Biomass boiler furnace temperature and load forecasting method

Abstract

An embodiment of the invention provides a biomass boiler furnace temperature and load forecasting method. According to the method, Pearson correlation coefficient is used to describe correlation between operating parameters and furnace temperature, and the correlation coefficient is used as the basis for data screening. Principal component analysis is performed on a screened data set, principal component parameters are used as input of a neural network, two-stage neural network forecasting models for furnace temperature and boiler load are established to forecast the boiler furnace temperature, the obtained furnace temperature, furnace outlet oxygen concentration, furnace pressure, induced draft fan outlet flue gas flow and other parameters are used as input of the next-stage neural network to forecast the boiler load. According to the method, the data volume and calculation amount are reduced by data screening and principal component analysis; not only are the first-stage neural network prediction data adopted, but also information of original data is directly used, and forecast results are faster and more accurate.

Description

technical field [0001] The invention belongs to the technical field of biomass power generation and big data analysis, in particular to a method for predicting the furnace temperature and load of a biomass boiler, which realizes the prediction of the temperature and load of the boiler furnace based on principal component analysis and a two-stage neural network. Background technique [0002] In order to ensure the safe and economical operation of biomass power plants, the level of real-time monitoring and control systems of biomass power plants has been continuously improved, generating and storing a large amount of operating data every day, analyzing massive historical data, discovering and utilizing the hidden information in the data, and effectively It is beneficial to further improve the operation and management level of biomass power plants. Biomass boilers are similar to coal-fired boilers. The operating parameters will show nonlinearity, large inertia, uncertainty and ...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a biomass boiler furnace for the problems of the existing biomass power plants with redundant data, large data volume, insufficient data utilization, and inaccurate prediction of the temperature and load of the biomass boiler furnace. The temperature and load prediction method, based on the principal component analysis method and bipolar neural network, makes the boiler furnace temperature and load prediction faster and more accurate, while reducing the amount of input data and calculation

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