Boiler NOX prediction model optimization method based on an improved quantum particle swarm algorithm

A technology of quantum particle swarm and prediction model, which is applied in computing models, predictions, biological models, etc., can solve the problems of local optimization of quantum particle swarm algorithm and difficulties in establishing emission models, and achieve effective prediction and high prediction accuracy

Inactive Publication Date: 2019-03-19
DATANG ENVIRONMENT IND GRP
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Problems solved by technology

For thermal power plant boiler NO x Difficulty in setting up the emission model and the problem that the quantum particle swarm algorithm is easy to fall into local optimum in the later stage of the search. The present invention combines the improved QPSO algorithm (COSQPSO) based on the cosine decreasing function

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  • Boiler NOX prediction model optimization method based on an improved quantum particle swarm algorithm
  • Boiler NOX prediction model optimization method based on an improved quantum particle swarm algorithm
  • Boiler NOX prediction model optimization method based on an improved quantum particle swarm algorithm

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Embodiment Construction

[0028] The specific embodiments of the present invention will be further described below in conjunction with the drawings.

[0029] Step 1: Take a 600MW supercritical unit boiler in a thermal power plant as the research object, and the data is sampled from the power plant’s DCS historical database. According to the analysis of NOx generation mechanism, choose to affect NO x The operating parameters of the emission characteristics are used as the input of the model. Through mechanism analysis and actual conditions, the boiler load (WM), total air volume (t h-1), coal feed amount of coal mill A (t h-1), coal feed amount of coal mill B (t h-1), coal feed quantity of coal mill C (t·h-1), coal feed quantity of coal mill D (t·h-1), coal feed quantity of coal mill E (t·h-1), Coal feed rate of coal mill F (t·h-1), secondary air flow on both sides (t·h-1), opening of two over-fire air baffles (%), primary air flow of six coal mills ( t·h-1), six secondary baffle openings (%) are used as...

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Abstract

The invention discloses a power plant boiler NOx prediction model optimization method based on an improved quantum particle swarm algorithm, and the method comprises the following steps: 1, carrying out the mechanism analysis of a boiler combustion system of a coal-fired unit, and determining the input variable of a NOx emission concentration prediction model; 2, combining a cosine decreasing function with a quantum particle swarm optimization algorithm, and providing an improved quantum particle swarm optimization algorithm; and 3, optimizing initial parameters of the extreme learning machineby utilizing an improved quantum particle swarm optimization algorithm. Establishing an accurate NOx emission model by taking the error absolute value sum minimization of a training data prediction value and an actual value as a target; and 4, through simulation verification, the precision of the model optimized by the improved quantum particle swarm algorithm is higher than that of the model optimized by other methods. The method has the advantages that the optimal initial parameters of the extreme learning machine can be efficiently and rapidly calculated through the improved quantum particle swarm optimization algorithm, then the accurate thermal power plant boiler NOx emission model is obtained, and the method is of great significance for reducing pollutant emission of a coal-fired unit.

Description

Technical field [0001] The invention belongs to the technical field of NOx emission prediction for power plant boilers, and particularly relates to a method for optimizing a NOx prediction model for thermal power plant boilers based on improved quantum particle swarms. Background technique [0002] Coal is one of my country's main energy sources, accounting for about 70% of primary energy production and consumption. This coal-based energy structure determines that coal-fired thermal power generation has a dominant position in my country's electricity production. According to statistics released by the China Electricity Council, the thermal power generation in 2012 accounted for 78% of the total power generation. It can be seen that thermal power generation is still the main mode of electricity production in my country. NO produced by fuel combustion in coal-fired power plants x It is one of the main harmful substances of air pollution. Build an accurate NO x The emission predic...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/00
CPCG06N3/006G06Q10/04G06Q50/06
Inventor 孟磊马宁谷小兵李广林李婷彦马务宁翔张妍王旭光
Owner DATANG ENVIRONMENT IND GRP
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