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首页> 外文期刊>Neurocomputing >Electrode regulating system modeling in electrical smelting furnace using recurrent neural network with attention mechanism
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Electrode regulating system modeling in electrical smelting furnace using recurrent neural network with attention mechanism

机译：基于注意力机制的递归神经网络在电炉电极调节系统建模中的应用。

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摘要

Electrical smelting furnace (ESF) is the primary equipment to produce steer, nonferrous metals and other materials. In smelting process, it is beneficial to keep the smelting current stable within a reasonable range to improve product quality and reduce energy consumption. ESF is characterized by nonlinearity, strong coupling and time variation, which makes it difficult to establish accurate mathematical models. Therefore, we propose a data-driven recurrent neural network (RNN) using gated recurrent unit (GRU) with attention mechanism not only to establish the relationship between the electrode position and the smelting current but also to reveal the internal dynamic variation between three-phase currents for the electrode regulating system. Our proposed model is tested on the actual production data collected from a fused magnesium furnace (one kind of ESF) in Liaoning Province of China. Numerical results show RNN excels at processing sequential data and describing inner changes of the dynamic system; GRU alleviates long-term dependency problems in industrial big data; attention mechanism can identify and put emphasis on the crucial points containing key information in long sequential data. The results indicate that the proposed model is effective and feasible for the modeling of electrode regulating system. (C) 2019 Published by Elsevier B.V.
机译：电冶炼炉（ESF）是生产转向钢，有色金属和其他材料的主要设备。在熔炼过程中，有利的是将熔炼电流保持在合理范围内，以提高产品质量并降低能耗。 ESF具有非线性，强耦合和时间变化的特征，这使得难以建立准确的数学模型。因此，我们提出了一种利用门控递归单元（GRU）的数据驱动递归神经网络（RNN），该机制具有注意机制，不仅可以建立电极位置与冶炼电流之间的关系，还可以揭示三相之间的内部动态变化。电极调节系统的电流。我们建议的模型是根据从中国辽宁省的熔融镁炉（一种ESF）收集的实际生产数据进行测试的。数值结果表明，RNN在处理顺序数据和描述动态系统内部变化方面表现出色。 GRU缓解了工业大数据中的长期依赖问题；注意机制可以识别并强调长序列数据中包含关键信息的关键点。结果表明，该模型对电极调节系统的建模是有效可行的。 （C）2019由Elsevier B.V.发布

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