Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques

This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results

Flame stability detection method for co-firing of biomass fuels based on digital image processing

Combustion of low-quality fuels or fuel blends will lead to flame instability, resulting in low combustion efficiency and high NOx emissions. Due to the inherent complexity of burner flames and the lack of an effective means for flame monitoring and characterization, it is difficult to evaluate the flame stability in a combustion process quantitatively. To solve this problem, a method based on digital image processing for co-firing biomass fuels is proposed in this paper to monitor various characteristic parameters of a burner flame and evaluate its stability. In this method, a general flame stability index with continuous values in the range of [0, 1] is defined, and by using a digital CCD camera, the flame image information is collected. After the collected image is analyzed, the characteristic parameters like the flame length/height, brightness, temperature, flicker frequency and others are extracted. Then, statistical analysis and data fusion are carried out for theses characteristic parameters, and the flame stability index is obtained. Thus, the quantitative detection and evaluation of flame stability is realized. Moreover, this method was verified on a laboratory-scale combustion test rig. The combustion behaviours of different biomass blends(corncob-wheat straw blend, willow-peanut shell blend and peanut shell-wheat straw blend) were compared. The results show that, the defined flame stability index could effectively characterize the flame combustion state

Effects of hydrogen and primary air in a commercial partially-premixed atmospheric gas burner by means of optical and supervised machine learning techniques

In order to ascertain the effects of the hydrogen addition and the primary air-fuel ratio on burner performance and emissions, we conduct tests on a commercial atmospheric gas burner using pure methane and a blend of hydrogen/methane. Relevant statistical image features are extracted from a UV–VIS camera equipped with narrow-band optical filters. Radical image results agrees with spectrometric data, showing the relevance of the OH* intensity radiation coming from the outer non-premixed zone. The double-cone flame structure is evident, showing a growing secondary non-premixed cone as the primary air-fuel ratio is decreased. In addition, the direct relationship found between flame radical imaging features and NOx emissions has been used to develop a predictive model by integrating classification techniques and neural networks. The research confirms UV–VIS chemiluminescence imaging techniques as powerful tools aimed at combustion monitoring, with huge prospects of being integrated within advanced emission control techniques for commercial burners

Profiling and characterization of flame radicals by combining spectroscopic imaging and neural network techniques

This paper presents the development of an instrumentation system for visualizing and characterizing free radicals in combustion flames. The system combines optical splitting, filtering, intensified imaging and image processing techniques for simultaneous and continuous monitoring of specific flame radicals ( OH*, CN*, CH*, and C2*). Computing algorithms are developed to analyze the images and quantify the radiative characteristics of the radicals. Experimental results are obtained from a gas-fired combustion rig to demonstrate the effectiveness of the system. The information obtained by the system is used to establish relationships between radical characteristics and air-to-fuel ratios of combustion gases, helping to obtain an in-depth understanding of burn characteristics

Advanced Flame Monitoring and Emission Prediction through Digital Imaging and Spectrometry

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for burner condition monitoring and NOx emissions prediction on fossil-fuel-fired furnaces. A review of methodologies and technologies for burner condition monitoring and NOx emissions prediction is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating digital imaging, UV-visible spectrum analysis and soft computing techniques, is proposed. Based on these techniques, a prototype flame imaging system is developed. The system consists mainly of an optical and fibre probe protected by water-air cooling jacket, a digital camera, a miniature spectrometer and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and spectral signals. Luminous and geometric parameters, temperature and oscillation frequency are collected through imaging, while flame radical information is collected by the spectrometer. These parameters are then used to construct a neural network model for the burner condition monitoring and NOx emission prediction. Extensive experimental work was conducted on a 120 MWth gas-fired heat recovery boiler to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 40 MWth coal-fired combustion test facility to investigate the production of NOx emissions and the burner performance. The results obtained demonstrate that an Artificial Neural Network using the above inputs has produced relative errors of around 3%, and maximum relative errors of 8% under real industrial conditions, even when predicting flame data from test conditions not disclosed to the network during the training procedure. This demonstrates that this off the shelf hardware with machine learning can be used as an online prediction method for NOx

Flame stability and burner condition monitoring through optical sensing and digital imaging

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for flame stability and burner condition monitoring on fossil-fuel-fired furnaces. A review of methodologies and technologies for the monitoring of flame stability and burner condition is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating optical sensing, digital imaging, digital signal/image processing and soft computing techniques, is proposed. Based on this strategy, a prototype flame imaging system is developed. The system consists of a rigid optical probe, an optical-bearn-splitting unit, an embedded photodetector and signal-processing board, a digital camera, and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and radiation signals. Power spectral density, oscillation frequency, and a proposed universal flame stability index are used for the assessment of flame stability. Kernel-based soft computing techniques are employed for burner condition monitoring. Specifically, kernel principal components analysis is used for the detection of abnormal conditions in a combustion process, whilst support vector machines are used for the prediction of NO x emission and the identification of flame state. Extensive experimental work was conducted on a 9MW th heavy-oil-fired combustion test facility to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 660MWth heavy-oil-fired boiler to investigate the cause of the boiler vibration from a flame stability point of view. Results Obtained from the tests are presented and discussed