Abstract: The provided is a Soft-sensing method for dioxin emissions of MSWI process based on ensemble T-S fuzzy regression tree. The highly toxic pollutant dioxins (DXN) generated in the municipal solid waste incineration (MSWI) process based on a grate furnace is a key environment index for realizing operation optimization control of the process. The method comprises the following steps: firstly, constructing a dioxin emission TSFRT model based on a screening layer and a fuzzy reasoning layer; then, a plurality of parameter updating learning algorithms aiming at the fuzzy reasoning antecedent part and the fuzzy reasoning consequent part are provided, and five dioxin emission TSFRT models including TSFRT-I, TSFRT-II, TSFRT-III, TSFRT-IV and TSFRT-V are obtained; finally, by taking the dioxin emission TSFRT-III model as an example, constructing an integrated TSFRT (EnTSFRT) model taking the TSFRT-III as a base learner so as to realize high-precision modeling of the dioxin emission concentration.

Abstract: The invention provides a soft measurement method for dioxin emission of grate furnace MSWI process based on simplified deep forest regression of residual fitting mechanism. The highly toxic pollutant dioxin (DXN) generated in the solid waste incineration process is a key environmental index which must be subjected to control. The rapid and accurate soft measurement of the DXN emission concentration is an urgent affair for reducing the emission control of the pollutants. The method comprises the following steps: firstly, carrying out feature selection on a high-dimensional process variable by adopting mutual information and significance test; then, constructing a simplified deep forest regression (SDFR) algorithm to learn a nonlinear relationship between the selected process variable and the DXN emission concentration; and finally, designing a gradient enhancement strategy based on a residual error fitting (REF) mechanism to improve the generalization performance of a layer-by-layer learning process.

Abstract: A broad hybrid forest regression (BHFR)-based soft sensor method for DXN emission in a municipal solid waste incineration (MSWI) process, including: based on a broad learning system (BLS) framework, constructing a BHFR soft sensor model for small sample high-dimensional data by replacing a neuron with a non-differential base learner, where the BHFR soft sensor model includes a feature mapping layer, a latent feature extraction layer, a feature incremental layer and an incremental learning layer, and the method includes: mapping a high-dimensional feature; extracting a latent feature from a feature space of a fully connected hybrid matrix, and reducing model complexity and computation consumption based on an information measurement criterion; enhancing a feature representation capacity by training the feature incremental layer based on an extracted latent feature; and constructing the incremental learning layer based on an incremental learning strategy, obtaining a weight matrix with a Moore-Penrose pseudo-inv