Yazar "Taki, Morteza" seçeneğine göre listele

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    Öğe
    Application of machine learning for solar radiation modeling
    (Springer Wien, 2021) Taki, Morteza; Rohani, Abbas; Yildizhan, Hasan
    Solar radiation is an important parameter that affects the atmosphere-earth thermal balance and many water and soil processes such as evapotranspiration and plant growth. The modeling of the daily and monthly solar radiation by Gaussian process regression (GPR) with K-fold cross-validation model has been discussed recently. This study evaluated different neural models such as artificial neural network (ANN), support vector machine (SVM), adaptive network-based fuzzy inference system (ANFIS), and multiple linear regression (MLR) for estimating the global solar radiation (daily and monthly) with K-fold cross-validation method. For the appropriate comparison of the models, the randomized complete block (RCB) design applied in the training and test phases. Also, different data sets were evaluated by K-fold cross-validation in each model. The results showed that radial basis function (RBF) model has the lowest error for estimating the monthly and daily solar radiation. In this study, the result of RBF was compared with the GPR models. The conclusion indicated that RBF methodology can predict solar radiation with higher accuracy relative to the GPR model. The results of yearly solar radiation estimation (2009-2014) showed that the RBF model can estimate solar radiation with the MAPE and RMSE of 5.1% and 0.29, respectively. Also, the coefficient of correlation (R-2) between actual and estimated values throughout the year is 98% and can be used by the engineers and other researchers for solar and thermal applications.
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    Öğe
    Renewable energy utilization in apple production process: A thermodynamic approach
    (Elsevier, 2021) Yildizhan, Hasan; Taki, Morteza; Ozilgen, Mustafa; Gorjian, Shiva
    Horticultural inputs have various potential environmental impacts which can be simultaneously evaluated by input-output energy methods. This technique is considered as an appropriate evaluation method to analyze ecosystems through recognizing, quantifying, and appraising resources depleted and released within the environment. This study aims to apply the thermodynamic approach to maximize the decision-making information on the environmental impacts of energy consumption in the apple production process. In this case, two different scenarios of energy and exergy flow during the apple production process are assessed thermodynamically and the environmental effects of these scenarios are evaluated. The first scenario is based on conventional agriculture carried out by using almost no renewable energy source, while in the second scenario, renewable energy sources including hydroelectricity, biodiesel, and microbial fertilizers are employed. The results indicated that in the second scenario, the Cumulative Exergy Consumption (CECx) and exergy loss are decreased by more than 93% and 74%, respectively (584.5 and 3023 MJ.ton(-1)), while the Cumulative Degree of Perfection (CDP) and Renewability Index (RI) are increased by 6.01 and 0.83, respectively. Additionally, in this scenario, the Cumulative Carbon Dioxide emission (CCO2) was decreased to 11.23 kg.ton(-1). The results also indicated that the application of microbial fertilizers along with improving the irrigation system can decrease the total energy-exergy loss and total input costs of the apple production process. The results of the present study pointed a direction for the invention and development of new technologies or methodologies in agricultural productions to improve the energy-exergy flow and mitigate CO(2 )emission in the future.