Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ3An 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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İncele - MakaleYalnız künye2020Scopus SJRQ2Empirical modeling between degree days and optimum insulation thickness for external wall
Kallioğlu, Mehmet Ali; Ercan, Umut; Avcı, Ali Serkan; Fidan, Cihad; Karakaya, Hakan
Insulating is the most effective method that is used to save energy in buildings. Samples from cities from different climatic zones from TS 825 (Turkey) first. Optimum insulation thickness (xopt) analysis is based on two types of insulating and four different fuels (natural gas, coal, fuel oil and electric) of related cities. Cost accounts, payback period and CO2-SO2 emission calculations were performed based on these analyses. Second of all, the relationship between a number of degree day (NDD) and optimum insulation thickness (xopt) was developed by linear, quadratic and cubic correlations. Thirty different mathematical correlations based on different fuel types by using XPS and EPS insulating materials. Twenty-four of these models that were developed were generated peculiar to the fuel type; six of them were generated based on average insulation thickness. R2 values of related correlations are between 0.9989 most and 0.9952 at least as well as it is pretty close to (R ≤ 1) one value. The model among these models is the xopt xopt = a+b (NDD) + c(NDD)2+ d(NDD)3 cubic mathematical model that gives the best average value. a = 0.0036, b = 5E-05, c =–7E-09 and d = 6E-13 are the values for XPS material. Following values are for EPS material; a = 0.0028, b = 5E-13, c =–7E-09 and d = 4E-05. R2 determination coefficient of both two equations is pretty close to 0.9989 and 1; the models obtained are less-than-stellar. Optimum insulation thickness of the area can be known based on the type of material via the number of degree day without the need for long analyses. According to the R2 values, the use of all models is recommended for academic and industrial users.
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İncele - MakaleYalnız künye2016Scopus SJRQ1Drying 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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