SCR denitration system prediction model optimization method based on machine learning

Abstract

The invention provides an SCR denitration system prediction model optimization method based on machine learning. The SCR denitration system prediction model optimization method comprises the followingsteps: S1, collecting the NOx concentration of a boiler outlet in an SCR denitration system and real-time sample data of related indexes influencing the NOx concentration; s2, carrying out dimensionreduction processing by utilizing principal component analysis; s3, establishing a support vector machine model; s4, introducing an exponential decay model to iteratively update the step size value ofthe longicorn beard algorithm, and optimizing vector machine parameters; s5, performing simulation of a support vector machine; and S6, repeating the step S1S5. The invention provides an SCR denitration system prediction model optimization method based on machine learning, which solves the problem that accurate control of ammonia injection quantity is difficult to realize in the existing thermalpower plant; and the invention comprises performing dimension reduction processing on sample data based on a PCA method, iteratively updating a step size value by introducing an exponential decay model, and optimizing by improving a BAS algorithm to obtain optimal support vector machine model parameters, and establishing an optimized support vector machine regression (SVM) model.

Description

technical field [0001] The invention is a machine learning-based SCR denitrification system prediction model optimization method, which relates to the fields of geotechnical engineering and tunnel engineering, and specifically relates to the fields of real-time monitoring and forecasting of foundation pit excavation deformation and stability analysis. Background technique [0002] More than half of my country's nitrogen oxide (NOx) emissions come from coal combustion, and one of the key industries for NOx pollution control is the thermal power industry. Selective Catalytic Reduction technology (Selective Catalytic Reduction, SCR) is under the condition of a catalyst, select the flue downstream of the boiler with a flue temperature of 300 ~ 4000 ℃, inject the reducing agent into the NOx reaction in the flue gas, and reduce the NOx , reduced to pollution-free N2 and H20. The SCR denitrification system has become an important equipment for large thermal power units to achieve ...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a machine learning-based SCR denitrification system prediction model optimization method to solve the problem that it is difficult to realize the precise control of ammonia injection in existing thermal power plants. The present invention is based on the principal component

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