Research On The Optimization Of Waters Cooling Control In High Speed Steel Rolling System

The water cooling control system takes an important role in automatic control of high-speed wire rod mill. Since it realizes the intelligent control of temperature of rolling line, and ensures the quality of production, the water cooling control system is essential for the improvement of rolling speed. The use of advanced computer technology in the water cooling control system can enhance the monitoring, operation and automatic control level of the process of the wire rod production, thereby raising the efficiency and quality of the production. The above indicates that the improvement of the system of high-speed wire rod mills, especially the reliability and the precision of control is very meaningful.This dissertation has carried out in-depth research into the question of the low reliability and the high range of the temperature wave in the water cooling control system in high speed steel wire production line. The work has improved the reliability of the system and the precision of the temperature control.For the problem of the accuracy of temperature control in the water cooling control system in high speed steel wire production line, by introducing the feedforward multilayer perceptions that can approach complex function and has the ability of self-learning adaptive, we established an artificial neural network based optimization model in the water cooling control system and finally improved the accuracy of temperature control in water-cooling control system. Through a large number of input/output data samples of SMS water-cooling control system, we established the training sample set on the basis of data characteristics analysis and preprocessing, realized water-cooling control optimization system based on the Feed Forward Network (FFN). By the automatic judgment of the data gathering range and data validity with the guarantee of the real time, we established online adjustment method of the data sample collection for the neural network training in the water-cooled closed-loop control system, cooperate with offline training of network weights, and asynchronous update data network of weights. All these efforts results in improvement of the adaptability and reliability of the control system. The rolling line finishing mill inlet temperature range reduced from±60℃down to±28℃, the fluctuation range of the finishing mill exit temperature reduced from±15℃to±30℃.For the difficult to balance the accuracy and the real-time of temperature control in the water cooling control system, we utilized two types of membrane algorithm to improve artificial neural networks based optimization model in the water cooling control system. The membrane algorithm is a distributed parallel computing algorithm inspired by the cell structure and function, which is a new area of intelligent optimization. In recent years, membrane algorithm attracts the attention of scholars in the field of intelligent optimization. Experimental results showed that our methods not only meet the real time of the industrial requirements, but also greatly improve the accuracy temperature control. On the basis of data characteristics analysis and preprocessing, we introduced a method using the MATLAB toolbox to train the BP neural network weights and thresholds with the help of membrane algorithm. On site operation shows that the use of membrane algorithm greatly enhanced the stability and control accuracy of the water cooling control system.For the Robustness of the water cooling control system of high speed steel wire production line, we established the cooling system fault diagnosis and fault-tolerant model based on Bayesian probability network theory. Determine the range of data collection and data validation, Bayesian networks automatically adjust the neural network training sample data collection, offline re-training the network weights, and asynchronous updating of the weights of the network data, thus ensuring a water-cooled closed-loop control system in real time the premise, the self-adaptability and reliability of the network. On site operation shows that the use of Bayesian probability network optimization system greatly enhanced the reliability and control accuracy of the water cooling control system.According to the artificial neural network based optimization model in the water cooling control system, optimizing the control software of water cooling control system for Wuhan iron and steel company, from the results of the actual data, it is shown that optimized water cooling control system can improve the operational reliability and control accuracy and realized the industrial upgrading.