TY - JOUR T1 - Görüntü İşleme ve Makine Öğrenmesi Yöntemleri ile Baca Gazı Sıcaklığının Tahmin Edilmesi TT - Estimation of Flue Gas Temperature by Image Processing and Machine Learning Methods AU - Golgiyaz, Sedat AU - Talu, Muhammed Fatih AU - Onat, Cem PY - 2019 DA - August DO - 10.31590/ejosat.568348 JF - Avrupa Bilim ve Teknoloji Dergisi JO - EJOSAT PB - Osman SAĞDIÇ WT - DergiPark SN - 2148-2683 SP - 283 EP - 291 IS - 16 LA - tr AB - Bu makalede,küçük ölçekli fındık kömürü yakıtlı brülörde baca gazı sıcaklığı tahmini ileilgili deneysel bir çalışma sunulmaktadır. Baca gazı sıcaklığı yakıt türünegöre belli bir aralıkta olması gerekir aksi durumda kazanda korozyona sebepolmaktadır. Bu çalışma kapsamında alev görüntüsünden öznitelikler eldeedilmiştir. Bu öznitelikler ve DVR modeli ile baca gazı sıcaklığı tahminedilmiştir. Alev görüntüsü CCD kamera ile alınmıştır. Aynı zamanda referansbaca gazı sıcaklığı, baca gazı analizörü ile alınmıştır. Alev görüntüsü ve sıcaklık değeri aynıbilgisayara kaydedilmiştir. Alev görüntüsü gri seviye görüntüsüne çevrilereköznitelikler elde edilmiştir. Öznitelikler elde edilirken alev görüntüsününyoğunluk dağılımı kullanılmıştır. Bu işlem için iki tip dağılım kullanılmıştır.Birincisi görüntünün histogramı alınarak konumdan bağımsız yoğunluk dağılımınınelde edilmesidir. İkincisi satır ve sütun toplamlarını kullanarak uzamsalyoğunluk dağılımının elde edilmesidir. Bu iki özniteliğin kombinasyonlarındanelde edilen öznitelikler 6 çeşit DVR modeli ile gerçekleştirilmiştir. En iyisonuçlar, her iki dağılımdan elde edilen özniteliklerin birlikte kullanıldığıöznitelik çıkarma yöntemi için kübik DVR modeli ile elde edilmiştir. Önerilenmodelde baca sıcaklığı (T °C) doğruluk ile tahmin edilmiştir. Elde edilensonuçlar baca gazı sıcaklığı ile alev görüntüsü arasında yüksek oranda birilişki olduğunu göstermektedir. KW - Baca gazı sıcaklığı tahmini KW - alev görüntüsü KW - destek vektör regresyon N2 - 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. CR - 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 CR - Baek, W. B., Lee, S. J., Baeg, S. Y., & Cho, C. H. (2001). 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