Open Access

ARTICLE

Optimization of Dimensional Factors Using AI Technique Affecting Solar Dryer Efficiency for Drying Agricultural Materials

Ravendra Kumar Ray^*, A.C. Tiwari

Department of Mechanical Engineering, University Institute of Technology (UIT-RGPV), Bhopal, 462033, India

* Corresponding Author: Ravendra Kumar Ray. Email:

(This article belongs to the Special Issue: Innovative Approaches to the Materials Genome: Machine Learning, Big Data, and Computational Methods for Modern Material Design and Manufacturing)

Computers, Materials & Continua 2025, 83(1), 845-860. https://doi.org/10.32604/cmc.2025.059435

Received 08 October 2024; Accepted 03 January 2025; Issue published 26 March 2025

Abstract

The design and development of solar dryers are crucial in regions with abundant solar energy, such as Bhopal, India, where seasonal variations significantly impact the efficiency of drying processes. The paper is focused on employing a comprehensive mathematical model to predict the dryer’s performance in drying the materials such as banana slices. To enhance this model, Hyper Tuned Swarm Optimization with Gradient Tree (HT_SOGT) was utilized to accurately predict and determine the optimal size of the dryer dimensions considering various mathematical calculations for material drying. The predictive model considered the influence of seasonal fluctuations, ensuring an efficient drying process with an objective function to optimize the drying time of an average of 7 hrs throughout the year. Across all recorded ambient temperatures (ranging from 16.985°C to 31.4°C), the outlet temperature of the solar dryer is consistently higher, ranging from 39.085°C to 66.2°C. The results show that the optimized dryer design, based on HT_SOGT modelling, significantly improves drying efficiency of the materials across varying conditions, making it suitable for sustainable applications in agriculture and food processing industries in the Bhopal region.

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