Diagnostic techniques for the monitoring and control of practical flames

In this paper, the possibility of visualization of three-dimensional (3-D) temperature distributions in large-scale boiler furnaces from radiative energy images captured by multiple charge-coupled device (CCD) cameras mounted around the furnace is studied numerically. For the calculation of the radiative energy image formation, a fast algorithm proposed by the authors for pinhole imaging is used in this paper, which is based on the Monte Carlo method and combined with a concept of angular factor effective for image formation. This algorithm is applicable for the emitting, absorbing, and isotropic scattering medium. For the inversion of the 3-D temperature distributions which is an ill-posed problem, a modified Tikhonov regularization method is improved, where the finite difference regularizer is defined and can be used in 3-D cases, and the optimal regularization parameter is suggested to be selected by using a post-treatment method. For a 3-D unimodal temperature distribution, the numerical simulation results show that the reconstruction errors for the 3-D temperature distribution can be maintained at levels similar to the measurement error and the visualization quality of the temperature distribution is satisfactory. For a kind of bimodal temperature distribution, the reconstruction errors are higher than those for the unimodal distribution, but the bimodal feature of the temperature distribution can also be reproduced clearly.

Visualization of three-dimensional temperature distributions in a large-scale furnace via regularized reconstruction from radiative energy images: numerical studies

Flame imaging as a diagnostic tool for industrial combustion

An on-line digital imaging system is developed for monitoring flames in an industrial boiler system. The information extracted from the RGB flame images is used to predict the performance of the boiler system. A practical method based on multivariate image analysis techniques and partial least squares is developed to efficiently extract information from the rapidly time varying flame images, and to predict boiler performance, NOx and SO2 concentration in the off-gas, and the energy content of an incoming waste fuel stream. The approach is very general and can be applied to a wide range of combustion processes. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1474–1483, 2004

Monitoring flames in an industrial boiler using multivariate image analysis

Due to the complicated processes for coal particles burning in industrial furnaces, their radiative properties, such as the absorption and scattering coefficients, which are essential to make reliable calculation of radiative transfer in combustion computation, are hard to be given exactly by the existing methods. In this paper, multiple color image detectors were used to capture approximately red, green, and blue monochromatic radiative intensity images in the visible wavelength region, and the flame emissivity and the radiative properties of the particulate media in three pulverized-coal-fired boiler furnaces were got from the flame images. It was shown that as the load increased, the flame emissivity and the radiative properties increased too; these radiative parameters had the largest values near the burner zone, and decreased along the combustion process. Compared with the combustion medium with a low-volatile anthracite coal burning in a 670 t/h boiler, the emissivity and the absorption coefficient of the medium with a high-volatile bituminous coal burning in a 1025 t/h boiler were smaller near the outlet zone, but were larger near the burner zone of the furnace, due to the significant contribution of soot to the radiation. This work will be of practical importance in modeling and calculating the radiative heat transfer in combustion processes, and improving the technology for in situ, multi-dimensional visualization of large-scale combustion processes in coal-fired furnaces of power plants.

Measurements of the flame emissivity and radiative properties of particulate medium in pulverized-coal-fired boiler furnaces by image processing of visible radiation

A method for predicting unburnt carbon in a coal-fired utility boiler has been developed using an image processing technique. The method consists of an image processing unit and a furnace model unit. The temperature distribution of combustion flames can be obtained through the former unit. The latter calculates dynamics of the carbon reduction from the burner stages to the furnace outlet using coal feed rate, air flow rate, and the chemical and ash content of the coal. An experimental study in the Sendai Power Station of Tohoku Electric Company Inc. shows that the prediction error of the unburnt carbon can be reduced to 10%. The results show that the unburnt carbon prediction algorithm could be widely used for evaluation, optimization and diagnosis of combustion systems for various boiler and coal types. >

K. Ishiyama

Papers

Prediction method of unburnt carbon for coal fired utility boiler using image processing technique of combustion flame