Variable dimensionality reduction modeling method for boiler combustion optimization

Abstract

The invention belongs to the technical field of a heat engineering technology and an artificial intelligence crossing technology, and relates to a variable dimensionality reduction modeling method for boiler combustion optimization. According to the method, DVs (disturbance variables) and MVs (manipulated variables) are selected as auxiliary variables of a model, CVs (controlled variables) to be predicated are used as output of the model, historical operation data is selected as initial training samples, principal component analysis is utilized for carrying out feature extraction on the DVs of the model, the dimensionality reduction of input variables is realized, the extracted feature variables and the MVs are simultaneously used as the input of the model, and an LSSVM (least square support vector machine) is used for building a CV model of a boiler. The variable dimensionality reduction modeling method has the advantages that through the dimensionality reduction on the input variables, the predication precision and the generalization capability of the model can be effectively improved, the precise prediction on the CV can be realized, and the important significance is realized on the combustion optimization of a power station boiler.

Description

technical field [0001] The invention belongs to the cross technical field of thermal technology and artificial intelligence, and relates to a variable dimension reduction modeling method for boiler combustion optimization. Background technique [0002] Boiler combustion optimization is an important technical means for thermal power units to achieve energy saving and emission reduction, and it is of great significance to improve the economic benefits of power generation enterprises and the sustainable development of energy. Combustion optimization technology mainly optimizes the combustion conditions in the furnace by controlling various adjustment parameters related to combustion, so as to improve boiler efficiency and reduce pollutant emissions. Establishing the model relationship between economic indicators such as boiler efficiency and pollutant discharge and various parameters of the boiler is the basis for implementing combustion optimization. [0003] Due to the compl...

AI Technical Summary

Problems solved by technology

If all relevant variables are used as the input of the model, it will not only make the calculation complicated, but also easily cause the model to fall into overfitting, resulting in a decrease in the generalization ability of the model and a decrease in prediction accuracy.

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