Abstract
Katı bir yakıtın ısıl değeri, birim ağırlıktaki yakıtın tamamen yanması sonucu açığa çıkan ısı biriminin sayısıdır. Bir kömürün ısıl değeri, türüne ve organik yapısına karışmış olan yanmayan maddelerin miktarına bağlıdır. Isıl değer, kömür türü yanında, kömür kül ve nem oranı tarafından da belirlenmektedir. Kömürün üst ısıl değeri kalorimetre ile, kömürün bir kalorimetre bombası içinde, basınç altında oksijen ile sabit hacimde yakılması ve oluşan ısının ölçülmesi esasına dayanmaktadır. Literatürde, kısa ve elementel analizlere dayanarak, üst ısıl değer hesaplaması yapabilmek için çeşitli denklemler geliştirilmiştir. Bu çalışmada 10 farklı linyit örneğinin ısıl değeri, hem deneysel olarak belirlenmiş hem de analiz verileri yardımıyla farklı denklemler kullanılarak hesaplanmıştır. Her bir kömür için, deneysel ve hesapla elde edilen üst ısıl değerler karşılaştırılmıştır. En iyi regresyon katsayısı değerleri (R2), kısa analiz ve elementel analiz modelleri için sırasıyla 0.7543 ve 0.5927 olarak belirlenmiştir. Modellerden elde edilen üst ısıl değerlerin, deneysel olarak hesaplananlarla uyum içinde olmadığı görülmüştür.
Keywords
References
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- Nzihou, J.F., Hamidou, S., Bouda, M., Koulidiati, J., Segda, B.G., 2014, “Using Dulong and Vandralek Formulas to Estimate the Calorific Heating Value of a Household Waste Model”, International Journal of Scientific & Engineering Research, Cilt 5, Sayı 1, ss. 1878-1883.
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Calculation of Higher Heating Values of Different Lignites Based on Proximate and Ultimate Analysis Data
Abstract
The calorific value of solid fuel is the number of units of heat released as a result of the complete burning of the unit weight fuel. The calorific value of coal depends on its type and the amount of non-combustible substances mixed into its organic structure. The calorific value is determined not only by the type of coal but also by the coal ash and humidity. The higher heating value of coal is based on the principle of burning the coal in a calorimeter bomb under pressure with a constant volume of oxygen and measuring the heat generated by the calorimeter. In the literature, based on short and elemental analyzes, various equations have been developed to calculate the higher heating value. In this study, the calorific value of 10 different lignite samples was determined both experimentally and calculated using different equations with the help of analysis data. For each coal, the higher heating values obtained by experimental and calculation were compared. The best regression coefficient results (R2) were determined as 0.7543 and 0.5927 for the models based on the proximate and ultimate analyses, respectively. It was seen that the higher heating values obtained from the models were not in agreement with the experimentally calculated values.
References
- Ahmaruzzaman, M., 2008, “Proximate analyses and predicting HHV of chars obtained from cocracking of petroleum vacuum residue with coal, plastics and biomass”, Bioresource Technology, Cilt 99, ss. 5043-5050.
- Akkaya, E., 2016, “ANFIS based prediction model for biomass heating value using proximate analysis components”, Fuel, Cilt 180, ss. 687-693.
- Boylu, F., Karaağaçlıoğlu, İ.E., 2018, “Kömür Bileşenlerinin Kalorifik Değer Üzerindeki Etkisi Üzerine Değerlendirme”, Yerbilimleri, Cilt 39, Sayı 3, ss. 221-236.
- Callejón-Ferre, A.J., Velázquez-Martí, B., López-Martínez, J.A., Manzano-Agugliaro, F., 2011, “Greenhouse crop residues: Energy potential and models for the prediction of their higher heating value”, Renewable and Sustainable Energy Reviews, Cilt 15, ss. 948-955.
- Channiwala, S.A., Parikh, P.P., 2002, “A unified correlation for estimating HHV of solid, liquid and gaseous fuels”, Fuel, Cilt 81, ss. 1051-1063.
- Erol, M., Haykiri-Acma, H., Küçükbayrak, S., 2010, “Calorific value estimation of biomass from their proximate analyses data”, Renewable Energy, Cilt 35, ss. 170-173.
- Feng, Q., Zhang, J., Zhang, X., Wen, S., 2015, “Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network”, Fuel Processing Technology, Cilt 129, ss. 120-129.
- García, R., Pizarro, C., Lavín, A.G., Bueno, J.L., 2014a, “Spanish biofuels heating value estimation. Part I: Ultimate analysis data”, Fuel, Cilt 117, ss. 1130-1138.
- García, R., Pizarro, C., Lavín, A.G., Bueno, J.L., 2014b, “Spanish biofuels heating value estimation. Part II: Proximate analysis data”, Fuel, Cilt 117, ss. 1139-1147.
- Kathiravale, S., Yunus, M.N.M., Sopian, K., Samsuddin, A.H., Rahman, R.A., 2003, “Modeling the heating value of Municipal Solid Waste”, Fuel, Cilt 82, ss. 1119-1125.
- Kemal, M., Arslan, V., 2010, Kömür Teknolojisi, Dokuz Eylül Üniversitesi Mühendislik Fakültesi Yayınları, İzmir, 394. Kural, O., 1998, Kömür Özellikleri, Teknolojisi ve Çevre İlişkileri, Özgün Ofset Matbaacılık A.Ş., İstanbul, 785.
- Leonard, J.W., Hardinge, B.C., 1991, Coal Preparation (5th Edition), Society for Mining, Metallurgy and Exploration Inc., Colorado, 1131.
- Majumder, A.K., Jain, R., Banerjee, P., Barnwal, J.P., 2008, “Development of a new proximate analysis based correlation to predict calorific value of coal”, Fuel, Cilt 87, ss. 3077-3081.
- Nzihou, J.F., Hamidou, S., Bouda, M., Koulidiati, J., Segda, B.G., 2014, “Using Dulong and Vandralek Formulas to Estimate the Calorific Heating Value of a Household Waste Model”, International Journal of Scientific & Engineering Research, Cilt 5, Sayı 1, ss. 1878-1883.
- Parikh, J., Channiwala, S.A., Ghosal, G.K., 2005, “A correlation for calculating HHV from proximate analysis of solid fuels”, Fuel, Cilt 84, ss. 487-494.
- Setyawati, W., Damanhuri, E., Lestari, P., Dewi, K., 2015, “Correlation equation to predict HHV of tropical peat based on its ultimate analyses”, Procedia Engineering, Cilt 125, ss. 298-303.
- Speight, J.G., 2005, Handbook of Coal Analysis, Wiley-Interscience, USA, 222.
- Thipkhunthod, P., Meeyoo, V., Rangsunvigit, P., Kitiyanan, B., Siemanond, K., Rirksomboon, T., 2005, “Predicting the heating value of sewage sludges in Thailand from proximate and ultimate analyses”, Fuel, Cilt 84, ss. 849-857.
- Wen, X., Jian, S., Wang, J., 2017, “Prediction models of calorific value of coal based on wavelet neural Networks”, Fuel, Cilt 199, ss. 512-522.
- Yin, C-Y., 2011, “Prediction of higher heating values of biomass from proximate and ultimate analyses”, Fuel, Cilt 90, ss. 1128-1132.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Article |
| Authors | |
| Publication Date | March 1, 2022 |
| Submission Date | January 27, 2021 |
| Acceptance Date | December 29, 2021 |
| Published in Issue | Year 2022 Volume: 10 Issue: 1 |
