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Abstract <p>Solar drying is a sustainable option for preserving horticultural products, but cover material strongly affects thermal behavior and drying efficiency. This work experimentally compares two identical natural-convection parabolic greenhouse solar dryers operated side-by-side in Ghardaïa (Algeria): one covered with polyethylene (PE) and the other with polycarbonate (PC). Drying kinetics were analyzed using Fick’s second law and empirical thin-layer models, effective moisture diffusivity was estimated, and internal temperatures were predicted using machine-learning models (feedforward ANN, NAR, and NARX) driven by ambient inputs. The PC-covered dryer exhibited higher internal air and absorber temperatures (by 15.5 and 18.8%, respectively) and shortened mint drying time by 68% relative to PE. The Midilli–Kucuk model best captured the drying curves, and the NARX architecture yielded the most accurate temperature forecasts (<i>R</i>&#xa0;≈ 0.9998; MSE &lt; 0.11). Overall, polycarbonate cladding materially enhances thermal retention and drying performance compared with polyethylene, while ANN-based prediction—especially NARX—provides reliable thermal behavior estimates that can support control and optimization of solar dryers in arid climates.</p>

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Impact of Cover Material on Greenhouse Solar Dryer Efficiency: A Case Study on Mint Leaves

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • Ch. Nettari,
  • A. Benseddik,
  • A. Boubekri,
  • H. Bensaha,
  • A. Badji,
  • Y. Benaioua,
  • I. Hasrane,
  • M. A. Kherrafi

摘要

Abstract

Solar drying is a sustainable option for preserving horticultural products, but cover material strongly affects thermal behavior and drying efficiency. This work experimentally compares two identical natural-convection parabolic greenhouse solar dryers operated side-by-side in Ghardaïa (Algeria): one covered with polyethylene (PE) and the other with polycarbonate (PC). Drying kinetics were analyzed using Fick’s second law and empirical thin-layer models, effective moisture diffusivity was estimated, and internal temperatures were predicted using machine-learning models (feedforward ANN, NAR, and NARX) driven by ambient inputs. The PC-covered dryer exhibited higher internal air and absorber temperatures (by 15.5 and 18.8%, respectively) and shortened mint drying time by 68% relative to PE. The Midilli–Kucuk model best captured the drying curves, and the NARX architecture yielded the most accurate temperature forecasts (R ≈ 0.9998; MSE < 0.11). Overall, polycarbonate cladding materially enhances thermal retention and drying performance compared with polyethylene, while ANN-based prediction—especially NARX—provides reliable thermal behavior estimates that can support control and optimization of solar dryers in arid climates.