Görüntü İşleme ve Makine Öğrenmesi Yöntemleri ile Baca Gazı Sıcaklığının Tahmin Edilmesi

Yıl 2019, Sayı: 16, 283 - 291, 31.08.2019

Sedat Golgiyaz Muhammed Fatih Talu Cem Onat

https://doi.org/10.31590/ejosat.568348

Cited By: 8

Öz

Bu makalede,
küçük ölçekli fındık kömürü yakıtlı brülörde baca gazı sıcaklığı tahmini ile
ilgili deneysel bir çalışma sunulmaktadır. Baca gazı sıcaklığı yakıt türüne
göre belli bir aralıkta olması gerekir aksi durumda kazanda korozyona sebep
olmaktadır. Bu çalışma kapsamında alev görüntüsünden öznitelikler elde
edilmiştir. Bu öznitelikler ve DVR modeli ile baca gazı sıcaklığı tahmin
edilmiştir. Alev görüntüsü CCD kamera ile alınmıştır. Aynı zamanda referans
baca gazı sıcaklığı, baca gazı analizörü ile alınmıştır.  Alev görüntüsü ve sıcaklık değeri aynı
bilgisayara kaydedilmiştir. Alev görüntüsü gri seviye görüntüsüne çevrilerek
öznitelikler elde edilmiştir. Öznitelikler elde edilirken alev görüntüsünün
yoğunluk dağılımı kullanılmıştır. Bu işlem için iki tip dağılım kullanılmıştır.
Birincisi görüntünün histogramı alınarak konumdan bağımsız yoğunluk dağılımının
elde edilmesidir. İkincisi satır ve sütun toplamlarını kullanarak uzamsal
yoğunluk dağılımının elde edilmesidir. Bu iki özniteliğin kombinasyonlarından
elde edilen öznitelikler 6 çeşit DVR modeli ile gerçekleştirilmiştir. En iyi
sonuçlar, her iki dağılımdan elde edilen özniteliklerin birlikte kullanıldığı
öznitelik çıkarma yöntemi için kübik DVR modeli ile elde edilmiştir. Önerilen
modelde baca sıcaklığı (T °C) doğruluk ile tahmin edilmiştir. Elde edilen
sonuçlar baca gazı sıcaklığı ile alev görüntüsü arasında yüksek oranda bir
ilişki olduğunu göstermektedir.

Anahtar Kelimeler

Baca gazı sıcaklığı tahmini, alev görüntüsü, destek vektör regresyon

Destekleyen Kurum

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)

Proje Numarası

117M121

Teşekkür

Bu çalışma, MIMSAN AŞ ve Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK, Proje no: 117M121) tarafından desteklenmiştir.

Estimation of Flue Gas Temperature by Image Processing and Machine Learning Methods

Öz

This paper presents an experimental study on the flue gas temperature estimation in small-scale nut coal-fired boiler. The flue gas temperature must be within a certain range depending on the fuel type, otherwise it causes corrosion in the boiler. Within the scope of this study, features were obtained from flame image. The flue gas temperature was estimated with these features and the SVR model. The flame image was taken with a CCD camera. At the same time, the reference flue gas temperature was taken with the flue gas analyzer. The flame image and temperature are recorded on the same computer. Flame image is converted to gray scale image and features are obtained. The intensity distribution of the flame image was used when obtaining the features. Two types of distribution were used for this process. The first is the histogram of the flame image to obtain a location independent intensity distribution. The second is to obtain a spatial intensity distribution using row and column sums. The attributes obtained from the combinations of these two type features were performed with 6 kinds of SVR models. The best results were obtained for the cubic SVR model for the feature extraction method in which the attributes obtained from both distributions were used together. In the proposed model the flue temperature (T ° C) was estimated with R = 0.97 accuracy. The results show that there is a high correlation between the flue gas temperature and the flame image.

Anahtar Kelimeler

Flue gas temperature estimation, flame image, support vector regression

Proje Numarası

117M121

Kaynakça

• Baek, W. B., Lee, S. J., Baeg, S. Y., & Cho, C. H. Flame image processing & analysis for optimal coal firing of thermal power plant. https://doi.org/10.1109/ISIE.2001.931596
• Baek, W. B., Lee, S. J., Baeg, S. Y., & Cho, C. H. (2001). Flame image processing & analysis for optimal coal firing of thermal power plant. {ISIE 2001: IEEE International Symposium on Industrial Electronics Proceedeing, Vols I-III}, {928-931}. https://doi.org/10.1109/ISIE.2001.931596
• Bilgin, A. (2007). Kazanlarda enerji Verimliliği ve Emisyonlar. In VIII. Ulusal Tesisat Mühendisliği Kongresi (pp. 33–40). İzmir: TMMOB Makina Mühendisleri Odası. Retrieved from http://www1.mmo.org.tr/resimler/dosya_ekler/c676cb12b9a742f_ek.pdf
• Bonefacic, I., Blecich, P., Bonefačić, I., & Blecich, -P. (2015). Two-color temperature measurement method using BPW34 PIN photodiodes. Engineering Review (Vol. 35). Retrieved from https://www.researchgate.net/publication/282818960
• Conti, J., Holtberg, P., Diefenderfer, J., LaRose, A., Turnure, J. T., & Westfall, L. (2016). International Energy Outlook 2016 With Projections to 2040. https://doi.org/10.2172/1296780
• GoldsWorthy, P., Eyre, D. J., & On, E. (2013). Value-in-use (VIU) assessment for thermal and metallurgical coal. The Coal Handbook: Towards Cleaner Production (Vol. 2). https://doi.org/10.1533/9781782421177.3.455
• Golgiyaz, S., Talu, M. F., & Onat, C. (2016). Estimation of Excess Air Coefficient for Automated Feed Coal Burners with Image-Based Gauss Model. In International Conferece on Artificial Intelligence and Data Processing, IDAP’16 (pp. 528–531). Malatya-Turkey.
• González-Cencerrado, A., Gil, A., & Peña, B. (2013). Characterization of PF flames under different swirl conditions based on visualization systems. Fuel, 113, 798–809. https://doi.org/10.1016/j.fuel.2013.05.077
• González-Cencerrado, A., Peña, B., & Gil, A. (2012). Coal flame characterization by means of digital image processing in a semi-industrial scale PF swirl burner. Applied Energy. https://doi.org/10.1016/j.apenergy.2012.01.059
• Huang, B., Luo, Z., & Zhou, H. (2010). Optimization of combustion based on introducing radiant energy signal in pulverized coal-fired boiler. Fuel Processing Technology, 91(6), 660–668. https://doi.org/10.1016/j.fuproc.2010.01.015
• Jiang, Z. W., Luo, Z. X., & Zhou, H. C. (2009). A simple measurement method of temperature and emissivity of coal-fired flames from visible radiation image and its application in a CFB boiler furnace. Fuel. https://doi.org/10.1016/j.fuel.2008.12.014
• Krabicka, J., Lu, G., & Yan SMIEEE, Y. (n.d.). A Spectroscopic Imaging System for Flame Radical Profiling.
• Li, B., Chen, F., & Shi, L. (2015). A coupling method of direct and inverse heat conduction problems for transient temperature calculation of a boiler drum. Dongli Gongcheng Xuebao/Journal of Chinese Society of Power Engineering, 35(2), 96–102. https://doi.org/10.1299/jtst.2016jtst0030
• Li, N., Lu, G., Li, X., & Yan, Y. (2014). Prediction of pollutant emissions of biomass flames using digital imaging, contourlet transform and Radial Basis Function network techniques. Conference Record - IEEE Instrumentation and Measurement Technology Conference, 697–700. https://doi.org/10.1109/I2MTC.2014.6860832
• Li, N., Lu, G., Li, X., & Yan, Y. (2016). Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques. Combustion Science and Technology, 188(2), 233–246. https://doi.org/10.1080/00102202.2015.1102905
• Li, T., Zhang, C., Yuan, Y., Shuai, Y., & Tan, H. (2018). Flame temperature estimation from light field image processing. Applied Optics, 57(25), 7259. https://doi.org/10.1364/ao.57.007259
• Liu, Z., Zheng, S., Luo, Z., & Zhou, H. (2016). A new method for constructing radiative energy signal in a coal-fired boiler. Applied Thermal Engineering, 101, 446–454. https://doi.org/10.1016/j.applthermaleng.2016.01.034
• Lou, C., Zhou, H. C., Yu, P. F., & Jiang, Z. W. (2007). Measurements of the flame emissivity and radiative properties of particulate medium in pulverized-coal-fired boiler furnaces by image processing of visible radiation. Proceedings of the Combustion Institute, 31 II, 2771–2778. https://doi.org/10.1016/j.proci.2006.07.178
• Mu, H., Li, Z., Han, Z., Li, J., Schlaberg, H. I., Liu, S., & Liu, S. (2015). Visualization Measurement of the Flame Temperature in a Power Station Using the Colorimetric Method. In Physics Procedia. https://doi.org/10.1016/j.egypro.2015.02.075
• Parveen, N., Zaidi, S., & Danish, M. (2017). Development of SVR-based model and comparative analysis with MLR and ANN models for predicting the sorption capacity of Cr(VI). Process Safety and Environmental Protection, 107, 428–437. https://doi.org/10.1016/j.psep.2017.03.007
• Peña, B., Bartolomé, C., & Gil, A. (2017). Analysis of thermal resistance evolution of ash deposits during co-firing of coal with biomass and coal mine waste residues. Fuel, 194, 357–367. https://doi.org/10.1016/j.fuel.2017.01.031
• Qi, M., Luo, H., Wei, P., & Fu, Z. (2018). Estimation of low calorific value of blended coals based on support vector regression and sensitivity analysis in coal-fired power plants. Fuel, 236, 1400–1407. https://doi.org/10.1016/j.fuel.2018.09.117
• Quej, V. H., Almorox, J., Arnaldo, J. A., & Saito, L. (2017). ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. Journal of Atmospheric and Solar-Terrestrial Physics, 155, 62–70. https://doi.org/10.1016/j.jastp.2017.02.002
• TALU, M. F., ONAT, C., & DASKIN, M. (2017). Prediction of Excess Air Factor in Automatic Feed Coal Burners by Processing of Flame Images. Chinese Journal of Mechanical Engineering, 30(3), 722–731. https://doi.org/10.1007/s10033-017-0095-3
• Vapnik, V. N. (2000). The nature of statistical learning theory. Springer. https://doi.org/10.1007/978-1-4757-3264-1
• Wang, F., Wang, X. J., Ma, Z. Y., Yan, J. H., Chi, Y., Wei, C. Y., … Cen, K. F. (2002). The research on the estimation for the NOxemissive concentration of the pulverized coal boiler by the flame image processing technique. Fuel, 81(16), 2113–2120. https://doi.org/10.1016/S0016-2361(02)00145-X
• Wang, Z., Liu, M., Dong, M., & Wu, L. (2017). Riemannian Alternative Matrix Completion for Image-Based Flame Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 27(11), 2490–2503. https://doi.org/10.1109/TCSVT.2016.2587378
• Wu, F., Zhou, H., Ren, T., Zheng, L., & Cen, K. (2009). Combining support vector regression and cellular genetic algorithm for multi-objective optimization of coal-fired utility boilers. Fuel, 88, 1864–1870. https://doi.org/10.1016/j.fuel.2009.04.023
• Xiangyu, Z., Shu, Z., Huaichun, Z., Bo, Z., Huajian, W., & Hongjie, X. (2016). Simultaneously reconstruction of inhomogeneous temperature and radiative properties by radiation image processing. International Journal of Thermal Sciences, 107, 121–130. https://doi.org/10.1016/j.ijthermalsci.2016.04.003
• Xiangyu, Z., Xu, L., yu, Y., Bo, Z., & Hongjie, X. (2018). Temperature measurement of coal fired flame in the cement kiln by raw image processing. Measurement: Journal of the International Measurement Confederation. https://doi.org/10.1016/j.measurement.2018.07.063
• Zhou, H., Han, C. (1994). An exploratory investigation of the computer-based control of utility coal-fired boiler furnace combustion. In J. Eng. Therm. Energy Power (pp. 111–116).
• Zhou, H., Tang, Q., Yang, L., Yan, Y., Lu, G., & Cen, K. (2014). Support vector machine based online coal identification through advanced flame monitoring. Fuel. https://doi.org/10.1016/j.fuel.2013.10.041