Biomass boiler furnace temperature and load forecasting method

A biomass boiler and furnace temperature technology, applied in combustion methods, lighting and heating equipment, combustion chambers, etc., can solve the problems of inaccurate prediction of biomass boiler furnace temperature and load, insufficient data utilization, and huge data volume. To achieve the effect of ensuring real-time and originality, ensuring real-time and rapidity, and eliminating the influence of correlation

Active Publication Date: 2020-04-14
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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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

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  • Biomass boiler furnace temperature and load forecasting method

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Embodiment Construction

[0019] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0020] Aiming at the existing problems, the present invention provides a method for predicting the furnace temperature and load of a biomass boiler. Based on the principal component analysis method and bipolar neural network, the temperature and load prediction of the boiler furnace can be predicted more quickly and accurately, and at the same time, the amount of input data and the load can be reduced. Calculations.

[0021] In order to realize the above technical solutions, such as figure 1 The shown embodiment is a method for predicting the furnace temperature and load of a biomass boiler, and the basic steps include: .

[0022] S1. Select the operating parameters directly related to the furnace temperature for preprocessing, and save it as an indepen...

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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 ...

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Application Information

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IPC IPC(8): F23N5/00F23M5/08
CPCF23M5/08F23N5/00
Inventor 张俊姣安梦迪董长青胡笑颖王孝强覃吴赵莹薛俊杰
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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