Heating furnace operation parameter recommendation system and method based on data mining

Abstract

The invention provides a heating furnace operation parameter recommendation system and method based on data mining. The method comprises the steps: acquiring and storing the production data and the heating data of a heating furnace at the current moment and the historical moments; generating a total sample set comprising a plurality of samples according to the stored data; setting a parameter recommendation condition according to the stored data; and calculating and displaying the optimal operation parameter value and the optimal operation parameter range according to the parameter recommendation condition. According to the method, more accurate and refined recommendation of each control parameter of heating operation of the heating furnace under different requirements can be realized, theheating quality of the heating furnace can be effectively improved, and meanwhile, the requirement for unit consumption of fuel is reduced.

Description

technical field [0001] The invention relates to the technical field of heating furnace control in the metallurgical industry, in particular to a system and method for recommending operating parameters of a heating furnace based on data mining. Background technique [0002] The heating furnace is the most important heating equipment in the production process of the metallurgical industry. Its function is to heat the billet to meet the requirements of subsequent rolling. The quality of the heating process of the heating furnace not only affects the heating quality and energy consumption of the billet, but also has an important impact on the output and quality of subsequent steel products. Therefore, it is very necessary to control the heating process of the heating furnace. [0003] The determination of the operating parameters of the heating furnace in the existing technical solutions usually depends on the understanding of the burnup theory and the heating process, and theo...

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Problems solved by technology

[0006] 1. Most of the existing methods are model-based, and the optimization model is constructed from the heating theory, heating process, automatic control principle, etc. The constructed model usually has a certain error with the real physical environment

[0007] 2. The scope of application is small, the migration ability is poor, and it cannot meet the needs of multiple levels

[0008] 3. The existing method can automatically adjust few parameters by computer (usually only the automatic setting of furnace temperature, time, and production rhythm), which is not refined enough, and some parameters still need to be manually set according to experience

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