Combustion Quality Estimation in Power Station Boilers using Median Threshold Clustering Algorithms

Combustion Quality Estimation in Power

Station Boilers using Median Threshold

Clustering Algorithms

*_{K.SUJATHA, **DR.N.PAPPA AND ***A. KALAIVANI} *K.Sujatha, Research Scholar, Dept of Instrumentation Engg., M.I.T., Chromepet, CH-25. **Dr.N.Pappa, A.P., Dept of Instrumentation Engg. M.I.T., Chromepet, CH-25.

***A. Kalaivani, A.P., Dep of MCA, Easwari Engineering College, Ramapuram, Chennai-89. Abstract

The estimation of combustion quality in power station boilers is of great importance in the present scenario as it plays an important role in controlling the air pollution. The harmful gases like NOx and CO from the chimney causes air pollution. The amount of NOx and CO concentration in flue gas can be maintained within admissible limits by analyzing the flame colour. The colour of the flame is affected by combustion quality. When complete combustion takes place the amount these gases at the exit are within tolerance. Hence if the quality of combustion was estimated from the flame using image processing technique, it is possible to minimize the air pollution. This new strategy developed uses median threshold for feature reduction and various clustering algorithms to estimate the quality of combustion.

Key words

Combustion quality, Image J, Feature reduction, Median threshold, Image Processing, K-means clustering algorithm, Farthest first algorithm, DBSCAN

1. Introduction  

The Neyveli Lignite Corporation (NLC), Neyveli, Tamil Nadu, India is one the major thermal power plants in India with highly modernized technology. The Thermal Power Station (TPS-Exp I) is the most recently renovated power station. The flame monitoring system used here indicates only the presence or absence of the flame inside the furnace. The major idea to determine the ON-OFF status was to prevent the explosion of boiler. The repeated addition of coal without monitoring the status of the flame will collapse the entire set up [1]. It is also possible to estimate the temperature and flue gas emissions by monitoring the flame colour [2, 3]. To avoid this situation the above said flame monitoring system is used. The existing set up at NLC is as shown in figure 1 below

1.1. Experimental set-up

The proposed technique for flame monitoring and combustion quality control includes the following device. The major components of the proposed flame monitoring system are

1. Infra-red camera placed inside a cooling jacket with servo-motor mechanism 2. CCTV set up placed in the control room

3. TV tuner for transferring the flame video from the CRT monitor on to the PC 4. Laptop connected to the TV tuner

5. The video file is split up into frames for further analysis

6. Image processing algorithms for analyzing the constituents of the flame 7. Intelligent control strategy to monitor and control the combustion quality 8. Validation of the developed system.

The features were extracted using Image J, the median threshold algorithm using Matlab for feature reduction and clustering algorithms like K-means, Farthest First and DB Scan algorithms [6, 7] was implemented with Weka.

2. Materials and Methods

The following stages are involved in flame video analysis is as shown in figure 2 a, b and c

Step 1: The video file is further processed by splitting into frames using any video splitter Step 2: The flame images are pre-processed for noise removal

Step 3: The features are extracted using Image J and normalised using Min-Max method Step 4: Features are reduced using Median threshold algorithm

Figure 2 (b). Procedural block diagram for flame image analysis by clustering without feature reduction

Figure 2 (c). Procedural block diagram for flame image analysis for combustion quality control

2.1 Pre-processing

Pre-processing is done to remove noise. The frame size acquired was 320x240. It is sufficient that we take 30x30 portion of the image for further analysis. Median filtering was done to remove noise. Histogram analysis was done to find the quality of the acquired image.

2.1.1 Median filtering

The extracted flame images if corrupted with noise, then filtering need to be done. Median filter was used for noise removal. The simulation for addition and removal of noise was done using Image J. Figure 3 shows the effect of median filter on the corrupted image.

Figure 3. Effect of median filtering on the corrupted flame image

(a)

(b)

2.1.2 Histogram Analysis

Histogram is the plot of the intensity values and the frequency of occurrence. If the dynamic range of the histogram is large then the quality of combustion is good. The dynamic range is the difference between the maximum and minimum intensity values. The plot in figure 4 shown below was obtained using Image J

Figure 4. Histogram for flame images captured with varying combustion conditions

(a) Complete Combustion (b) Incomplete combustion

Since the flame images acquired vary with colour intensity under different combustion conditions it becomes a must to analyse the images in R, G and B planes. The results of the analysis in all the three are shown in the figure 5 below

Figure 5. Profile plot for flame images in R,G,B plane for complete combustion

(a) Red plane

(c) Green plane

Figure 6. Profile of the flame image with complete and incomplete combustion

(a) Complete combustion (b) Incomplete combustion

The figure 6 above shows the profile of the flame images obtained under complete and incomplete combustion conditions. It is inferred that the intensity value changes with respect to quality of combustion. If the combustion is complete then the maximum intensity value is 255 but whereas for incomplete combustion the maximum intensity value is only 85. Similarly the surface plot in figure 9 shows that the flame image has many pixels with intensity closer to 255.

Figure 7. Surface plot for flame images obtained when the combustion is complete

2.2 Feature Extraction

2.3 Normalization

The extracted features for complete and incomplete combustion are normalized using the Min-Max criteria. Normalization reduces computational complexity. Features extracted and normalized using Image J for complete and incomplete combustion.

2.4Feature reduction

The features were selected using the median threshold algorithm given below. The statistical measures like mean and median were selected as the threshold values. Previously the threshold was the mean and overall mean. Considering median as the threshold yields better results. The block diagram for feature reduction is given in figure 8 and the results are tabulated in table 1 below

1) The mean of each column was calculated

2) The threshold was calculated by finding the median for the mean of each feature

3) The features having a mean less than or equal to the threshold were alone selected. The selected features alone are numbered serially and those unselected were numbered ‘0’

Figure 8. Block diagram for Median threshold algorithm

Table 1. Central Measure based on Statistical - including all features using Min-Max Normalization

Mean 0.99704 0.95063 0.7485 0.9970 0.15903 0.99729 0.98236 0.97401 0.9653

Median

Threshold 0.97401 – obtained as the median of the above calculated mean Features

selected 0 2 3 0 5 0 0 0 9

2.5 Clustering

This was done to cluster the images belonging to the two categories. The K-means algorithm, Farthest First and Density Based Spatial Clustering of Application with Noise (DBSCAN) algorithms are used to identify the combustion conditions. The above said algorithms are implemented using Weka.

2.5.1 Farthest First Algorithm

The k-center clustering problem is also called minmax radius clustering problem, whose objective is to

minimize the maximum diameter of any cluster on some set of points. A simple 2-approximation algorithm for the k-center clustering problem is proposed by Gonzales, which utilizes a farthest-point clustering heuristic. In this paper, the algorithm and its variant are used as the initialization methods for k-modes clustering. The

farthest-point heuristic starts with an arbitrary point s1. Pick a point s2 that is as far from s1 as possible. Pick si

to maximize the distance to the nearest of all centroids picked so far. That is, maximize the min {dist (si, s1), dist (si, s2) ...}. After all k representatives are chosen we can define the partition of D: cluster Cj consists of all

points closer to sj than to any other representative. As shown in, let the maximum radius of these k clusters be σ,

then even in the optimal k-clustering the maximum radius must be at least σ / 2. Here optimal is in the sense of

minimizing the maximum radius of any cluster. Obviously, farthest-point heuristic based method has the time complexity O (nk), where n is number of objects in the dataset and k is number of desired clusters. Furthermore,

one can implement farthest-point heuristic based method in constant dimension in O (nlogk) time. In fact, it can be solved in O (n) this case. Clearly, farthest-point heuristic based method is fast and suitable for large-scale

2.5.2 Density Based Spatial Clustering of Application with Noise (DBSCAN)

DBSCAN is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996. It is a density based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature. DBSCAN's definition of a cluster is based on the notion of density reach-ability. Basically, a point q is directly density-reachable from a point p if it is not farther away

than a given distance ε (i.e., is part of its ε-neighbourhood), and if p is surrounded by sufficiently many points

such that one may consider p and q be part of a cluster. q is called density-reachable from p if there is a

sequence of points with and p1 = p and pn = q where each pi + 1 is directly density-reachable from pi. Note that the relation of density-reachable is not symmetric (since q might lie on the edge of a cluster, having

insufficiently many neighbours to count as a genuine cluster element), so the notion of density-connected is

introduced at two points p and q are density-connected if there is a point o such that o and p as well as o and q

are density-reachable [10]. A cluster, which is a subset of the points of the database, satisfies two properties: 1. All points within the cluster are mutually density-connected.

2. If a point is density-connected to any point of the cluster, it is part of the cluster as well.

2.5.3 K-means algorithm

K-means clustering is a well-known method of assigning cluster membership by minimizing the differences among items in a cluster while maximizing the distance between clusters. The "means" in k-means refers to the centroid of the cluster, which is a data point that is chosen arbitrarily and then refined iteratively until it represents the true mean of all data points in the cluster. The "k" refers to an arbitrary number of points that are used to seed the clustering process. The k-means algorithm calculates the squared Euclidean distances between data records in a cluster and the vector that represents the cluster mean, and converges on a final set of k clusters when that sum reaches its minimum value [9].

3. Results and Discussions

The results for the clustering algorithms with and without feature reduction are shown below. The results obtained by using WEKA were tabulated in table 2 below

Table 2. Screen shots for Farthest First, DB scan and K-means algorithms using Weka

S.No Algorithm Screen Shots

2. DB Scan

3. K-means Clustering

Figure 9. Comparison chart for K-means, Farthest First and DB Scan algorithms

4. Conclusion

Thus the flame monitoring system was designed to automate the estimation of combustion quality in water tube boilers. This technique adds value as it is possible to infer combustion quality from the flame status thereby reducing the flue gas emissions. The Farthest First and DB scan clustering algorithms with reduced feature set provides better performance when compared to the other type. The above said statement is well established from the figure 9. The same can be extended to identify the flame temperature and thereby infer the flue gas emissions so as to automate the entire process.

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