Image based temperature field reconstruction for combustion flame

Wang, Xuguang; Wang, Zhiheng; Cheng, Hui‐Ming

doi:10.1016/j.ijleo.2015.03.015

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Furthermore, the sampled video frames contain visual representations of various process data. For instance, Wang [42] utilized CCD radiation energy images to reconstruct the temperature distribution within the incineration system. Similarly, He et al [43] measured flame radiation spectra to acquire temperature and emission rates of the burning flames, and Xie et al [44] predicted calorific value by employing Yolov5 to identify waste types in images.…”

Section: Comprehensive Analysismentioning

confidence: 99%

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Pan,

Tang,

Xia

et al. 2023

Sustainability

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The prevailing method for handling municipal solid waste (MSW) is incineration, a critical process that demands safe, stable, and eco-conscious operation. While grate-typed furnaces offer operational flexibility, they often generate pollution during unstable operating conditions. Moreover, fluctuations in the physical and chemical characteristics of MSW contribute to variable combustion statuses, accelerating internal furnace wear and ash accumulation. Tackling the challenges of pollution, wear, and efficiency in the MSW incineration (MSWI) process necessitates the automatic online recognition of combustion status. This article introduces a novel online recognition method using deep forest classification (DFC) based on convolutional multi-layer feature fusion. The method entails several key steps: initial collection and analysis of flame image modeling data and construction of an offline model utilizing LeNet-5 and DFC. Here, LeNet-5 trains to extract deep features from flame images, while an adaptive selection fusion method on multi-layer features selects the most effective fused deep features. Subsequently, these fused deep features feed into DFC, constructing an offline recognition model for identifying combustion status. Finally, embedding this recognition system into an existing MSWI process data monitoring system enables online flame video recognition. Experimental results show remarkable accuracies: 93.80% and 95.08% for left and right grate furnace offline samples, respectively. When implemented in an online flame video recognition platform, it aptly meets recognition demands.

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Section: Comprehensive Analysismentioning

confidence: 99%

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Pan,

Tang,

Xia

et al. 2023

Sustainability

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“…However, some works have also focused on temperature reconstruction [4], [25]. In this work, we reconstruct both color and temperature using our method.…”

Section: Volume Reconstructionmentioning

confidence: 99%

“…In recent years, the physical nature of flame illumination has been utilized using the mathematical black-body radiation model to aid in flame temperature reconstruction [24], [25], [26]. Unfortunately, the black-body radiation model by itself cannot provide realistic visual flame results, which limits its application in the computer graphics domain.…”

Section: Flame Color and Temperature Measurementmentioning

confidence: 99%

Radiative Transport Based Flame Volume Reconstruction from Videos

Shen

Zhu

Nadeem

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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We introduce a novel approach for flame volume reconstruction from videos using inexpensive charge-coupled device (CCD) consumer cameras. The approach includes an economical data capture technique using inexpensive CCD cameras. Leveraging the smear feature of the CCD chip, we present a technique for synchronizing CCD cameras while capturing flame videos from different views. Our reconstruction is based on the radiative transport equation which enables complex phenomena such as emission, extinction, and scattering to be used in the rendering process. Both the color intensity and temperature reconstructions are implemented using the CUDA parallel computing framework, which provides real-time performance and allows visualization of reconstruction results after every iteration. We present the results of our approach using real captured data and physically-based simulated data. Finally, we also compare our approach against the other state-of-the-art flame volume reconstruction methods and demonstrate the efficacy and efficiency of our approach in four different applications: (1) rendering of reconstructed flames in virtual environments, (2) rendering of reconstructed flames in augmented reality, (3) flame stylization, and (4) reconstruction of other semitransparent phenomena.

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“…In addition, a novel solution to accurately reconstruct threedimensional temperature field for combustion flame was presented by Wang et al [18]. Also, a novel geometric calibration method for focused light field camera to trace the rays of flame radiance and the reconstruction of the three-dimensional (3-D) temperature distribution of a flame was proposed by Sun et al [19].…”

Section: Introductionmentioning

confidence: 99%

Flame imaging reconstruction method using high resolution spectral data of OH , CH and C 2 * radicals

Alviso

Mendieta

Molina

et al. 2017

International Journal of Thermal Sciences

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Image based temperature field reconstruction for combustion flame

Cited by 8 publications

References 23 publications

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Radiative Transport Based Flame Volume Reconstruction from Videos

Flame imaging reconstruction method using high resolution spectral data of OH , CH and C 2 * radicals

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Image based temperature field reconstruction for combustion flame

Cited by 8 publications

References 23 publications

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Radiative Transport Based Flame Volume Reconstruction from Videos

Flame imaging reconstruction method using high resolution spectral data of OH *, CH * and C 2 * radicals

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Product

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Flame imaging reconstruction method using high resolution spectral data of OH , CH and C 2 * radicals