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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules
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Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules

机译：基于GA-BP神经网络的组合方法和逻辑规则的板坯连续铸造模具突破预测

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摘要

Given advantages of artificial intelligent technology, the genetic algorithm (GA) and back propagation (BP) neural network is used to construct time series model for recognizing temperature change waveform of single thermocouple in mold breakout process. Based on breakout mechanism, the logic rules are used to construct spatial model of multi-thermocouples for identifying two-dimensional (2D) propagation behavior of the sticker. Time series model based on GA-BP neural network and spatial model based on logic rules form a new breakout prediction method. And the simulation and field test of this method are carried out for validating its performance. Simulation results of time series model show that the GA-BP neural network has better recognition precision than BP neural network for sticking temperature pattern of single thermocouple. And simulation results of spatial model show that it can predict all stickers accurately and timely, with no missed alarm and false alarm. Furthermore, field test results show that this breakout prediction method has detection ratio of 100% and a lower false alarm frequency (0.1365% times/heat), which is better than actual breakout prediction system used in continuous casting production. So the combined method of GA-BP neural network and logic rules is feasible and effective in breakout prediction and can be used in more intelligently industrial process.
机译：鉴于人工智能技术的优点，遗传算法（GA）和反向传播（BP）神经网络用于构造模突发过程中单热电偶温度变化波形的时间序列模型。基于断开机制，逻辑规则用于构造用于识别贴纸的二维（2D）传播行为的多热电偶的空间模型。基于GA-BP神经网络和基于逻辑规则的空间模型的时间序列模型形成了一种新的断开预测方法。并执行该方法的仿真和现场测试，以验证其性能。时间序列模型的仿真结果表明，GA-BP神经网络具有比BP神经网络更好的识别精度，用于粘附单热电偶温度模式。和空间模型的仿真结果表明它可以准确及时预测所有贴纸，没有错过警报和误报。此外，现场测试结果表明，该断开预测方法具有100％的检测比和较低的误报（0.1365％/加热），这比连续铸造生产中使用的实际突破预测系统更好。因此，GA-BP神经网络和逻辑规则的组合方法在突破预测中是可行的，有效的，并且可以在更智能地工业过程中使用。

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