Kömür Yakıcılarında Yanma Verimi Tahmini için Gömülü Platformda Çalışabilen Evrişimsel Sinir Ağının Parametre Analizi

Year 2023, , 48 - 54, 22.06.2023

Veysel Gündüzalp , Gaffari Çelik , Muhammed Fatih Talu , Cem Onat

https://doi.org/10.46810/tdfd.1190216

Abstract

Kömür yakıcılarında yanma veriminin doğru ve etkin bir şekilde hesaplanması endüstriyel kazan üreticileri için oldukça önemlidir. Kazan veriminin hesaplanabilmesi için iki temel yaklaşımın olduğu görülmektedir: 1) bacadan çıkan gazın analizi; 2) kazandaki yanma odasının görüntülenmesi. Kullanımı yeterince kolay olmayan baca gazı analizörleri yüksek maliyete sahiptir. Ayrıca baca ile yanma odası arasındaki fiziksel uzaklık yapılan ölçümün zaman gecikmeli olmasına neden olmaktadır. Yanma odasının görüntülenmesine dayalı yöntemler bahsedilen dezavantajları içermemektedir. Bu çalışmada önerilen ve yanma odasının görüntülenmesine dayanan sistemin literatüre iki katkısı bulunmaktadır: 1) yanma veriminin tahmininde ilk defa modern evrişimsel sinir ağları (ESA) yaklaşımının kullanılması; 2) Optimum parametrelere sahip ESA mimarisinin gömülü bir platformda çalışabilmesi. Klasik sınıflandırma teknikleri ve CPU destekli bir işlemci kartı kullanıldığında, 1,7 saniyede 1 adet alev formu görüntüsünden verim hesaplanabilirken, önerilen ESA mimarisi ve GPU destekli bir işlemci kartı kullanıldığında bu sayı saniyede yaklaşık 20 adet seviyesine çıkmaktadır (34 kat hızlı). Elde edilen sonuçlar, kömür kazanı yanma verimi tahmininde önerilen ESA mimarisinin ve donanımının klasik yaklaşımlara olan üstünlüğünü açık bir şekilde ortaya koymaktadır.

Keywords

Kömür, Yakıcı Sistem, Yanma Verimi, Görüntü İşleme, Evrişimsel Sinir Ağları

Parameter Analysis of Convolutional Neural Network Operated on Embedded Platform for Estimation of Combustion Efficiency in Coal Burners

Abstract

Accurately and effectively calculating combustion efficiency in coal burners is crucial for industrial boiler manufacturers. Two main approaches can be used to calculate boiler efficiency: 1) Analyzing the gas emitted from the flue; 2) Visualizing the combustion chamber in the boiler. Flue gas analyzers, which are not user-friendly, come with high costs. Additionally, the physical distance between the flue and the combustion chamber causes the measurement to be delayed. Methods based on visualizing the combustion chamber do not have these disadvantages. This study proposes a system based on visualizing the combustion chamber and has two contributions to the literature: 1) for the first time, the modern Convolutional Neural Networks (CNN) approach is used to estimate combustion efficiency; 2) the CNN architecture with optimal parameters can work on an embedded platform. When classical classification techniques and a CPU-supported processor card are used, efficiency can be calculated from one flame image in 1.7 seconds, while this number increases to approximately 20 frames per second (34 times faster) when the proposed CNN architecture and GPU-supported processor card are used. The results obtained demonstrate the superiority of the proposed CNN architecture and hardware over classical approaches in estimating coal boiler combustion efficiency.

Keywords

Coal, Combustor, Combustion Efficiency, Image Processing, Convolutional Neural Networks

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