A HYBRID GP-GWO FRAMEWORK FOR ENHANCED PERFORMANCE OF ROTARY DRYING SYSTEMS

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259142" target="_blank" >RIV/61989100:27230/25:10259142 - isvavai.cz</a>
Result on the web
<a href="https://www.mmscience.eu/journal/issues/march-2025/articles" target="_blank" >https://www.mmscience.eu/journal/issues/march-2025/articles</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.17973/MMSJ.2025_03_2025004" target="_blank" >10.17973/MMSJ.2025_03_2025004</a>

Result language
angličtina
Original language name
A HYBRID GP-GWO FRAMEWORK FOR ENHANCED PERFORMANCE OF ROTARY DRYING SYSTEMS
Original language description
This study presents a hybrid framework combining Genetic Programming (GP) and Grey Wolf Optimizer (GWO) to enhance the performance of rotary drying systems. The methodology employs Box Behnken Design (BBD) to investigate the effects of critical process parameters—drying temperature, time, and airflow rate—on moisture ratio (MR). GP is utilized to develop predictive models that capture nonlinear interactions among variables, whereas GWO optimizes the parameters to achieve the desired MR. The proposed GP-GWO framework demonstrates superior predictive accuracy and optimization efficiency compared to traditional methods. It achieves a 1.5% improvement in moisture ratio (MR) optimization over El-Mesery et al.'s model. Experimental validation highlights the framework's ability to minimize moisture ratio while maximizing energy efficiency. © 2025, MM publishing Ltd.. All rights reserved.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20301 - Mechanical engineering

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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