Thermal power plant intelligent combustion control method based on cloud data and cloud computing

Abstract

The invention relates to a thermal power plant intelligent combustion control method based on cloud data and cloud computing, and the method comprises the steps of: firstly, constructing a cloud database, conducting abnormal value processing on input data and output data in the constructed cloud database, and then performing principal component analysis, training a heat exchange surface wall temperature prediction model, a denitration reactor inlet NOx concentration distribution prediction model and a boiler efficiency prediction model by adopting a principal component analysis result, and comparing a training result with data of a model calibration system, and if the root-mean-square error is within 15%, according to the heat exchange surface wall temperature prediction value, the NOx concentration prediction value and the boiler efficiency prediction value provided by an integrated prediction model, conducting calculation through an intelligent operation control module to obtain optimal operation parameters, thus realizing dynamic optimization control over boiler combustion and pollutant generation. The boiler efficiency and the adaptability to coal type changes are improved, the average emission load of NOx is reduced by 10% or above, and power generation benefits are maximized.

Description

technical field [0001] The invention belongs to the field of thermal automatic control, relates to an optimized operation technology of a boiler combustion system in a power plant, and in particular relates to a smart combustion control method for a thermal power plant based on cloud data and cloud computing. Background technique [0002] Thermal power plants generally use combustion adjustment tests to optimize operation. By carrying out combustion experiments under different loads, the boiler efficiency and NOx are taken as optimization targets, and the operating variables corresponding to the optimal combustion conditions are saved in the DCS setting module, so as to control the actual operation of the boiler. To regulate. However, changes in boundary conditions such as boiler load, coal type, and ambient temperature lead to a large difference between the actual operating conditions of the boiler and the experimental conditions, and the combustion adjustment test rules do...

AI Technical Summary

Problems solved by technology

[0004] Due to the complexity of the flow field and combustion chemical reactions in the boiler furnace, there are complex coupling relationships among many operating parameters, which brings great difficulties to the modeling and optimization of boiler combustion.

The boiler has a large time-varying characteristic. With the passage of time and changes in the operating conditions of the boiler, the learning model of the boiler combustion process will have large errors. The model established offline cannot adapt to this change, resulting in model mismatch.

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