Abstract
Bu çalışmada, bir CCD (Charge Couple Device) kamera ile donatılmış evsel kömür yakma sisteminde alev görüntüsünden hava fazlalık katsayısının tahmin doğruluğunu artıran bir yöntem önerilmiştir. Önerilen yöntem, kameradan elde edilen sayısal alev bilgisi ve baca gazı sıcaklığının hava fazlalık katsayısı ile ilişkisini ortaya koyan çoklu lineer regresyon bağıntısına dayanmaktadır. Bu bağıntı ile oluşturulan mimarinin basit yapısı pratik uygulamalar bakımından önemli bir avantajıdır. Deneysel veriler üzerinden yapılan doğruluk çalışması önerilen sistemin geleneksel sisteme göre doğruluğu kayda değer biçimde artırdığını göstermektedir.
Keywords
Ethical Statement
Bu çalışmada araştırma ve yayın etiğine uyulmuştur.
Supporting Institution
TÜBİTAK ve MİMSAN AŞ.
Project Number
114M116 (TÜBİTAK 3001 projesi)
Thanks
Bu çalışma 114M116 numaralı TÜBİTAK 3001 projesinden elde edilen veriler ile gerçekleştirilmiştir. TÜBİTAK’a ve destekçi firma MİMSAN AŞ’ne verdikleri destek sebebiyle teşekkür ederim.
References
- Brown S H., (2009). Multiple Linear Regression Analysis: A Matrix Approach with MATLAB, Alabama Journal of Mathematics, Vol.34, pp.1-3.
- Catalina T., Iordache V. ve Caracaleanu B., (2013). Multiple regression model for fast prediction of the heating energy demand, Energy and Buildings, 57, pp 302–312. https://doi.org/10.1016/j.enbuild.2012.11.010
- Clay K., Lewis J. ve Severnini E., (2024). The historical impact of coal on cities, Regional Science and Urban Economics, 107 (2024) 103951 pp 1-9. https://doi.org/10.1016/j.regsciurbeco.2023.103951
- Erken H T., (2016). Pulverize Kömür Kazanında Yakıcı Açılarının Alev Yapısı Üzerine Etkisinin İncelenmesi, İstanbul Teknik Üniversitesi, Yüksek Lisans Tezi, İstanbul.
- Der O., Ordu M., ve Basar G., (2024). Optimization of cutting parameters in manufacturing of polymeric materials for flexible two-phase thermal management systems, Materials Testing, 2024. doi.org/10.1515/mt-2024-0127
- Golgiyaz S., Talu M F., Daskin M. ve Onat C. (2022). Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network, Alexandria Engineering Journal, 61, 1079–1089. https://doi.org/10.1016/j.aej.2021.06.022
- Mammadli S., (2017). Financial time series prediction using artificial neural network based on Levenberg-Marquardt algorithm, Procedia Computer Science, 120, pp 602–607.
- Onat C. (2019). A new design method for PI–PD control of unstable processes with dead time, ISA Transactions, 84, 69–81. https://doi.org/10.1016/j.isatra.2018.08.029
- Onat C. ve Daskin M., (2019). A Basic ANN System for Prediction of Excess Air Coefficient on Coal Burners Equipped with a CCD Camera, Mathematics and Statistics 7(1) pp 1-9. DOI: 10.13189/ms.2019.070101
- Onat C., Daskin M., Toraman S., Golgiyaz S. ve Talu M F., (2021). Prediction of combustion states from flame image in a domestic coal burner, Measurement Science and Technology, 32(7), pp 1-10. DOI: 10.1088/1361-6501/abe446
- Talu M F., Onat C. ve Daskin M., (2017). Prediction of excess air factor in automatic feed coal burners by processing of flame images, Chinese J. Mech. Eng. 30 (3) (May 2017) 722–731. https://doi. org/10.1007/s10033-017-0095-3.
- Yadav S. ve Mondal S S., (2019). A complete review based on various aspects of pulverized coal combustion, Int J Energy Res. 2019;43 pp 3134–3165. https://doi.org/10.1002/er.4395
- Yılmaz A O. ve Uslu T.,(2007). The role of coal in energy production—Consumption and sustainable development of Turkey, Energy Policy 35 pp 1117–1128 https://doi.org/10.1016/j.enpol.2006.02.008
- You C F. ve Xu X C., (2010). Coal combustion and its pollution control in China, Energy 35 pp 4467–4472. https://doi.org/10.1016/j.energy.2009.04.019
Abstract
In this study, a method increasing the forecast accuracy of the excess air coefficient from the flame image in a domestic coal burning system equipped with a CCD (Charge Couple Device) camera has been proposed. The proposed method is based on a multiple linear regression formula that reveals the relationship between the digital flame information obtained from the camera and the flue gas temperature with the excess air coefficient.
The simple structure of the architecture created with this relation is an important advantage in terms of practical applications. The accuracy study based on experimental data shows that the proposed system significantly increases the accuracy compared to the traditional system.
Project Number
114M116 (TÜBİTAK 3001 projesi)
References
- Brown S H., (2009). Multiple Linear Regression Analysis: A Matrix Approach with MATLAB, Alabama Journal of Mathematics, Vol.34, pp.1-3.
- Catalina T., Iordache V. ve Caracaleanu B., (2013). Multiple regression model for fast prediction of the heating energy demand, Energy and Buildings, 57, pp 302–312. https://doi.org/10.1016/j.enbuild.2012.11.010
- Clay K., Lewis J. ve Severnini E., (2024). The historical impact of coal on cities, Regional Science and Urban Economics, 107 (2024) 103951 pp 1-9. https://doi.org/10.1016/j.regsciurbeco.2023.103951
- Erken H T., (2016). Pulverize Kömür Kazanında Yakıcı Açılarının Alev Yapısı Üzerine Etkisinin İncelenmesi, İstanbul Teknik Üniversitesi, Yüksek Lisans Tezi, İstanbul.
- Der O., Ordu M., ve Basar G., (2024). Optimization of cutting parameters in manufacturing of polymeric materials for flexible two-phase thermal management systems, Materials Testing, 2024. doi.org/10.1515/mt-2024-0127
- Golgiyaz S., Talu M F., Daskin M. ve Onat C. (2022). Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network, Alexandria Engineering Journal, 61, 1079–1089. https://doi.org/10.1016/j.aej.2021.06.022
- Mammadli S., (2017). Financial time series prediction using artificial neural network based on Levenberg-Marquardt algorithm, Procedia Computer Science, 120, pp 602–607.
- Onat C. (2019). A new design method for PI–PD control of unstable processes with dead time, ISA Transactions, 84, 69–81. https://doi.org/10.1016/j.isatra.2018.08.029
- Onat C. ve Daskin M., (2019). A Basic ANN System for Prediction of Excess Air Coefficient on Coal Burners Equipped with a CCD Camera, Mathematics and Statistics 7(1) pp 1-9. DOI: 10.13189/ms.2019.070101
- Onat C., Daskin M., Toraman S., Golgiyaz S. ve Talu M F., (2021). Prediction of combustion states from flame image in a domestic coal burner, Measurement Science and Technology, 32(7), pp 1-10. DOI: 10.1088/1361-6501/abe446
- Talu M F., Onat C. ve Daskin M., (2017). Prediction of excess air factor in automatic feed coal burners by processing of flame images, Chinese J. Mech. Eng. 30 (3) (May 2017) 722–731. https://doi. org/10.1007/s10033-017-0095-3.
- Yadav S. ve Mondal S S., (2019). A complete review based on various aspects of pulverized coal combustion, Int J Energy Res. 2019;43 pp 3134–3165. https://doi.org/10.1002/er.4395
- Yılmaz A O. ve Uslu T.,(2007). The role of coal in energy production—Consumption and sustainable development of Turkey, Energy Policy 35 pp 1117–1128 https://doi.org/10.1016/j.enpol.2006.02.008
- You C F. ve Xu X C., (2010). Coal combustion and its pollution control in China, Energy 35 pp 4467–4472. https://doi.org/10.1016/j.energy.2009.04.019
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Mechanical Engineering (Other) |
| Journal Section | Research Article |
| Authors | |
| Project Number | 114M116 (TÜBİTAK 3001 projesi) |
| Early Pub Date | March 21, 2025 |
| Publication Date | April 7, 2025 |
| Submission Date | September 29, 2024 |
| Acceptance Date | November 26, 2024 |
| Published in Issue | Year 2025 Issue: 718 |
Derginin DergiPark'a aktarımı devam ettiğinden arşiv sayılarına https://www.mmo.org.tr/muhendismakina adresinden erişebilirsiniz.
ISSN : 1300-3402
E-ISSN : 2667-7520