| 加热炉炉温神经网络预测模型的开发 |
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| 引用本文： | 谭玉倩,曹,磊,钟,闻.加热炉炉温神经网络预测模型的开发[J].冶金动力,2014(4):58-60,65. |
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| 作者姓名： | 谭玉倩 曹 磊 钟 闻 |
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| 作者单位： | 山东钢铁股份有限公司莱芜分公司自动化部,山东莱芜271104 |
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| 摘 要： | 针对加热炉系统非线性、大滞后、大惯性,炉温难以有效预测的问题,以山东钢铁莱芜分公司宽厚板加热炉为研究对象,通过神经网络训练获得充分逼近仿真对象的系统参数,最后使用该方法对莱钢宽厚板加热炉炉温进行预测,结果说明该方法预测准确,具有较强的实践意义,为炉温控制提供了可靠依据,提高了生产效率,降低了能耗。
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| 关 键 词： | 加热炉炉温 学习速率 动量因子 BP神经网络 |
Development of Neural Network Prediction Model for Temperature of Heating Furnace |
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| TAN Yuqian,CHAO Lei,ZHONG Wen.Development of Neural Network Prediction Model for Temperature of Heating Furnace[J].Metallurgical Power,2014(4):58-60,65. |
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| Authors: | TAN Yuqian CHAO Lei ZHONG Wen |
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| Affiliation: | (The Automation Dept. of Laiwu Steel of Shandong Iron and Steel Group, Laiwu, Shandong 271104, China) |
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| Abstract: | The temperature inside heating furnace is hard to predict due to nonlinear, high hysteretic and big inertia of the system. Aimed to the wide-heavy plate heating furnace of Laiwu Steel, system parameters fully close to emulated object were obtained through neural network training. Finally this approach was used to predict temperature inside the wide-heavy plate heating furnace of Laiwu Steel, the results of which showed that the method predicts accurately, bears practical significance, provides reliable basis for furnace temperature control, improves production efficiency and reduces energy consumption. |
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| Keywords: | temperature inside heating furnace learning rate factor of momentum BP neural network |
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