Dioxin Emission Concentration Soft Measurement Model of MSWI Process Based on Unmarked Samples and Improved Deep Belief Network

Municipal solid waste incineration (MSWI) is a widely used domestic waste resource treatment technology. However, this process products pollution dioxins (DXN). It has high toxicity and durability. This is one of the main reasons of "not in my backyard" effect when constructing MSWI plant. As the long period and high cost of off-line DXN detection method, it is difficult to realize the real-time monitoring of DXN emission concentration. The massive unlabeled samples in the industrial field contain the generation mechanism of DXN, which are not fully utilized. Aim at the above problems, this paper proposes an unmarked samples and improved deep belief network (DBN) based method to construct DXN soft measurement model. Firstly, a large number of unlabeled samples are added to the training input sample set to improve the learning ability of soft measurement model at pre training phase. Then, an energy function is derived as the activation function of restricted Boltzmann machine (RBM). Finally, for the whole DBN, dropout algorithm is used to improve the robustness of the model, and the adaptive learning rate error back propagation algorithm is used to fine tune the weight iteratively at the fine tuning phase. The validity and rationality of this method are validated by DXN data set.

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