Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Karakaya, Hakan(2)
• Durmus, Aydin(1)
• Durmuş, Aydın(1)
• Gulcimen, Fevzi(1)
• Gülçimen, Fevzi(1)

• MakaleAçık Erişim2026Scopus SJRQ3/r/munzur/713
  An Artificial Neural Network Modeling of Solar Drying of Mint: Energy, Exergy, and Drying Kinetics

  Gülçimen, Fevzi; Karakaya, Hakan; Durmuş, Aydın

  Abstract: Solar drying is an environmentally friendly method for preserving agricultural products; however, limited studies have jointly examined energy–exergy performance and data-driven prediction methods under varying airflow conditions, which restricts performance optimization. This study aims to investigate the effects of inlet airflow rate on energy utilization ratio, energy generation, exergy loss, exergy efficiency, and drying kinetics of mint leaves, and to evaluate the predictive capability of artificial neural network modeling. Thin-layer drying experiments were conducted in a forced convective solar dryer equipped with a newly designed solar air collector at different air mass flow rates. Energy and exergy analyses were conducted using experimental thermodynamic data, and energy utilization and exergy loss were modeled with a feed-forward back-propagation artificial neural network. The energy utilization ratio ranged from 7.45 to 87.1% and increased with decreasing airflow rate. The average exergy losses for air mass flow rates of 0.012, 0.026, and 0.033 kg s–1 were 16.2, 8.2, and 6.88 W, respectively. Both experimental and artificial neural network results showed that increasing airflow rate reduced energy utilization ratio and exergy loss. The artificial neural network predictions exhibited very close agreement with experimental data. The results show that artificial neural networks can accurately predict energy utilization ratio and exergy loss in a forced convective solar dryer. Airflow rate is a key parameter for thermodynamic performance, and the integrated experimental–ANN approach offers a robust tool for optimizing and designing solar drying systems.

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• MakaleYalnız künye2016Scopus SJRQ1/r/munzur/3952
  Drying of sweet basil with solar air collectors

  Gulcimen, Fevzi; Karakaya, Hakan; Durmus, Aydin

  In this study, sweet basil was dried and its drying parameters were investigated experimentally and theoretically by using newly developed solar air collectors. Proper temperatures were chosen to dry sweet basil and experiments were carried out at different flow rates. At the end of drying experiments, it was determined that total mass of sweet basil decreased from 0.250 kg to 0.029 kg. In drying sweet basil, dimensionless moisture ratios were decreased rapidly to 300 min for 0.012 kg/s, 360 min for 0.026 kg/s, and 450 min for 0.033 kg/s. It was observed that the efficiency of collector was increased at the same rate with air flow changed between 29 and 63%. Among the models in the literature, Page Model was found to suit best for drying sweet basil. Furthermore, a novel mathematical model rendering more valid results for sweet basil and leafy products was developed. (C) 2016 Elsevier Ltd. All rights reserved.

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  AtıfWoS64·OpenAlex80

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