Skeleton extraction of boiler flame images based on mathematical morphology

In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.

Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods

Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN.

Prediction of Excess Air Factor in Automatic Feed Coal Burners by Processing of Flame Images

In this study, the relationship between the visual information gathered from the flame images and the excess air factor 2 in coal burners is investigated. In conventional coal burners the excess air factor 2. can be obtained using very expensive air measurement instruments. The proposed method to predict ） for a specific time in the coal burners consists of three distinct and consecutive stages; a） online flame images acquisition using a CCD camera, b） extrac- tion meaningful information （flame intensity and bright- ness）from flame images, and c） learning these information （image features） with ANNs and estimate 2. Six different feature extraction methods have been used： CDF of Blue Channel, Co-Occurrence Matrix, L-Frobenius Norms, Radiant Energy Signal （RES）, PCA and Wavelet. When compared prediction results, it has seen that the use of co- occurrence matrix with ANNs has the best performance （RMSE = 0.07） in terms of accuracy. The results show that the proposed predicting system using flame images can be preferred instead of using expensive devices to measure excess air factor in during combustion.

Design and implementation of gasifier flame detection system based on SCNN

Flame detection is a research hotspot in industrial production,and it has been widely used in various fields.Based on the ignition and combustion video sequence,this paper aims to improve the accuracy and unintuitive detection results of the current flame detection methods of gasifier and industrial boiler.A furnace flame detection model based on support vector machine convolutional neural network(SCNN)is proposed.This algorithm uses the advantages of neural networks in the field of image classification to process flame burning video sequences which needs detailed analysis.Firstly,the support vector machine(SVM)with better small sample classification effect is used to replace the Softmax classification layer of the convolutional neural network(CNN)network.Secondly,a Dropout layer is introduced to improve the generalization ability of the network.Subsequently,the area,frequency and other important parameters of the flame image are analyzed and processed.Eventually,the experimental results show that the flame detection model designed in this paper is more accurate than the CNN model,and the accuracy of the judgment on the flame data set collected in the gasifier furnace reaches 99.53%.After several ignition tests,the furnace flame of the gasifier can be detected in real time.

Measurements of Non-reacting and Reacting Flow Fields of a Liquid Swirl Flame Burner

The understanding of the liquid fuel spray and flow field characteristics inside a combustor is crucial for designing a fuel efficient and low emission device.Characterisation of the flow field of a model gas turbine liquid swirl burner is performed by using a2-D particle imaging velocimetry（PIV）system.The flow field pattern of an axial flow burner with a fixed swirl intensity is compared under confined and unconfined conditions,i.e.,with and without the combustor wall.The effect of temperature on the main swirling air flow is investigated under open and non-reacting conditions.The result shows that axial and radial velocities increase as a result of decreased flow density and increased flow volume.The flow field of the main swirling flow with liquid fuel spray injection is compared to non-spray swirling flow.Introduction of liquid fuel spray changes the swirl air flow field at the burner outlet,where the radial velocity components increase for both open and confined environment.Under reacting condition,the enclosure generates a corner recirculation zone that intensifies the strength of radial velocity.The reverse flow and corner recirculation zone assists in stabilizing the flame by preheating the reactants.The flow field data can be used as validation target for swirl combustion modelling.

A hybrid deep neural network based prediction of 300 MW coalfired boiler combustion operation condition

In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustion efficiency.However,it is difficult to establish an accurate prediction model based on traditional data-driven methods,which requires prior expert knowledge and a large number of labeled data.To overcome these limitations,a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed.The proposed hybrid model is a combination of convolutional sparse autoencoder(CSAE)and least support vector machine(LSSVM),i.e.,CSAE-LSSVM,where the convolutional sparse autoencoder with deep architectures is utilized to extract the essential features of flame image,and then essential features are input into the least support vector machine for operation condition prediction.A comprehensive investigation of optimal hyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM.The effectiveness of the proposed model is evaluated by 300 MW tangential coal-fired boiler flame images.The prediction accuracy of the proposed hybrid model reaches 98.06%,and its prediction time is 3.06 ms/image.It is observed that the proposed model could present a superior performance in comparison to other existing neural network models.