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Isı yalıtımı konusu global ölçüde önem arz eden ve üzerinde sürekli çalışılan bir alanı oluşturmaktadır. Bu bağlamda, Ağrı ili için dört farklı duvar modeli (taş, tuğla, briket ve betonarme) ve beş farklı yakıt (kömür, doğalgaz, Likit Propan Gaz (LPG), fuel oil ve elektrik) çeşidi seçilerek optimum yalıtım kalınlığı, yalıtım maliyeti, yakıt ve toplam maliyetler, geri ödeme süresi ve yıllık enerji kazancı değerleri hesaplanmıştır. Çalışmada yalıtım malzemesi olarak Genleştirilmiş Polistiren (EPS) kullanılmıştır. En yüksek optimum yalıtım kalınlığı, yakıt maliyeti ve toplam maliyet değerleri elektrik enerjisinin kullanıldığı durumda betonarme duvar modeli için belirlenmiştir. Betonarme duvar modelinde elektrik enerjisi için optimum yalıtım kalınlığı 0.168 m, yakıt maliyeti 3,2392 $/m2 ve toplam maliyet 23,9826 $/m2 olarak tespit edilmiştir. Betonarme duvar modeli için optimum yalıtım kalınlığı, yakıt maliyeti ve toplam maliyet diğer duvar türlerine göre daha yüksek değerler gösterse de geri ödeme süresi bakımından en verimli duvar modeli olduğu belirlenmiştir. Kullanılan yakıt çeşitleri arasında geri ödeme süresi bakımından en verimli olanının elektrik, kömür ve doğal gazın kendilerini en geç amorti edebilen iki yakıt çeşidi olduğu belirlenmiştir.

Solar energy systems have significant advantages over traditional energy production methods, but improvements are needed to improve performance and efficiency. In this study, the effect of the use of nanofluids on power and efficiency values in a heat pipe solar collector was analyzed using experimental and artificial intelligence approaches. A heat pipe solar collector was fabricated and the effects of prepared water-based Al₂O₃ and TiO₂ nanofluids on power and efficiency values were experimentally investigated. Using the obtained experimental data, an artificial neural network model has been developed to predict power and efficiency values. The values obtained from the network model were compared with the experimental data and the prediction performance of the network model was extensively examined using various performance parameters. The coefficient of performance value for the neural network model was calculated as 0.99332 and the mean squared error value was calculated as 2.77E-03. The study findings revealed that the use of nanofluids in the heat pipe solar collector improves the power and efficiency values. It has also been seen as a result of the study that the developed artificial neural network model can predict power and efficiency values with deviation rates lower than 0.48%.

In this study, it was tried to determine the biogas and electricity production amounts of Turkey's animal manure (bovine, ovine and poultry) by using the 2021 data of the Turkish Statistical Institute (TSI) and it was determined how much of the electrical energy our country could meet for 2021. Biogas and electrical energy production amounts were calculated based on the total number of animals in Turkey. In this context, our country's biogas production from animal waste for 2021 has been determined as 15.894×106 m3/year and electrical energy production as 28.609×106 kWh/year. As a result of the calculations, it has been determined that the first three regions with the highest annual biogas amounts are the Aegean Region (3.889×106 m3/year), the Central Anatolia Region (2.701×106 m3/year) and the Mediterranean Region (2.533×106 m3/year). It has been determined that the first three regions with the highest annual electricity production amounts are Aegean Region (7.000×106 kWh/year), Central Anatolia Region (4.862×106 kWh/year) and Mediterranean Region (4.559×106 kWh/year). Considering that the total electricity consumption of our country in 2021 is 329.634×106 kWh, it has been determined that 8.67% of the annual energy need can be met by using biogas energy if there are biogas facilities in each province.