Knowing the properties of coal, which is still the most widely used among primary energy sources, is critical for determining the application area and the technology to be applied. The ultimate analysis results contain important information for the estimation of the gas product composition to be released to the environment as a result of the combustion process. This study aims to make a comparative study using different machine learning models, namely, Support Vector Regression (SVR), Regression Trees (RT), the Ensemble of Trees (ET), Gaussian Process Regression (GPR) for predicting the elemental composition of coal. The moisture, ash, volatile matter, and fixed carbon contents were used as input variables to predict carbon, hydrogen, and oxygen contents of coal. 70% of 6339 coal samples (4437 data) containing different quality coal types were for the training of the models, whereas the remaining 30% sets of data (1902 data) were used to evaluate the prediction performance of the developed models. Analysis results proved that the GPR-exponential model performs best among all models. In the training stage, the correlation of determination (R²), mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute deviation (MAD) values were obtained as 0.9970, 0.7525%, 0.6492, 0.4635 for Carbon content; 0.9900, 0.5712%, 0.0646, 0.0297 for Hydrogen content; 0.9951, 4.0664%, 0.7910, 0.4152 for Oxygen content, respectively. Reported indices showed that the GPR-exponential model is a promising procedure for achieving high accuracy and can be used as a reliable model to predict the coal elemental components successfully.

In this study, the effect of heat sink geometries on the performance of a thermoelectric generator was researched. In this context, fins with different geometries (flat fin, square pin fin, hexagonal pin fin, and circle pin fin) placed on an aluminum heat sink, and their effects on heat transfer and electricity generation were investigated numerically. Calculations were carried out according to three different hot surface temperatures and five different air velocities. As a result of this study, it was determined that the electrical output increased with the increase in air velocity and hot surface temperature in all geometries. The highest electrical outputs were obtained at 150 °C hot surface temperatures and 15,000 Reynolds numbers, while the lowest electrical outputs were obtained at 50 °C hot surface temperatures and 5000 Reynolds numbers in all geometries. The best thermal and electrical performance was obtained with a flat fin heat sink, and the worst performance was seen in the thermoelectric generator (TEG) system without a heat sink. The highest power outputs of the flat fin heat sink and without heat sink TEG systems were 6.42 W and 1.12 W, respectively. In addition, the highest conversion efficiencies of the flat fin heat sink and without heat sink TEG systems were 14.94% and 5.54%, respectively.

Günümüzde artan enerji talebinin büyük bir kısmı halen yüksek miktarda hidrokarbon içeren fosil yakıt kaynaklarından sağlanmaktadır. Bu durum hava kirliliği ve küresel ısınma gibi birçok çevresel probleme yol açmaktadır. Bu sorunlar karşısında tüm dünya yenilenebilir enerji kaynaklarına yönelik araştırma ve geliştirme çalışmalarına ağırlık vermiştir. Bu çalışmada, dünyada ve Türkiye’de yenilenebilir enerji kaynaklarının kullanım durumları ve kurulum maliyetleri güncel verilerle incelenmiştir. Çalışma neticesinde yenilenemez enerji kaynağı olan kömürün küresel çapta %36’lık payla günümüzde hala en çok tüketilen enerji kaynağı olduğu tespit edilmiştir. Buna karşın toplam temiz enerjinin elektrik üretimindeki payının %38 seviyelerine geldiği belirlenmiştir. Bu bağlamda yenilenebilir enerji kullanımının dünyada ve Türkiye’de hızlı bir şekilde yaygınlaşması, yenilenemez enerjiye olan talebin hızla azaltılması hem çevresel hem de kaynak çeşitliliği açısından oldukça önem arz etmektedir.