Multi-coal type blending combustion optimization method of coal-fired unit

Abstract

The invention discloses a multi-coal type blending combustion optimization method of a coal-fired unit. The method includes the following steps that: a coal blending combustion database is built according to a fuzzy control principle; a comprehensive cost per kilowatt hour benchmark library is generated; and an optimal mixed coal combustion scheme is searched according to different set work conditions. With the method adopted, the coal combustion cost of a power plant can be decreased.

Description

technical field [0001] The invention relates to the field of coal power technology, in particular to a multi-coal blending coal blending combustion optimization method for a coal-fired unit. Background technique [0002] At present, the selection of boilers in thermal power plants has a corresponding design coal type. When the coal quality exceeds the adaptable range of the boiler, it will have a great impact on the safety and economy of the boiler. In recent years, especially in southern inland power plants, the designed coal type cannot meet the demand, and the coal quality of the coal source is unstable, which has led to problems such as coking in the boiler furnace, over-temperature, tube burst, fire extinguishing, output drop, and boiler efficiency drop. At present, all thermal power plants have generally adopted the coal blending combustion technology. By mixing different coal types in a certain proportion according to certain principles before the pulverized coal ente...

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Problems solved by technology

The former needs to stabilize specific working conditions and coal quality. The test conditions are harsh and the sample size is relatively small, which makes it difficult to truly reflect the operating conditions of the equipment and the operating level of personnel.

The latter lacks data support, especially the separation of blending and consumption in the medium-storage pulverizing system, with many intermediate links, complex coal quality, large change delay, and large change in working conditions. Due to the influence of manual calculation on the roughness of data processing, the obtained The coal blending and blending scheme is not precise enough, and the applied coal blending and blending method is simple. At the same time, it is impossible to establish a huge database to provide optimization guidance for coal blending and blending work only by relying on brain power; The blending scheme corresponding to the lowest coal consumption and the lowest power supply cost makes it impossible to effectively use a large amount of historically generated data that can be used as a guide, and the cost of coal-fired power supply cannot be effectively reduced

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