Fly ash carbon content prediction method, device and apparatus and readable storage medium

Abstract

The invention discloses a fly ash carbon content prediction method, device and apparatus and a computer readable storage medium, and the method comprises the steps: determining an influence parameterof fly ash carbon content, and building a long-term and short-term memory network model according to historical operation data corresponding to the influence parameter and a corresponding historical fly ash carbon content value; obtaining operation data corresponding to the influence parameters at the current moment; and obtaining the fly ash carbon content value at the current moment according tothe operation data and the long-term and short-term memory network model. According to the technical scheme, the long-term and short-term memory network model is a time recurrent neural network. Historical information can be used for helping a current decision, so that the dynamic prediction of the carbon content of the fly ash can be realized through the established long-term and short-term memory network model and the operation data of the influence parameters at the current moment, and the accuracy of the prediction of the carbon content of the fly ash can be improved.

Description

technical field [0001] The present application relates to the technical field of thermal power generation, and more specifically, to a method, device, equipment, and computer-readable storage medium for predicting the carbon content of fly ash. Background technique [0002] With the increasingly prominent environmental and energy issues, people have increasingly stringent requirements for green energy, energy conservation and emission reduction. How to improve the utilization rate of coal energy and reduce pollution is the key to sustainable development. As one of the main indicators of the economic operation of thermal power plants, the carbon content of fly ash plays a key guiding role in improving power generation efficiency and reducing production costs. [0003] At present, BP neural network, support vector machine and other methods are often used to establish fly ash carbon content models, and use these models to realize fly ash carbon content prediction, but this type...

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Problems solved by technology

[0003] At present, BP neural network, support vector machine and other methods are often used to establish fly ash carbon content models, and use these models to realize fly ash carbon content prediction, but this type of model is to use a certain moment of operation to predict the fly ash carbon content. The carbon content of fly ash is the result of a period of operation. Therefore, the above method is difficult to meet the dynamic prediction of carbon content of fly ash, which will reduce the accuracy of carbon content prediction of fly ash

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