Mixing and combustion in supersonic/hypersonic flows

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

“…The functions of each module in the offline modeling stage are as follows. Before inputting the flame-image dataset {I n } N n=1 into LeNet-5 [12], it needs to be preprocessed to meet the network input requirements. The preprocessing operation used here is to first adjust the size of the original color flame image I n to 32 × 32, and then perform grayscale processing.…”

“…Presently, the observation of waste incinerator combustion status primarily relies on visual assessments by experts. They combine visual observations with flame conditions from on-site observation holes to adjust key parameters, ensuring combustion stability [12]. However, this method faces several challenges: (1) the lack a unified judgment standard leads to inconsistent results, susceptible to subjective variations; (2) prolonged on-site image observation induces visual fatigue in workers, impacting their health; (3) multiple interrelated key regulatory parameters significantly impact combustion efficiency, making accurate individual control by operators extremely challenging, potentially causing unstable control processes.…”

“…Guo et al [16] presented a combustion status-recognition method employing mixed data augmentation and a deep convolutional generative adversarial network (DCGAN) to obtain flame images under diverse conditions. Huang et al [12] extracted key parameters like grayscale mean, flame area ratio, high-temperature ratio, and flame front to characterize and evaluate combustion status. Meanwhile, Zhang et al [17] extracted 19 feature vectors encompassing color, shape, and texture of flame images, constructing an echo state network recognition model.…”