Multivariate flame monitor-based on-line judgment method for fuel type

A technology of fuel type and identification method, applied in instruments, measuring devices, scientific instruments, etc., can solve problems such as large errors, improve combustion efficiency, enhance safety and economy, and avoid unstable flame combustion in the furnace.

Inactive Publication Date: 2010-04-07
BEIHANG UNIV
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Problems solved by technology

However, this method only has a high discrimination rate when the feature distinction between fuels is large. When the features of the fuel are close to each other, that is, when the distinction between the fuel feature values ​​is small, the error of the method larger

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  • Multivariate flame monitor-based on-line judgment method for fuel type
  • Multivariate flame monitor-based on-line judgment method for fuel type
  • Multivariate flame monitor-based on-line judgment method for fuel type

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Embodiment Construction

[0018] In order to enable those skilled in the art to clearly understand the technical solution of the present invention, the specific implementation manners of the present invention are now further described.

[0019] Specific implementation method:

[0020] Such as figure 1 As shown, n (≥ 3) photoelectric sensors are used to obtain a radiation signal of a known fuel combustion flame in the radiation band, and M groups are collected to form a signal sample set {x(m, s)|m=1, 2 , . . . , M; s=1, 2, . . . , n}. Among them, M should be selected so that the signals in the collected sample set {x(m, s)} can cover various conditions of fuel combustion. For example, M=500 may be selected, that is, 500 sets of signals are collected for each fuel. Extract the eigenvalues ​​of the flame in the time domain and frequency domain {c(m, s, t)|m=1, 2,..., M; s=1, 2,..., n; t=1, 2 ,...,T} (such as flicker frequency, mean, root mean square, variance, number of zero crossings, skew rate, kur...

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Abstract

The invention discloses a multivariate flame monitor-based on-line judgment method for fuel type, which comprises the following steps: when a known fuel and a new fuel exist in burning, extracting characteristic values in a time domain and a frequency domain from flame radiation signals of the known fuel, and mathematically transforming the characteristic values to acquire orthogonal characteristic values; establishing a joint probability density model and a neural network model of characteristic value distribution of the known fuel based on the orthogonal characteristic values; extracting characteristic values in the time domain and the frequency domain from burning flame radiation signals of a fuel to be identified, mathematically transforming the characteristic values to acquire orthogonal characteristic values and inputting the orthogonal characteristic values into the joint probability density model of various known fuels to judge, and if the fuel to be identified in burning is the new fuel, storing the orthogonal characteristic values of the flame radiation of the new fuel to establish the joint probability density model of the new fuel and update the neural network model; and if the fuel to be identified in burning is not the new fuel, judging the type of the fuel through the neural network model.

Description

【Technical field】 [0001] The invention relates to an online identification method for combustion fuel types, belonging to the technical field of industrial boiler fuel identification. 【Background technique】 [0002] Due to the limitation of economic factors and other factors, industries usually need to burn different types of fuels, and the types of fuels are usually unknown and unpredictable during combustion. The change of fuel type makes the combustion more complicated, directly affects the stability of the combustion flame, makes it very difficult to detect and control the combustion state, and seriously affects the efficiency of combustion. Therefore, the change of fuel type affects the safety and economy of combustion operation. [0003] The neural network technology used in this patent is an artificial neural network, which is composed of a large number of nodes and interconnected with each other, and is a mathematical model for information processing with a connectio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/02G01N21/00
Inventor 徐立军谭丞李小路
Owner BEIHANG UNIV
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