{'fraction_non_alphanumeric': 0.021710924660496497, 'fraction_numerical': 0.014445117204394084, 'mean_word_length': 4.701183431952662, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Flame analysis using image processing techniques

This paper presents image processing techniques with the use of fuzzy logic and neural network approach to perform flame analysis. Flame diagnostic is important in the industry to extract relevant information from flame images. Experiment test is carried out in a model industrial burner with different flow rates. Flame features such as luminous and spectral parameters are extracted using image processing and Fast Fourier Transform (FFT). Flame images are acquired using FLIR infrared camera. Non-linearities such as thermal acoustic oscillations and background noise affect the stability of flame. Flame velocity is one of the important characteristics that determines stability of flame. In this paper, an image processing method is proposed to determine flame velocity. Power spectral density (PSD) graph is a good tool for vibration analysis where flame stability can be approximated. However, a more intelligent diagnostic system is needed to automatically determine flame stability. In this paper, flame features of different flow rates are compared and analyzed. The selected flame features are used as inputs to the proposed fuzzy inference system to determine flame stability. Neural network is used to test the performance of the fuzzy inference system.


Introduction
Flame visualization and characterization using digital image processing is a tool to provide an understanding of flame features related to flame stability. Flame features are extracted in both time and frequency domains of flame with different flow rates to perform flame analysis. The features in time domain are the luminous parameters, while features in frequency domain are the spectral parameters. Flame velocity determines the rate of expansion of flame, while thermal acoustic oscillations cause wear and tear to burners in industry, thus affecting flame stability. Unstable flames also cause harmful effects such as release of nitrogen oxide gas, thermal stress and vibrational phenomena [1].
The flame images' brightness is used to approximate flame velocity [2], while flame visualization and characterization techniques extract luminous and spectral parameters that represents combustion states for monitoring and control purposes [3,4]. Fuel (or coal) type can be identified through image processing of its flame features under various combustion conditions [5]. Features extracted using digital signal processing are used as inputs to fuzzy inference system for identification of coal type being burnt [6]. Neural networks are used to map flame features to fuel type [7], while power spectral densities (PSD) have been used to provide oscillatory characteristics of flames, which is important to understand the flame structure and stability [8]. Other parameter such as the oscillation frequency has been obtained from image processing to derive a universal index, a simple universal index for flame stability assessment [9]. The above flame visualization methods have been carried out using advanced optical cameras, and are more suitable for laboratory research studies. A more simpler and cost-effective method is required to capture flame images in industrial furnaces. Infrared (IR) cameras are suitable to capture flame images as it is able to capture the flame radiation and movement of flame [2]. Flame images are processed using image processing techniques with Fast Fourier Transform (FFT) to extract flame features.
The main contributions of this work are as follows. First, the mean velocities of the flames for different fuel flow rates are determined using the image processing techniques. The PSD of the flame with different flow rates are then determined for comparison. The second contribution of the work includes a fuzzy logic scheme which uses the extracted luminous features as inputs to predict flame stability. Finally, a neural network approach is used to investigate the performance of the fuzzy inference system and the correlation between the flame features with the flame stability.

Combustion Test Facility and Experiments
Flame images are recorded from a lab scale burner in TNB Research Sdn, Bhd. that has a diameter of 50mm as shown in Figure 1. Two videos are recorded for flames with different fuel flow rates of 15 lpm CO2 + 10 lpm CH4 flow rate and 30 lpm CO2 + 10 lpm CH4. The videos are recorded using an IR camera from FLIR (model A615). The videos are used to extract a sequence of images for image processing.  threshold has been applied) are transformed to HSV images for further processing (refer Figure 3). (iii) Edge detection using Sobel filter. Edges of the flame images represent its' shape and structure and therefore needs to be identified as accurate as possible [12]. In this study, a filtering method by Sobel is used to determine the edges of the flame by applying convolving masks to the image in the x and y directions (refer Figure 4).

2.2.2.
Proposed method to determine flame velocity. The area of the flame is filled with white pixels after edge detection is shown in Figure 5. The images are then converted back to grayscale image as shown in Figure 5. A threshold value is applied to the grayscale images based on equation (1). A white pixel is detected if the grayscale value is greater than 1. The mean flame velocity is then approximated by determining the difference in the number of white pixels of the two consecutive image frames, divided by the frame rate of the video as shown in equation (2). The difference in the number of pixels is converted into distance. In this study, 325 pixels represents 50 mm, which is the diameter of the lab scale burner.

Extraction of flame features.
In this study, flame features are divided into luminous parameters and spectral parameters. Luminous parameters such as brightness and its fluctuation provide information on the statistical distribution of the flame's radiation. Spectral parameters such as power spectral density (PSD) are representative of the flame radiation signals' distribution and are related to the flame's stability.
(i) Luminous parameters. Each individual pixel of the images represents the grey values in the time domain and is used to determine the brightness and fluctuation. The brightness is determined from equation (3): where are grey values, N is the number of values and G is the maximum gray value.
The fluctuation is determined from equation (4): The flame area, Ai is calculated through: where the threshold is estimated from the maximum background noise of the image [9]. Previous work has shown that the brightness of the image is related to radiation level while (ii) Spectral parameters. Spectral parameters are frequency domain features, and is an analysis of natural resonance. Spectral parameters are used to monitor vibration signals and ball bearing faults [13]. This study uses power spectral densities (PSD) to determine the fluctuation of the flame. The PSD is given as: where is the PSD estimate, N is the number of samples and ( ) is obtained via Fast Fourier Transform (FFT) of the N data sequence. Based on the PSD, the flame's flicker F (or the flame's fluctuation) is given as: where is the PSD of kth frequency and is the kth frequency.

Results and discussions
In this section, the mean velocities and PSD of the flame with different flow rates are compared and analyzed. A discussion of the flame features from the proposed fuzzy inference system is presented.

Mean velocity and PSD of flame with different flow rates 3.1.1 Mean velocities.
The flame velocities with respect to time for 15 and 30 lpm CO2 are shown in Figure 6, while Table 1 shows the mean flame velocities and the standard deviations for both flames. From Table 1, the results show that there is no significant difference in mean velocities and standard deviation for both flames with different CO2 flow rates. This is because in both cases the flames are opened or exposed to the surroundings, and the amount of oxidant are not in a controlled environment. Figure 7. The histograms for the PSD are shown in Figure 8. Based on the histogram plot in Figure 8, it can be observed that with a higher CO2 flow rate (30 lpm), there are higher occurrences of oscillations. The dominant PSD range is also higher at 0.8 to 1.0 (refer Table 1). The flame is also observed to be more turbulent. This is because the flame with a higher flow rate (30 lpm) is more affected by nonlinearities such as thermal oscillations.

Features Extraction
The flame features are extracted as inputs to the proposed fuzzy inference system. The extracted features in terms of the brightness, fluctuation amplitude, flame area and flicker are shown in Figure 9 to 11. The mean values of the extracted features are then shown in Table 2. Table 2, the mean brightness of the flame with 30 lpm CO2 + 10 lpm CH4 flow rates is lower than the flame with 15 lpm CO2 + 10 lpm CH4. This is caused by the higher CO2 flow rate. Both flames have a constant CH4 flow rate at 10 lpm and are mixed with CO2 before it is ignited at the outlet of the burner. At a higher CO2 flow rate, the total energy content in the gas that comes from CH4 is diluted. Hence, the flame with a higher CO2 flow rate is not as intense nor bright. The fluctuation amplitude and flicker for flame with 15 lpm CO2 + 10 lpm CH4 is higher because it has a higher energy content compared to the flame with 30 lpm CO2 + 10 lpm CH4. The change in the fluctuation amplitude represents a change in the luminous radiation, while the change in flicker reflects a change in the flame geometry. Unlike the mean flame velocities which are similar, these flame features show that both flames are distinctly different.

Implementation and Evaluation of fuzzy inference system
The brightness, fluctuation and flame area are used as inputs for the fuzzy inference system. The membership function for each input is shown from Figures 12 to 13. The performance of the fuzzy inference system is evaluated using a neural network fitting function by plotting the regression and mean square error as shown in Figures 13 and 14.  Based on Figure 13, the regression for the 15 lpm CO2 is 0.58 while the regression for 30 lpm flame is 0.97. The significant difference in the values is due to greater number of samples obtained for the flame with 30 lpm CO2 taken from the neural network. The video recorded for the 30 lpm CO2 flame was longer (31s) compared to the 15 lpm CO2 flame (24s). The high regression value for the 30 lpm CO2 flame shows that the fuzzy inference system has the potential to simplify the non-linear model into a linear model. Furthermore, it shows the potential to derive linear equations using flame features. The low mean square error shown in Figure 14 shows that the fuzzy inference system is reliable in predicting flame stability.

Conclusions
An image processing technique is proposed to determine the flame velocities flame for different fuel flow rates. No significant difference in flame velocities is observed for flames with CO2 flow rates of 15 lpm and 30 lpm. The higher oscillation in flame with flow rate of 30 lpm CO2 shows that the flame with flow rate of 15 lpm CO2 is more stable. In addition, flame features such as brightness, fluctuation and flicker show that flame with CO2 flow rates of 15 and 30 lpm are distinctly different. A proposed fuzzy inference system using extracted features as input to predict flame stability produces good performance from neural network but more experiments are needed to validate the results.