共查询到19条相似文献,搜索用时 187 毫秒
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为提高LF精炼钢水终点温度控制水平,提出了基于主成分分析(PCA)和BP神经网络的联合方法预测LF钢包炉精炼钢水终点温度。基于冶金理论和实际生产实践,选取了42CrMo钢生产过程的10个对终点温度有显著影响的因素作为预测模型的指标体系,然后借助主成分分析法对样本数据进行处理,得到了7个主成分变量,累计方差贡献率为87.24%,消除了数据之间的关联性,以此为基础,建立了基于PCA-BP神经网络的LF炉终点温度预测模型,该模型预测误差在±25℃时,模型的命中率为98.71%,模型有较好的识别能力,能够达到LF炉生产过程预测终点温度的目的。 相似文献
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LF精炼工序在炼钢过程起着调节温度的关键作用,准确预报LF精炼终点钢水温度对实际生产有重要意义.传统的LF精炼预报模型包括机理模型与黑箱模型.机理预报模型能够体现各工艺因素对终点钢水温度的影响,但由于LF精炼传热机理研究尚不完善,依靠机理模型预报终点钢水温度,难以达到预期效果;黑箱预报模型能够准确预报终点钢水温度,但不能反映精炼过程各工艺因素对钢水温度的影响,尤其当生产工艺条件发生改变时,黑箱模型在应用上会受到限制.本文以方大特钢LF精炼炉为研究对象,建立一种机理预报模型与黑箱预报模型(BP神经网络预报模型)相结合的LF精炼终点钢水温度灰箱预报模型.该模型既能反映各工艺因素对终点钢水温度的影响,又能准确预测终点钢水温度,其终点钢水温度预测误差在±5℃以内的命中率可以达到95%以上. 相似文献
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《中国锰业》2019,(2)
在国内重工业领域中,很多钢铁企业所采用的转炉大部分为最小型的转炉,由于容量有限无法对转炉冶炼结束时的锰、磷进行静态预测,进行影响了冶炼的精度。然而,传统算法用于实现锰和磷的冶炼终点。因此,充分利用最近开发的人工神经网络技术,基于Visual Basic编程语言,神经网络模型用于预测转炉冶炼结束时的锰和磷状态。针对半钢炼钢分开建立锰、磷含量、温度预测模型,确定输入层参数有37个,中间隐藏层参数有30个,输出层参数有两个3层BP神经网络。模型在30 000炉样本的基础上做数据训练,对权值、阈值进行修正,并保存100炉未训练过的学习样本作为模型网络训练依据,对转炉冶炼进行在线训练,通过训练的模型可以很好的适应转炉冶炼多变的生产条件。 相似文献
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LF钢包炉精炼终点钢水温度的预报模型 总被引:1,自引:0,他引:1
采用多元回归分析方法建立了宝钢一炼钢厂LF钢包炉精炼终点钢水温度的预报模型,应用该模型对LF精炼终点钢水温度进行预测,对预测结果进行了统计分析,结果表示该模型对LF钢包炉精炼终点温度的预测误差在+10℃时的命中率达到95%。 相似文献
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采用多元回归分析方法建立了涟钢210转炉厂LF钢包炉精炼终点钢水温度的变化模型,应用该模型对LF精炼终点钢水温度进行预测,对预测结果进行了统计分析,结果表明该模型对LF钢包炉精炼终点温度的预测误差较小,能对现场产生指导意义。 相似文献
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In order to improve the temperature control level of molten steel in ladle furnace (LF), a case‐based reasoning (CBR) method has been proposed for predicting end temperature of molten steel in LF. To predict the temperature accurately and efficiently, this paper develops two‐step retrieval approach and the correlation based feature weighting (CFW) method for CBR. And, the study evaluates the prediction effect of CBR method by the experiment of comparison with back propagation neural network (BPNN) model and CBR model. Experimental results show that CBR model achieves better accuracy than BPNN model and the CBR method is effective to predict end temperature of molten steel in LF. 相似文献
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Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM (extreme learning machine) for predicting the molten steel temperature in LF. Secondly, the self adaptive data fusion is proposed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy. 相似文献
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Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could over- come the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are es- tablished and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish tempera- ture, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blo- wiag station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production. 相似文献
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转炉钢水温度是转炉终点控制的工艺参数之一,精确的钢水温度预测对转炉终点控制具有重要的指导意义。然而,以往的大多数转炉终点预测模型属于静态模型,只能够实现对转炉吹炼终点钢水温度的预测,无法实现动态预测,导致模型的作用有限。针对该问题,提出了一种基于数据驱动的转炉二吹阶段钢水温度动态预测模型。模型先通过新案例主吹阶段的工艺参数,基于案例推理算法找到历史案例库中相似案例。再利用相似案例的二吹阶段工艺参数并基于长短期记忆网络(Long short-term memory,LSTM)算法训练工艺参数与钢水温度的变化关系。然后利用训练好的LSTM模型,计算新案例二吹阶段的钢水温度变化。最后,利用某钢厂实际生产数据,研究了不同重用案例个数及神经元个数对模型预测精度的影响,实验结果表明:模型在重用案例个数为4,神经元个数为10时模型的预测精度最高,此时模型对钢水温度的预测误差在[?5 ℃, 5 ℃]、[?10 ℃,10 ℃]和[?15 ℃,15 ℃]的命中率分别达到40.33%、68.92%和88.33%,模型的性能高于传统二次方模型和三次方模型。 相似文献
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