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Identification of biomass co-combustion operating point using image processing

机译:利用图像处理识别生物质共燃工作点

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Thermal power and excess air coefficient are one of key parameters that characterize operating point of combustion process. In practice, they are hard to determine directly. The k-nearest neighbor (k-NN) regression algorithm was applied where some flame image geometric parameters were used as predictors. The model was assessed by carrying out several combustion tests for nine different settings of the laboratory combustion facility. Thermal power and excess air coefficient were kept constant and set independently for known biomass content.
机译:火力和过量空气系数是表征燃烧过程工作点的关键参数之一。实际上,很难直接确定它们。应用k最近邻(k-NN)回归算法,其中一些火焰图像几何参数用作预测变量。通过对实验室燃烧设施的九种不同设置进行几次燃烧测试来评估模型。对于已知的生物质含量,热功率和过量空气系数保持恒定并独立设置。

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