Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network

A computationally-supported experimental technique is presented, to measure height of luminous flames, using convolution and density-based spatial clustering for image processing. The experimental setup employs a high-definition camera array to capture flame imagery from 0°, 45° and 90° visualization planes. The volumetric fuel flow was ranged from 350 to 1800 cc/min and images of the resulting flame structure were captured and measured. Results show that output measurements are affected by both the volumetric fuel flow and the visualization plane. Whilst the former is evidently the most significant factor, the latter was found to be relevant, since several flame features, particularly, flame tilt and flickering, are only perceivable through specific visualization planes; attributed to uneven flame structure due non-homogeneous thermal stress. The experimental technique proposed yields an overall statistical tolerance of 1.29 cm and an expanded uncertainty of 0.609 cm (~11.5%). From these results, the test is considered successful and the proposed experimental technique is deemed to be on par with the already existing ones. The utilization of spatial density clustering of image gridded data has only been tested for this implementation; being severely constrained by the sample size and density variability of the data. Consequently, care should be exercised. Nonetheless, this approach was found to inherently recognize flame front edge, and mitigate variations of pixel value due change in flame intensity, normalizing image processing, hence, it is proposed as a viable alternative for flame feature/structure visualization and estimation, and further research is encouraged.

Section: Computer Vision and Image Processingmentioning

confidence: 99%

Image convolution-based experimental technique for flame front detection and dimension estimation: a case study on laminar-to-transition jet diffusion flame height measurement

León-Ruiz¹,

Carvajal-Mariscal²,

Cruz-Ávila³

et al. 2022

“…The main goal of addressing flame stability in a combustion system is to achieve high combustion efficiency, low pollutant emissions and safe plant operation. Theoretical and experimental studies have been carried out to understand combustion instability in depth [7][8][9][10][11][12][13][14][15]. In these studies, spectroscopic cameras [16], laser-based imaging [17,18], charged coupling devices (CCD) camera [19][20][21][22][23], infrared absorption [24], and using different camera types simultaneously [8,9] were used.…”

Section: Introductionmentioning

confidence: 99%

“…When examining flame stability, two cases such as flame stable-unstable [9,25], or three cases such as incomplete, complete, and partial combustion [26] are examined. To verify flame stability, excess air coefficient (λlambda) [9,13,14] or user-labeled flame images [25,26] are generally used. In some studies, flame stability is decided by examining the combustion reaction parameters in detail [27].…”

Section: Introductionmentioning

confidence: 99%

Flame stability measurement through image moments and texture analysis

Golgiyaz¹,

Cellek²,

Daskin³

et al. 2023

Self Cite

In 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 CCD 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.

“…The onlinemeasured flame images provide valuable information about the current burning status [12]. They have been used to predict the outlet content of the flue gas [13], oxygen content [1], flue gas temperature [14], emission [15], and excess air coefficient [16]. However, the features need to be extracted first from the flame images based on feature extraction methods before training prediction models such as the artificial neural network (ANN) regression model in [14].…”

Section: Introductionmentioning

confidence: 99%

Augmented flame image soft sensor for combustion oxygen content prediction

Gao

Dai

et al. 2022

Oxygen content is one of the most critical factors for high-efficiency combustion. Online measurement of oxygen content from flame images is important but still challenging. For construction of an oxygen content prediction model, most current feature extraction methods are not straightforward. Additionally, there are always sufficient data for common operating conditions in practice, while only limited data for other operating conditions. The data collection process for model training is costly and time-consuming. To tackle the problem, this work presents an augmented flame image soft sensor for automated combustion oxygen content prediction. A convolutional neural network (CNN) regression model is designed to predict the oxygen content directly from flame images, without a single feature extraction process. Moreover, a regression generative adversarial network with gradient penalty (RGAN-GP) is proposed to generate flame images with oxygen content labels. It overcomes the imbalanced and insufficient data problem arising in the CNN regression model training. The proposed soft sensor is compared with several common regression methods for oxygen content prediction. Experimental results show that the proposed method can predict the combustion oxygen content with high accuracy from flame images although the original datasets are imbalanced.