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Öğe Artificial neural network regression model to predict flue gas temperature and emissions with the spectral norm of flame image(Elsevier Sci Ltd, 2019) Golgiyaz, Sedat; Talu, Muhammed Fatih; Onat, CemThis paper presents an experimental study on flue gas temperature (FGT) and emissions estimation in home-type nut coal-fired burner. The proposed method does not require prior knowledge of Charge-Coupled Device (CCD) camera features. Therefore, it can be applied easily without costly and complex adaptation requirement to control the combustion process. In the proposed system, the flame image was taken with a CCD camera. At the same time, reference temperature and emissions were taken with flue gas analyzer. Combustion characteristics were extracted by image processing techniques from each two-color channels of the flame image. When the features were obtained, instead of converting the flame image to grayscale and obtaining the general features, local feature extraction was preferred from each of the two-color channels that express the combustion process better. For this process, the image was divided into local windows and individual features for each two-color channel was extracted. The optimum number of windows was decided by experimental investigation. The features were obtained by using the spectral norm of the region of interest. The obtaining image features were used to train the Artificial Neural Network (ANN) regression model which predicted the FGT and emissions. Estimation accuracy (correlation coefficient (R)) of developed FGT prediction model is 0.99. The emission prediction models estimate SO2, O-2, NOx, CO2 andCO emissions with R = 0.97, R = 0.96, R = 0.77, R = 0.96, and R = 0.87 accuracies, respectively. The experimental results show that the FGT and emissions can be estimated by the flame image.Öğe Bilgisayarlı görü teknikleriyle kömür yakıtlı kazanlardaki yanma veriminin tahmini(İnönü Üniversitesi, 2020) Golgiyaz, SedatKömür yakıtlı kazanlar kişisel ev, bina veya iş yerlerinin ısıtmalarında yaygın bir şekilde kullanılmaktadır. Kazanın verimli yakılması, ekonomik bir katma değer sağlamakla birlikte atmosfere bırakılan zararlı gazları azaltarak çevreye olumlu katkı sağlamaktadır. Bu tez çalışmasında, ev tipi kömür kazanlarının yanma verimini bilgisayarlı görme teknikleriyle otomatik hesaplayabilen yeni bir sistem önerilmektedir. Bu sistem, yanma sürecinde kazan içerisindeki alev formu görselini elde ederek profesyonel baca gazı analizör cihazlarıyla ölçülen verim değerlerine eşlemektedir. Bunun için elde edilen yüksek boyutlu alev görüntüleri düşük boyutlu öznitelik vektörlerine indirgenmekte ve yapay öğrenme teknikleriyle kazan veriminin tahmini yapılmaktadır. Tez kapsamında, birçok farklı öznitelik çıkarma ve modelleme yaklaşımının eşleşme doğruluğu üzerindeki etkisi analiz edilmiştir. Bu tezle sağlanan temel bilimsel katkı, mevcut yöntemlerle kıyaslandığında alev görseli ile verim ölçümü arasındaki en yüksek eşleştirme doğruluğunu sağlayan matematiksel modellerin geliştirilmesidir. Ayrıca alev görselinden baca gazı sıcaklığını tahmin edebilen matematiksel bir model de önerilmştir. Geliştirilen tahmin modellerinin prototip bir kömür kazanı üzerinde gerçek zamanlı uygulamaları yapılmıştır.Öğe Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network(Elsevier, 2022) Golgiyaz, Sedat; Talu, Muhammed Fatih; Das, Mahmut; Onat, CemIt is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient (lambda) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, lambda data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature-lambda. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.Öğe Flame stability measurement through image moments and texture analysis(Iop Publishing Ltd, 2023) Golgiyaz, Sedat; Cellek, M. Salih; Daskin, Mahmut; Talu, M. Fatih; Onat, CemIn this article, the first two moments of the image, mean and standard deviation, uniform local binary pattern (LBP) texture analysis methods were experimentally investigated in coal-fired boilers to measure flame stability. The first two moments of the flame image were used to evaluate the flame stability in terms of color and brightness (average gray value). Although the radiation signal of the flame is widely obtained by the spectral analysis method, the radiation signal of the flame was obtained by the LBP texture analysis method in this study. The flame stability measurement technique proposed in this study does not require prior knowledge about charged coupling devices camera features. Therefore, it can be easily applied to measure flame stability without expensive and complicated adaptation processes. Flame stability was measured with R = 0.9868 accuracy with the proposed method. The experimental results show that the proposed texture analysis method is more effective than current spectral analysis methods. The results obtained within the scope of this study also show that it can be easily applied to existing closed-loop control systems to monitor flame stability.Öğe Görüntü İşleme ve Makine Öğrenmesi Yöntemleri ile Baca Gazı Sıcaklığının Tahmin Edilmesi(2019) Golgiyaz, Sedat; Talu, Muhammed Fatih; Onat, CemÖz: 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.Öğe Prediction of combustion states from flame image in a domestic coal burner(Iop Publishing Ltd, 2021) Onat, Cem; Daskin, Mahmut; Toraman, Suat; Golgiyaz, Sedat; Talu, Muhammed FatihCoal is still a strategic fuel for many developing countries. The environmental impact of emissions resulting from the widespread use of coal worldwide is a matter of serious debate. In this perspective, clean coal burning technologies are in demand. In this study, a measurement system that estimates emission from flame images in a domestic coal burner is proposed. The system consists of a charge-coupled device camera, image processing software (real time image acquisition, noise reduction and extracting features) and artificial intelligence elements (classification of features by neural networks). In feature extraction stage, only five flame region features (G(x), G(y) , trace, L (2) and L (infinity) norm) is extracted. G(cx) and G(cy) are the instantaneous change of the horizontal and vertical components of center mass of the flame image. These features are new concepts for emission estimation from the flame image. The proposed system makes a difference with its simpler structure and higher accuracy compared to its counterparts previously presented in the literature.Öğe Real time fabric defect detection system on Matlab and C plus plus /Opencv platforms(Ieee, 2017) Hanbay, Kazim; Golgiyaz, Sedat; Talu, Muhammed FatihIn industrial fabric productions, real time systems are needed to detect the fabric defects. This paper presents a real time defect detection approach which compares the time performances of Matlab and C++ programming languages. In the proposed method, important texture features of the fabric images are extracted using CoHOG method. Artificial neural network is used to classify the fabric defects. The developed method has been applied to detect the knitting fabric defects on a circular knitting machine. An overall defect detection success rate of 93% is achieved for the Matlab and C++ applications. To give an idea to the researches in defect detection area, real time operation speeds of Matlab and C++ codes have been examined. Especially, the number of images that can be processed in one second has been determined. While the Matlab based coding can process 3 images in 1 second, C++/Opencv based coding can process 55 images in 1 second. Previous works have rarely included the practical comparative evaluations of software environments. Therefore, we believe that the results of our industrial experiments will be a valuable resource for future works in this area.Öğe Unburnt carbon estimation through flame image and gauss process regression(Taylor & Francis Ltd, 2024) Golgiyaz, Sedat; Demir, Usame; Cellek, Mehmet Salih; Daskin, Mahmut; Talu, M. Fatih; Onat, CemThe presence of unburned carbon in coal-burning systems undoubtedly causes a loss in the amount of energy that can be obtained from the system, and also reveals an inadequacy in terms of the usability of the ashes. The expensiveness of existing unburned carbon prediction methods is one of the reasons why these technologies cannot be used. This situation requires working on alternative non-combustible carbon technologies. In this paper, a new approach is presented for estimating unburned carbon in a small-scale coal burner system using the Gaussian regression model and CCD camera-acquired flame image. The proposed approach evaluates brightness, fluctuation amplitude, area, and radiation signal properties of the flame image. The proposed non-combustible carbon estimation technique does not require prior knowledge of CCD camera features. In the feature acquisition phase, results were obtained for each natural component of the flame image in RGB colour space separately, in pairs, all together and for three artificial colour channels (grey image). With the proposed method, the unburned carbon estimation was obtained with an accuracy of R = 0.9664 when all colour channels of the RGB image were used together. This result shows that unburned carbon can be estimated from the instantaneous flame images obtained with the CCD camera.
