On-line hardness forecasting method of continuous annealing product by means of integrated learning

Abstract

The invention discloses an on-line hardness forecasting method of a continuous annealing product by means of integrated learning, and belongs to the technical field of automatic control of continuous annealing production process in iron and steel enterprises. An integrated learning modeling method using LSSVM as a sub-learning machine is used by utilizing historical continuous annealing production data samples of enterprises, and hardness forecasting models of off-line products are respectively established for strip steel with different tempers; in practical production, the continuous annealing production process data is read in real time, and the hardness of the current strip steel product is forecasted in real time through the hardness forecasting models of off-line products established through the integrated learning; a test on the practical production data proves that the method can obviously improve the accuracy and the robustness of hardness forecasting results of the continuous annealing product, so that site operation personnel can master the quality of the current strip steel product in real time and can adjust timely according to situations; therefore, the deficiency of off-line detection large lag is made up, and the product quality, the production operation level and the economic benefit of the continuous annealing production line are improved.

Description

technical field [0001] The invention belongs to the technical field of automatic control of the continuous annealing production process of iron and steel enterprises, and in particular relates to an online integrated learning and prediction method for the hardness of continuous annealing products. Background technique [0002] In the actual production process of the continuous annealing unit of the cold rolling plant of the iron and steel enterprise, the hardness of the strip steel is the core index to measure the product quality and guide the production. In the actual production process, the hardness of the strip steel cannot be detected online. The on-site method is to measure the hardness of the strip steel by intercepting the head and tail parts of the annealed steel strip, and then conduct offline experimental analysis, so as to judge the product quality. However, obtaining product quality information through off-line experimental analysis generally has a certain time l...

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Problems solved by technology

However, obtaining product quality information through off-line experimental analysis generally has a certain time lag, that is to say, only when the strip steel is produced for a period of time, can its specific quality information be obtained. The speed is very fast, and the annealing treatment can be completed within a few minutes, so that the hardness of the strip often fluctuates greatly, resulting in quality problems such as substandard hardness or even scrap, which seriously affects the economic benefits of the cold rolling plant

[0003] The paper "Design and Implementation of Strip Quality Prediction and Process Monitoring System of Continuous Annealing Unit Based on PLS [D]" (Wang Yuan, Northeastern University, 2009) although proposed a method based on partial least squares (Partial Least Squares) for the hardness of strip products Squares, PLS) data-driven modeling method, but the method proposed in this paper cannot meet the needs of the actual production process. The main reasons are: (1) The information related to the hardness of the strip steel considered in this paper is relatively limited. There are only about 20, but there are as many as 51 pieces of process information related to strip hardness in the actual production process; (2) The PLS method proposed in this document is mainly for the monitoring and fault diagnosis of the continuous annealing production process , and the PLS method is a linear regression method, but the actual production process is nonlinear, which leads to the low accuracy of the PLS method; (3) the sample data has many input items and mutual coupling, but the output range is narrow The problem is that there are large differences in input items between samples, but the output results are the same or similar, making it difficult for traditional data modeling methods to obtain high prediction accuracy and robustness.

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