Research On Biomass Combustion Monitoring Through Flame Spectral Analysis And Image Processing

In recent years,with the sustained and stable development of biomass power generation,the monitoring and control of combustion state of biomass boilers has attracted increasing attention.There are many kinds of biomass fuels available in China,and the deviation of fuel from boiler design is easy to cause coking and serious ash deposition,which directly affects production safety and economic benefits.In this thesis,research on the biomass combustion monitoring using flame spectrum analysis and image processing is conducted based on the in-depth analysis of the state-of-the-art in biomass combustion processes.The study focuses on the fuel identification and combustion stability analysis of blended biomass based on the combustion characteristics of a single type of biomass.The outcome of the results provides the basis for maintaining the continuous supply of various types of fuel and the stable economic operation of combustion in biomass power plants.The main achievement and contributions of the research are as follows:1)Aiming at the problem of biomass co-combustion,a biomass fuel identification method based on 6-dimensional characteristics of flame spectrum and ensemble learning is constructed.The method composed of the radiation intensities of five types of free radicals,such as OH*,CN*,CH*,C2*-1 and C2*-2,and the intensity of flame radiation signals,are selected based on the analysis of the time-domain and frequency-domain characteristics of the flame spectrum of biomass combustion.Combustion tests using our types of biomass(peanut shell,willow,wheat straw,corncob)and six different biomass blends with equal mass were conducted.Comparison analysis between the proposed 6-D method,SVM and decision trees were performed.Test results demonstrate that the proposed method with ensemble learning has a remarkable recognition rate of 99.32%,and 100%for pure biomasses and blended biomass,respectively.The proposed method not only simplifies the feature extraction process,but also integrates the advantages of support vector machines(SVM)and decision trees,therefore,has better generalization ability.2)A fuel identification method based on flame image and convolution neural network is constructed.To overcome the drawback of strong dependence on image feature extraction,which is crucial to traditional image-based fuel recognition methods,a biomass fuel recognition method using flame images and depth convolution neural network ResNet50 is proposed based on the analysis of the geometric,optical and thermodynamic characteristics of flame images of biomass combustion.The fuel recognition tests of pure biomass and blended biomass were carried out and the recognition results were compared with four traditional machine learning methods.The comparison results show that the accuracy of fuel recognition based on the proposed method excelled the traditional methods by 97.41%versus 85.83%at best.3)The combustion stability index based on flame image and data fusion is defined?A flame stability index is proposed to quantitatively characterize the combustion stability of biomass flame by fusing the height,average gray level,average temperature,maximum temperature and other characteristic parameters of biomass flames.Based on the stability index and flame flicker frequency,the stability of pure biomass and blended biomass fuel is analyzed.The analysis demonstrated that the proposed method effectively characterizes the combustion stability of a biomass flame.The stability index has the advantages of easy calculation,independent of external conditions such as burner structure,is suitable to use for online monitoring,and is guaranteed ranged within[0,1].4)An optimization scheme of biomass combustion monitoring and stability discrimination is constructed.An optimization scheme of biomass combustion monitoring is proposed based on biomass fuel identification and flame stability obtained by the 6-D method and the flame stability index.Comparative study on the effects of spectral analysis and image processing on biomass fuel identification and combustion stability was conducted based on the confusion matrix method.The classification of combustion stability and instability of different biomass is realized based on the multivariate statistical indexes T2 and SPE.Then,the real-time identification of fuel type and determination of combustion stability are realized based on the obtained threshold of stability index(according to the classification results).