Yazar "Goz, E." seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    A software for parameter estimation in thin-layer drying models
    (Plapiqui(Uns-Conicet), 2024) Yuceer, M.; Goz, E.; Tosun, E.
    Drying is a highly significant process because it reduces water content, prolongs shelf life, and facilitates the transportation of fruits and vegetables. However, the absence of a mathematical evaluation of drying kinetics can lead to various issues, ranging from increased production costs to diminished product quality. Estimating parameters in thin-layer drying kinetic models through experimental data and identifying the model or models that best fit the experimental data typically involves time-consuming and experience-demanding processes. There is a pressing need for straightforward and user-friendly optimization software capable of calibrating these models. To address this need, a user-interactive graphical user interface (PEDKiM) was developed to estimate the kinetic parameters of nine thin-layer drying models. This study represents an innovative approach to the thin-layer drying process of fruits and vegetables.
  • Küçük Resim Yok
    Öğe
    Enhanced prediction of ozone concentrations using an artificial neural network model
    (Plapiqui(Uns-Conicet), 2025) Uguz, G.; Karadurmus, E.; Kaya, S.; Goz, E.; Akyazi, H.; Yuceer, M.
    The primary goal of this research was to develop an Artificial Neural Network (ANN) model to predict ozone (O3) concentrations using hourly data obtained from a monitoring station in Samsun City, located in the Middle Black Sea Region of Turkey. The dataset utilized encompassed the years from 2016 to 2020. The ANN architecture incorporated eleven input nodes representing various parameters: month, hour, concentrations of particulate matter (PM2.5 and PM10), nitrogen oxides (NOx), wind direction, relative humidity, air temperature, wind speed, cabin temperature of the measuring station, and air pressure. The focus of the model's output was on predicting the O3 concentration. During the training and testing phases, the ANN model displayed outstanding performance, as evidenced by correlation coefficients nearing one. The model also registered minimal values for Mean Absolute Percentage Error (MAPE, %), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In the training phase, the model achieved a Training R-value of 0.9993, an RMSE of 0.7424, a M APE of 4.3221 %, and a MAE of 0.5301. The testing phase showed equally strong results, with a Test R-value of 0.9990, an RMSE of 0.8595, a M APE of 4.5642 %, and an MAE of 0.5823. These outcomes emphasize the model's ability to accurately predict ozone concentrations, markedly enhancing the precision compared to previous models based on traditional statistical methods. The findings of this study highlight the potential of this ANN model in providing precise ozone concentration readings in the atmosphere. The proposed ANN model distinguishes itself from previous studies by incorporating more representative variables as inputs, significantly boosting prediction accuracy. Additionally, the removal of outliers during preprocessing enhances data quality, thereby increasing the reliability of the predictions. Despite its simple structure, the model demonstrates high performance, making it both innovative and effective in comparison to earlier models. Moreover, the model's superior performance may reduce the need for additional measurement devices at newly established monitoring stations.
  • Küçük Resim Yok
    Öğe
    OPTIMIZATION OF HYDRODISTILLATION OF ESSENTIAL OIL FROM MENTHA SPICATA L. BY USING RESPONSE SURFACE METHODOLOGY
    (Plapiqui(Uns-Conicet), 2023) Karakaplan, N.; Goz, E.; Tosun, E.; Yuceer, M.
    This study was conducted to optimize the hydrodistillation process of Mentha spicata L. essential oil using the response surface methodology. The optimal values of operating parameters (independent variables) such as extraction time (100-240 min) and water volume to plant mass ratio (0.055-0.120) were investigated using a central composite design, which led to only 13 experiments. The response variables were selected based on the highest essential oil yield as well as the carvone ratio, which is the main component of the essential oil. The experimental data were fitted to a linear model for essential oil yield and a modified quadratic model for the carvone ratio. The hydrodistillation time and water volume to plant mass ratio have significant effects (p<0.05) on both the essential oil yield and the carvone ratio. The optimal conditions were identified as 145.7 min of extraction time and a 0.105 ml/g water volume to plant mass ratio by the 3D response surface and the contour plots derived from the models. At these predicted conditions, the essential oil yield and carvone ratio were calculated to be 1.383% and 28.541%, respectively. The findings indicate that the response surface approach can be used successfully in the hydrodistillation of Mentha spicata L.