Simultaneous measurement of flame temperature and absorption coefficient through LMBC-NNLS and plenoptic imaging techniques

It is important to identify boundary constraints in the inverse algorithm for the reconstruction of flame temperature because a negative temperature can be reconstructed with improper boundary constraints. In this study, a hybrid algorithm, a combination of Levenberg-Marquardt with boundary constraint (LMBC) and non-negative least squares (NNLS), was proposed to reconstruct the flame temperature and absorption coefficient simultaneously by sampling the multi-wavelength flame radiation with a colored plenoptic camera. To validate the proposed algorithm, numerical simulations were carried out for both the symmetric and asymmetric distributions of the flame temperature and absorption coefficient. The plenoptic flame images were modeled to investigate the characteristics of flame radiation sampling. Different Gaussian noises were added into the radiation samplings to investigate the noise effects on the reconstruction accuracy. Simulation results showed that the relative errors of the reconstructed temperature and absorption coefficient are less than 10, indicating that accurate and reliable reconstruction can be obtained by the proposed algorithm. The algorithm was further verified by experimental studies, where the reconstructed results were compared with the thermocouple measurements. The simulation and experimental results demonstrated that the proposed algorithm is effective for the simultaneous reconstruction of the flame temperature and absorption coefficient

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

Temperature reconstruction and acoustic time of flight determination for boiler furnace exit temperature measurement

The furnace exit gas temperature (FEGT) is one of the fundamental parameters necessary to determine the energy balance of the boiler in a coal-fired power plant, and is thus beneficial to the production of reliable thermo-fluid models of its operation and the operation of the systems down and upstream. The continuous measurement of the FEGT would also be a useful indicator to predict, prevent and diagnose faults, optimize boiler operation and aid the design of heat transfer surfaces. Acoustic pyrometry, a technique that measures temperature based on the travel time of an acoustic wave in a gas, is investigated as a viable solution for continuous direct measurement of the FEGT. This study focuses specifically on using acoustic pyrometry to reconstruct the temperature profile at the furnace exit including methods for accurately determining the time of flight (TOF) of acoustic waves. An improved reconstruction technique using radial basis functions (RBF) for interpolation and a least squares algorithm is simulated and its performance was compared to cubic spline interpolation, regression and Lagrange interpolation by evaluating its reconstruction accuracy in terms of mean and root-mean-squared (RMS) error when reconstructing set temperature profiles. Various parameters including transceiver positions, grid divisions and time of flight error, are investigated in terms of how they inform acoustic pyrometry implementation. The improved RBF interpolation function managed to reconstruct complex temperature profiles and had a greater reconstruction accuracy than compared interpolation methods, improving on the accuracy of previous work done. Random acoustic path error was found to not be additive with reconstruction error however repeating acoustic TOF readings improved reconstruction accuracy to mitigate this effect. In general, it was also found that symmetrical transmitter/receiver positions produced more accurate reconstructions as well as positioning receivers/transceivers and grid lines closer to the furnace walls, where the greatest temperature change occurs. In addition to testing reconstruction methods, a low-cost experimental set-up was constructed to measure the time of flight. The focus of this study was on using various signal processing methods to determine the time of flight and evaluating their accuracy in the presence of noise. Methods such as threshold detection with bandpass filtering, cross correlation, generalized cross-correlation (GCC) and a new method developed employing variable notch filters with locations and widths based on repetitive frequencies identified in the noise with cross correlation. The performance of methods was experimentally tested under varying signal to noise ratios (SNR) and noise conditions. These SNR tests showed that cross-correlation methods produced more reliable TOF readings under lower SNRs than threshold detection methods. Under white noise the smooth coherent transform (SCOT) GCC variation proved to produce the most accurate results producing an average TOF error of 0.84 % up until a SNR of 1.4 before reducing in accuracy. In coloured noise (generated based on previous boiler recordings) the variable notch filter method with crosscorrelation was able to identify repetitive noise frequencies filter them out and ultimately produced results with an average TOF error of 1.99 % up until a SNR of 0.67, where the noise level exceeds that of the signal

Optical Fiber Imaging Based Tomographic Reconstruction of Burner Flames

This paper presents the design, implementation, and evaluation of an optical fiber imaging based tomographic system for the 3-D visualization and characterization of a burner flame. Eight imaging fiber bundles coupled with two RGB chargecoupled device cameras are used to acquire flame images simultaneously from eight different directions around the burner. The fiber bundle has 30k picture elements and an objective lens with a 92? angle of view. The characteristic evaluation of the imaging fiber bundles and the calibration of the system were conducted to ensure the accuracy of the system. A new tomographic algorithm that combines the logical filtered back-projection and the simultaneous algebraic reconstruction technique is proposed to reconstruct the flame sections from the images. A direct comparison between the proposed algorithm and other tomographic approaches is conducted through computer simulation for different test templates and numbers of projections. The 3-D reconstruction of the cross- and longitudinal-sections of a burner flame from image projections obtained from the imaging system was also performed. The effectiveness of the imaging system and computer algorithm is assessed through experimental tests

Tomographic imaging of combustion zones using tunable diode laser absorption spectroscopy (TDLAS)

This work concentrates on enabling the usage of a specific variant of tunable diode laser absorption spectroscopy (abbr. TDLAS) for tomogaphically reconstructing spatially varying temperature and concentrations of gases with as few reconstruction artifacts as possible. The specific variant of TDLAS used here is known as wavelength modulation with second harmonic detection (abbr. WMS-2f) which uses the wavelength dependent absorbance information of two different spectroscopic transitions to determine temperature and concentration values. Traditionally, WMS-2f has generally been applied to domains where temperature although unknown, was spatially largely invariant while concentration was constant and known to a reasonable approximation (_x0006_+/- 10% ). In case of unknown temperatures and concentrations with large variations in space such techniques do not hold good since TDLAS is a “line-of-sight” (LOS) technique. To alleviate this problem, computer tomographic methods were developed and used to convert LOS projection data measured using WMS-2f TDLAS into spatially resolved local measurements. These locally reconstructed measurements have been used to determine temperature and concentration of points inside the flame following a new temperature and concentration determination strategy for WMS-2f that was also developed for this work. Specifically, the vibrational transitions (in the 1.39 microns to 1.44 microns range) of water vapor (H2O) in an axi-symmetric laminar flame issuing from a standard flat flame burner (McKenna burner) was probed using telecom grade diode lasers. The temperature and concentration of water vapor inside this flame was reconstructed using axi-symmetric Abel de-convolution method. The two different sources of errors in Abel’s deconvolution - regularization errors and perturbation errors, were analyzed and strategies for their mitigation were discussed. Numerical studies also revealed the existence of a third kind of error - tomographic TDLAS artifact. For 2D tomography, studies showing the required number of views, number of rays per view, orientation of the view and the best possible algorithm were conducted. Finally, data from 1D tomography was extrapolated to 2D and reconstructions were benchmarked with the results of 1D tomography

Computed Tomography of Chemiluminescence: A 3D Time Resolved Sensor for Turbulent Combustion

Time resolved 3D measurements of turbulent flames are required to further understanding of combustion and support advanced simulation techniques (LES). Computed Tomography of Chemiluminescence (CTC) allows a flame’s 3D chemiluminescence profile to be obtained by inverting a series of integral measurements. CTC provides the instantaneous 3D flame structure, and can also measure: excited species concentrations, equivalence ratio, heat release rate, and possibly strain rate. High resolutions require simultaneous measurements from many view points, and the cost of multiple sensors has traditionally limited spatial resolutions. However, recent improvements in commodity cameras makes a high resolution CTC sensor possible and is investigated in this work. Using realistic LES Phantoms (known fields), the CT algorithm (ART) is shown to produce low error reconstructions even from limited noisy datasets. Error from selfabsorption is also tested using LES Phantoms and a modification to ART that successfully corrects this error is presented. A proof-of-concept experiment using 48 non-simultaneous views is performed and successfully resolves a Matrix Burner flame to 0.01% of the domain width (D). ART is also extended to 3D (without stacking) to allow 3D camera locations and optical effects to be considered. An optical integral geometry (weighted double-cone) is presented that corrects for limited depth-of-field, and (even with poorly estimated camera parameters) reconstructs the Matrix Burner as well as the standard geometry. CTC is implemented using five PicSight P32M cameras and mirrors to provide 10 simultaneous views. Measurements of the Matrix Burner and a Turbulent Opposed Jet achieve exposure times as low as 62 μs, with even shorter exposures possible. With only 10 views the spatial resolution of the reconstructions is low. However, a cosine Phantom study shows that 20–40 viewing angles are necessary to achieve high resolutions (0.01– 0.04D). With 40 P32M cameras costing £40000, future CTC implementations can achieve high spatial and temporal resolutions