by employing artificial neural networks (ANNs), optimized with genetic algorithms, to enhance prediction accuracy and process efficiency.

Materials and Methods: A comprehensive experimental design was employed, analyzing the effects of three wood types (Doncel, Tamburo, and Zapote), two thickness levels, and three drying speeds on MC_Out and waviness. Data collected were subjected to both traditional statistical analysis and ANNs. The ANNs were fine-tuned through genetic algorithms, exploring different network architectures to achieve optimal predictive performance.

Results: Statistical models revealed the significant influence of wood type, thickness, and drying speed on MC_Out and waviness, explaining 95.9% and 84.3% of the variations, respectively. The optimized ANN models, however, demonstrated superior accuracy, with the MC_Out model achieving fitted R-squared values of 0.940 and 0.757 for training and validation sets, respectively, thus outperforming traditional models in predicting drying outcomes.

Discussion: The study underscores the effectiveness of ANNs in capturing complex nonlinear relationships within the plywood drying data, which traditional statistical models might not fully elucidate. The successful optimization of ANN architecture via genetic algorithms further highlights the potential of machine learning approaches in industrial applications, offering a more precise and reliable method for predicting drying process outcomes.

Conclusion: The integration of artificial neural networks, optimized through genetic algorithms, represents a significant advancement in the predictive modeling of plywood drying processes. This approach not only offers enhanced prediction accuracy for key variables such as MC_Out and waviness but also paves the way for more efficient and controlled drying operations, ultimately contributing to the production of higher-quality plywood.

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