Abstract
It is important to accurately identify the combustion state of the municipal solid waste incineration (MSWI) processes. Stable state not only can greatly improve the combustion efficiency, but also can ensure safety of the MSWI processes. What’s more, the pollution emission concentration would be greatly reduced. Aiming at the situation that domain experts identify the combustion state in terms of self-experience in the actual MSWI processes, this study proposes an efficient method based on improved deep forest (IDF). First, the image preprocessing methods such as defogging and denoising, were used to preprocess the combustion flame image to obtain a clear one. Then, the multi-source features (brightness, flame and color) were extracted. Finally, the multi-source features were used as the input of cascade forest module in terms of substituting multi-grained scanning module. Therefore, a combustion state recognition model of MSWI processes based on IDF was established. Based on actual flame images of industrial processes, many experiments has been done. The results showed that the constructed model can reach a recognition accuracy of 95.28%.
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Acknowledgments
The work was supported by Beijing Natural Science Foundation (No. 4212032), National Natural Science Foundation of China (No. 62073006).
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Pan, X., Tang, J., Xia, H., Li, W., Guo, H. (2022). Combustion State Recognition Method in Municipal Solid Waste Incineration Processes Based on Improved Deep Forest. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_6
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DOI: https://doi.org/10.1007/978-981-19-6142-7_6
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