Intelligent fuel model library capable of boosting efficient productivity

Abstract

The invention discloses an intelligent fuel model library for boosting efficient productivity, and belongs to the technical field of coal-fired power generation. The intelligent fuel model library is provided with a price difference benefit point measurement and calculation model and a purchase combination matched combustion benefit model, no matter how the market coal price changes and how the heat value of the coal to be purchased is predicted, the price difference benefit point of the coal to be purchased can be obtained only by inputting the related heat value and the unit price of the comprehensive tax-free standard coal to the measurement and calculation model, a purchase department can quickly and conveniently make a purchase decision, meanwhile, the original solidified thinking that only the price difference benefit point of a single coal type is considered is broken through, the final benefits after matching of the coal types are compared through the benefit model, and fuel purchase can be more refined.

Description

technical field [0001] The invention belongs to the technical field of coal-fired power generation, and in particular relates to a fuel intelligent model library for boosting high-efficiency production capacity. Background technique [0002] Coal is an important production raw material for coal-fired power generation enterprises, and its cost accounts for more than 70% of the production cost of coal-fired power plants. Since the reform of the supply side of the coal industry, affected by factors such as capacity constraints, environmental protection, and safety supervision, the supply of coal is generally tight The coal price will remain at a high level for a long time in the future, and the profitability of power plants will continue to decline. [0003] In order to reduce the cost of coal, the key to reducing the cost of fuel is to expand resources and tap potential internally. The diversity of coal types will be a normal state. Therefore, improving the company's fuel mana...

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Problems solved by technology

[0004] 1. The power generation department roughly estimates the coal consumption based on the start-up mode of the unit and the estimated power generation load, and then submits it to the fuel procurement department. The procurement department purchases relevant coal according to the demand. In terms of procurement strategy, it generally follows the price difference benefit points obtained from previous experiments. Procurement, and the benefit point evaluation is only for a single coal type, and the benefits after the comprehensive collocation of various coal types are not considered. In the period of constant changes in coal prices, the evaluation of the benefit point of the price difference cannot meet the actual procurement needs;

[0005] 2. For the next day’s boiler maximum load forecast, it is necessary to mobilize power generation load, heating load, and the recent provincial dispatch to calculate the probability of the maximum output of our driver’s crew calling the unit, which requires a lot of manpower and cannot be passed in time. The power generation load makes timely and dynamic adjustments to the calorific value of coal fired into the boiler;

[0006] 3. The update of the coal storage information in the coal yard is lagging and not intuitive. It is necessary to understand the coal storage structure of the coal yard through the report data. At the same time, there is a lack of intuitive presentation of the furnace data under the combination of coal calorific value, sulfur content, and ash content.

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