Improved BP neural network-based coal pyrolysis product prediction method

A BP neural network and prediction method technology, which is applied in the prediction field of coal pyrolysis products, can solve the problems of complex application of three types of models, and achieve the effect of promoting efficient learning and rapid convergence, strong pertinence, and rapid approximation of target errors.

Inactive Publication Date: 2018-06-29
NORTHWEST UNIV
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Problems solved by technology

Although the above three models want to predict the pyrolysis products of coal on the basis of mechanism and coal structure, the structure of coal itself has not been really recognized by peop

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  • Improved BP neural network-based coal pyrolysis product prediction method
  • Improved BP neural network-based coal pyrolysis product prediction method
  • Improved BP neural network-based coal pyrolysis product prediction method

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[0046] The present invention will be described in further detail below with reference to the drawings and examples, but the present invention is not limited to the following embodiments.

[0047] exist figure 1 Among them, the method for predicting coal pyrolysis products based on the improved BP neural network of the present embodiment consists of the following steps:

[0048] (1) Select Datong coal, Fenxi coal, Huolinhe coal, Pingshuo coal, Shenhua coal, Xianfeng coal, Yilan coal, Yima coal, and Yanzhou coal. ℃, 500℃, 550℃, 600℃, 650℃, 700℃, the corresponding coal pyrolysis semi-coke yield, a total of 63 sets of data are used as learning samples; the Shenmu coal pyrolysis temperature is selected as 425℃, 525℃, At 625°C, the corresponding coal pyrolysis semi-coke yield, a total of 3 sets of data are used as prediction samples;

[0049] The pyrolysis temperature (T), volatile matter (V daf ), ash (A d ), the carbon-hydrogen ratio (C / H), and the oxygen-hydrogen ratio (O / H) ...

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Abstract

The invention discloses an improved BP neural network-based coal pyrolysis product prediction method. A coal pyrolysis product is accurately predicted by adopting industrial analysis and element analysis data and pyrolysis temperature of coal; an initial weight and a threshold of a BP neural network are optimized by applying a PSO algorithm and GA combination method; and an adaptive learning rateis embedded in a calculation process of the BP neural network. The stability of the BP neural network is improved through the PSO algorithm and the GA; and the BP neural network is quickly converged by adding the adaptive learning rate, so that the calculation efficiency and the data prediction precision of the BP neural network are improved. The method has the advantages of simple model, easy operation, high prediction precision, favorability for promotion in the present stage and the like.

Description

technical field [0001] The invention belongs to the technical field of digital calculation or data processing methods specially suitable for specific applications, and in particular relates to a coal pyrolysis product prediction method based on an improved BP neural network. Background technique [0002] In view of my country's "poor oil, low gas, relatively rich in coal" energy resource structure, my country's energy structure will still be dominated by coal for a long period of time in the future. It is estimated that by 2030, my country's coal consumption will still account for the primary energy consumption. More than 55% of the total consumption. In recent years, with the continuous proposing of the polygeneration process with coal pyrolysis as the source and the increasingly prominent environmental problems, the research on related technologies of coal pyrolysis has become a hot spot in this field, but the coal pyrolysis process is a very Complex physical and chemical ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/02G06N3/08
CPCG06N3/084G06Q10/04G06Q50/02
Inventor 谢良才闫雨瑗刘方宣乐徐龙马晓迅孙鸣
Owner NORTHWEST UNIV
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