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Recognition of Combustion Condition in MSWI Process Based on Multi-scale Color Moment Features and Random Forest

The redundancy and complexity of combustion flame image features increase the difficulty of recognizing the combustion condition of municipal solid waste incineration (MSWI). The complexity of solid waste components and the inherent nonlinearity, time variation and uncertainty of MSWI process lead to the instability of flame image feature distribution. The traditional method based on fixed sliding window only extract fixed size features and cannot reflect global and local features, which lead to low recognition accuracy of combustion conditions. To address the issue, this paper proposes a model of combustion condition recognition in MSWI process based on multi-scale color moment features and random forest (RF). Firstly, the image is pre-processed by defogging and denoising. Then, the color moment features of different scales of the flame image is extracted via using sliding windows based on prior setting scales. Finally, taking the classification accuracy as the evaluation criterion, and the RF algorithm based on feature selection is adopted to realize accurate identification of combustion conditions. Based on the flame image data of an MSWI plant in Beijing, this method has been validated by the experimental results.