Biyokütle kaynaklarının proximate bileşimleri ile üst ısı değerleri arasında yeni bir denklem geliştirilmesi ve denklemin başarısının araştırılması

Bu çalışmada, biyokütle kaynaklarının üst ısı değerlerinin (ÜID) tahmini için proximate analizlerinedayanan yeni bir denklem geliştirilmiştir. Bu amaçla, termal dönüşüm süreçlerinde hammadde olarakkullanılabilecek biyokütle kaynaklarını temsilen on farklı biyokütle örneği seçilmiştir. Bu örneklerinkalori tayinler ve proximate analizleri yapılmış ve bir denklem geliştirilmiştir. Geliştirilen denklemkullanılarak biyokütle kaynaklarının hesaplanan ÜID değerleri ve deneysel ÜID değerlerikarşılaştırılmıştır. Ayrıca mutlak sapma ve yüzde hata değerleri hesaplanmıştır. Daha sonra, biyokütlekaynaklarının proximate analiz verileri ile kalorifik değerleri arasında bir ilişki kurulmaya çalışılmıştır.MATLAB 2019A programı ile elde edilen denkleminin başarısı, literatürde bulunan 17 farklıdenklemlerle kıyaslanmıştır. Geliştirilen denklemin başarısının diğer eşitliklerden yüksek olduğu,regresyon katsayısının 1 ve mutlak sapma değerlerinin sıfır olduğu belirlenmiştir. Ayrıca, geliştirilendenklemin literatürdeki farklı biyokütle örneklerinin kalorifik değerlerinin tahminindeki başarısı daaraştırılmıştır. Bu aşamada da 18 farklı biyokütle kaynağı kullanılmıştır ve denklemin mutlak sapma,yüzde hata ve regresyon katsayıları tespit edilmiştir. Bu değerler sırasıyla 0.91 kJ/g(max.), % 4.3 (max.)ve 0.91 olarak belirlenmiştir. Geliştirilen denkleminin diğer biyokütle kaynakları içinde oldukça başarılısonuçlar verdiği sonucuna varılmıştır.

Developing a new equation between proximate contents and upper heat values of biomass sources and investigating the success of the equation

In this study, a new equation was developed based on proximate analysis for estimation of higher heat values (HHV) of biomass sources. For this purpose, ten different biomass samples that can represent biomass resources that can be used as raw materials in thermal conversion processes have been selected and characterized. Then, a relationship was tried to be established between proximate analysis data of biomass sources and calorific values. For this purpose, data analysis was done with MATLAB 2019A program and an equation was developed. HHV values and experimental HHV values of biomass sources calculated using the equation were compared. Also, the absolute deviation and percent error values were calculated. In addition, the success of the equation was compared with 17 different model equations in the literature. It has been determined that the success of the developed equation is higher than the other equations, the regression coefficient is 1 and the absolute deviation values are zero. In addition, the success of the developed equation in the estimation of calorific values of different biomass samples in the literature was investigated. At this stage, 18 different biomass sources were used and absolute deviation, percent error and regression coefficients of the equation were determined. These values are determined as 0.91 kJ/g (max.), 4.3% (max.) and 0.91, respectively. It is concluded that the developed equation gives very successful results as also for other biomass sources.

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