SavedAdd your journal

Journals / Mühendis ve Makina / 2026 / Sayı: 718

Evaluation on the Effect of Suspension System to Pointing Quality in a Mobil Weapon Platform

Kömür Yakma Sistemlerinde Verim Tahmini Doğruluğunu Artıran Bir Yöntem

Cem Onat(ADIYAMAN UNIVERSITY)0000-0002-4295-4860
Pages
116–128
Publication Date
2025-04-07
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The accuracy study based on experimental data shows that the proposed system significantly increases the accuracy compared to the traditional system.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:The accuracy study based on experimental data shows that the proposed system significantly increases the accuracy compared to the traditional system.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Kömür · yakıcı · alev

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.

Özet

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.

🔖

Cite This Article

Onat, C. (2025). Evaluation on the Effect of Suspension System to Pointing Quality in a Mobil Weapon Platform. Mühendis ve Makina, Sayı: 718, 116–128. https://doi.org/10.46399/muhendismakina.1557793

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. Brown S H., (2009). Multiple Linear Regression Analysis: A Matrix Approach with MATLAB, Alabama Journal of Mathematics, Vol.34, pp.1-3.
  2. 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
  3. 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
  4. Erken H T., (2016). Pulverize Kömür Kazanında Yakıcı Açılarının Alev Yapısı Üzerine Etkisinin İncelenmesi, İstanbul Teknik Üniversitesi, Yüksek Lisans Tezi, İstanbul.
  5. 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
  6. 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
  7. Mammadli S., (2017). Financial time series prediction using artificial neural network based on Levenberg-Marquardt algorithm, Procedia Computer Science, 120, pp 602–607.
  8. 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
  9. 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
  10. 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
  11. 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.
  12. 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
  13. 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
  14. 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