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Fecha
2026-01-12
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Elsevier
Citas
Resumen
This study investigates the exergy-based performance and sustainability of a semi-industrial convective dryer equipped with a waste heat recovery unit for drying poplar wood chips. Four key exergy-related indicators, namely exergetic improvement potential (EIP), exergetic sustainability index (ESI), universal exergetic efficiency (UEE), and overall exergetic efficiency (OEE) were examined and then predicted using a feedforward backpropagation multilayer perceptron neural network (FFBP-MLPNN) with the Levenberg-Marquardt (LM) learning algorithm and a single hidden layer. The network evaluated different numbers of neurons in the hidden layer and utilized tansig and purelin activation functions in the hidden and output layers, respectively. Experimental trials demonstrated that increasing the air recirculation ratio enhances the ESI due to improved heat recovery and reduced exergy losses, while it reduces the EIP, indicating lower thermodynamic inefficiencies and less potential for further improvement. In contrast, lower recirculation ratios yielded lower ESI values and higher EIP, highlighting greater exergy destruction and larger optimization potential. Additionally, increased air temperatures and flow rates improved both indices. The results indicated that the neural network can predict all four outcomes with R2 > 0.97. Additionally, 0.013601 (using a 4–23–1 topology), 6.4137 × 10−6 (4–15–1 topology), 3.186 × 10−6 (4–30–1 topology), and 0.036108 (4–32–1 topology) were the mean squared error (MSE) values for predicting the EIP, ESI, UEE, and OEE, respectively. Hence, this study suggests that the ANNs approach could be an effective tool for analyzing thermal sustainability indicators in industrial convective drying processes.
Descripción
The registered version of this article, first published in Thermal Science and Engineering Progress, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.tsep.2026.104501
La versión registrada de este artículo, publicado por primera vez en Thermal Science and Engineering Progress, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.tsep.2026.104501
La versión registrada de este artículo, publicado por primera vez en Thermal Science and Engineering Progress, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.tsep.2026.104501
Categorías UNESCO
Palabras clave
Artificial neural network, Convective drying, Exergetic efficiency, Exergy analysis, Waste heat recovery, Wood chips
Citación
Saman Zohrabi, Seyed Sadegh Seiiedlou, Iman Golpour, Jochen Mellmann, Barbara Sturm, José Daniel Marcos, Ana M. Blanco-Marigorta, Fardad Didaran, and Mark Lefsrud. "Exergetic sustainability assessment of a semi-industrial convective dryer employing waste heat recovery for drying wood chips: A BPANN-based approach." Thermal Science and Engineering Progress. 70. 2026.104501.
Centro
E.T.S. de Ingenieros Industriales
Departamento
Ingeniería Energética