Sedat GOLGİYAZ, Cem ONAT, Muhammed Fatih TALU

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

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 çalışmakapsamında 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. Alevgörüntüsü ve sıcaklık değeri aynı bilgisayara kaydedilmiştir. Alev görüntüsü gri seviye görüntüsüne çevrilerek öznitelikler eldeedilmiştir. 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 sütuntoplamlarını kullanarak uzamsal yoğunluk dağılımının elde edilmesidir. Bu iki özniteliğin kombinasyonlarından elde edilenöznitelikler 6 çeşit DVR modeli ile gerçekleştirilmiştir. En iyi sonuçlar, her iki dağılımdan elde edilen özniteliklerin birliktekullanıldığı öznitelik çıkarma yöntemi için kübik DVR modeli ile elde edilmiştir. Önerilen modelde baca sıcaklığı (T °C) R =0.97 doğruluk ile tahmin edilmiştir. Elde edilen sonuçlar baca gazı sıcaklığı ile alev görüntüsü arasında yüksek oranda bir ilişkiolduğunu göstermektedir.

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

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.

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