Method for detecting a dioxin emission concentration of a municipal solid waste incineration process based on multi-level feature selection

A computer-implemented method for detecting a dioxin (DXN) emission concentration in a municipal solid waste incineration (MSWI) process based on multi-level feature selection, comprising the steps of:

• 1. dividing a grate furnace-based municipal solid waste incineration (MSWI) process into a plurality of sub-processes based on an incineration process;
• wherein the plurality of sub-processes comprise an incineration treatment sub-process, a boiler operation sub-process, a flue gas treatment sub-process, a steam electric power generation sub-process, a stack emission sub-process and a common resource supply sub-process;
• 2. obtaining a correlation coefficient value and a mutual information value between each of original input features of the sub-processes and the DXN emission concentration;
• and obtaining a comprehensive evaluation value of candidate input features according to the obtained correlation coefficient value and the obtained mutual information value, thereby completing the selection of first-level features of all of the sub-processes;
• 3. selecting and statistically processing the first-level features by adopting a feature selection algorithm based on genetic algorithm-based partial least squares (GAPLS) and according to redundancy between different features, thereby completing the selection of second-level features of all of the sub-processes;
• 4. performing a third-level feature selection according to the first-level features and statistical results of the second-level features within a preset threshold range, thereby completing the selection of third-level features of all of the sub-processes;
• and
• 5. establishing a partial least squares (PLS) algorithm-based DXN detection model according to model prediction performance and the third-level features;
• and detecting the DXN emission concentration by the obtained PLS algorithm-based DXN detection model,
• wherein the step of establishing the PLS algorithm-based DXN detection model comprises a microprocessor performing the steps of:
• increasing values of a third-level feature selection threshold θDXN3rd between θDXNdownlimit and θDXNuplimit one by one, so as to establish a plurality of first temporary PLS algorithm-based DXN detection models, wherein θDXNdownlimit is a lower limit of the threshold θDXN3rd, and θDXNuplimit is a upper limit of the threshold θDXN3rd;
• selecting a second temporary PLS algorithm-based DXN detection model from the plurality of first temporary PLS algorithm-based DXN detection models, wherein the second temporary PLS algorithm-based DXN detection model has a minimum value of root mean square error (RMSE);
• and
• checking the input features of the DXN emission concentration detection model to determine whether the input features comprise concentrations of CO, HCl, O2 and NOx emitted from a chimney, and removing features in the common resource supply sub-process;
• if the input features do not include concentrations of CO, HCl, O2 and NOx, additionally selecting the third-level features to obtain selected three-level features X3rdsel, thereby varying a number of features that are selected and establishing the PLS algorithm-based DXN detection model.