共查询到18条相似文献,搜索用时 671 毫秒
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一种转炉炼钢动态终点预报的新方法 总被引:3,自引:0,他引:3
1 引言转炉炼钢就是将含有较多杂质的铁水与吹入的氧气发生反应 ,达到去除杂质的目的 ,从而获得要求的钢水成分和温度 .目前自动化炼钢的方法是静态控制和以副枪检测信息为基础的动态控制相结合的方法 .动态控制的关键是准确预报转炉炼钢终点温度和碳含量 .文 [1 ]在文 [2 ,3]的基础上采用 RBF神经网络对转炉炼钢终点温度和碳含量进行预报 ,提高了预报精度 .但是 ,终点温度和碳含量受到非定量因素的影响 ,文 [4 ]提出以灰色模型为基础的预报方法 ,由于应用线性回归补偿 ,因而影响预报精度 .本文将灰色模型与RBF神经网络相结合提出新的转… 相似文献
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基于变量选择的转炉炼钢终点预报模型 总被引:4,自引:0,他引:4
转炉炼钢的终点预报模型对于钢水终点碳含量和温度的命中非常重要.针对高维输入不利于建立精确模型的问题,使用互信息方法对预报模型输入变量进行选择.为了区分各输入变量对输出的不同重要程度,对各输入变量进行加权处理,并采用微粒群算法对权值进行优化.最后,使用支持向量机方法建立转炉炼钢终点碳含量和温度预报模型.对一座180t转炉实际生产数据进行仿真,结果表明,合理的变量选择和加权处理能有效提高模型的预报精度. 相似文献
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钢水碳含量模型的模糊辨识及应用 总被引:1,自引:1,他引:0
提出一种依据实测的温度、氧含量、碳含量数据,应用模糊推理辨识氧气顶吹转炉钢水终点定碳预报模型的方法.介绍了如何采用窗口移动收缩法确定有效测量值. 相似文献
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钢水质量通常根据终点命中率来判断,但炼钢过程影响因素众多,机理分析难以准确预测终点温度和含碳量,鉴于此,提出一种由数据驱动的多任务学习(MTL)炼钢终点预测方法。首先,分析并提取炼钢过程的输入和输出要素,结合炼钢两阶段吹炼特点选择多个子学习任务;其次,根据子任务与终点参数的相关性选择合适的子任务,提升终点预测的准确度并构建多任务学习模型,再对模型输出结果进行二次优化;最后,通过近端梯度算法对处理后的生产数据进行模型训练,获取多任务学习模型的过程参数。以某钢厂为案例,该方法相比神经网络在终点温度12℃误差范围内和终点含碳量0.01%误差内的准确度提升了10%,误差范围6℃和0.005%的预测准确度分别提升了11%和7%。实验结果表明,多任务学习在实际中能够提升终点预测的准确性。 相似文献
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The endpoint parameters of molten steel, such as the steel temperature and the carbon content, directly affect the quality of the production steel. Moreover, these endpoint results cannot be the online continuous measurement in time. To solve the above-mentioned problems, an anti-jamming endpoint prediction model is proposed to predict the endpoint parameters of molten steel. More specifically, the model is constructed on the parameters of extreme learning machine (ELM) adaptively adjusted by the evolutionary membrane algorithm with the global optimization ability. In other words, the evolutionary membrane algorithm may find the suitable parameters of an ELM model which reduces the incidence of the overfitting of ELM affected by the noise in the actual data. Finally, the proposed model is applied to predict the endpoint parameters of molten steel in steel-making. In the simulation experiments, two test problems, including ‘SinC’ function with the Gaussian noise and the actual production data of basic oxygen furnace (BOF) steel-making, are employed to evaluate the performance of the proposed model. The results indicate that the proposed model has good prediction accuracy and robustness in the data with noise. Therefore, the proposed model has good application prospects in the industrial field. 相似文献
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钢坯加热过程是钢铁企业热轧生产中非常重要的工艺环节。钢坯温度预报模型是实现加热炉优化控制的重要基础,用常规仪器很难直接测量出钢坯温度。给出了基于RBF神经网络的软测量模型结构,对钢坯温度进行预报的仿真结果。 相似文献
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Nonlinear model predictive control for the ALSTOM gasifier 总被引:2,自引:0,他引:2
In this work a nonlinear model predictive control based on Wiener model has been developed and used to control the ALSTOM gasifier. The 0% load condition was identified as the most difficult case to control among three operating conditions. A linear model of the plant at 0% load is adopted as a base model for prediction. A nonlinear static gain represented by a feedforward neural network was identified for a particular output channel—namely, fuel gas pressure, to compensate its strong nonlinear behaviour observed in open-loop simulations. By linearising the neural network at each sampling time, the static nonlinear model provides certain adaptation to the linear base model at all other load conditions. The resulting controller showed noticeable performance improvement when compared with pure linear model based predictive control. 相似文献
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Xinzhe Wang Min Han Jun Wang 《Engineering Applications of Artificial Intelligence》2010,23(6):1012-1018
Basic oxygen furnace (BOF) steelmaking is a complex process and dynamic model is very important for endpoint control. It is usually difficult to build a precise BOF endpoint dynamic model because many input variables affect the endpoint carbon content and temperature. For this problem, two effective variables selection steps: mechanism analysis and mutual information calculation are proposed to choose appropriate input variables according to a variable selection algorithm. Then, the selected inputs are weighted on the basis of mutual information values. Finally, two input weighted support vector machine BOF endpoint dynamic models are constructed to predict endpoint carbon content and temperature. Results show that the variable selection for BOF endpoint prediction model is essential and effective. The complexity and precise of two endpoint prediction models are improved. 相似文献
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基于Hammerstein 模型的感应电机变频器调速系统神经网络控制 总被引:1,自引:0,他引:1
针对感应电机变频器调速系统的非线性特点,提出一种基于Hammerstein模型的神经网络控制方法。 Hammerstein模型由静态非线性模块和动态线性模块组成。首先,利用ARMA模型实现对感应电机变频器调速系统的线性动态模块辨识;然后,基于该辨识模型,实现调速系统非线性静态模块神经网络逆模型辨识与系统直接逆控制;最后,针对控制过程中存在的电机负载扰动问题,设计了神经网络直接逆控制器在线学习与控制策略。仿真实验表明,所提出的控制策略可以获得满意的控制效果。 相似文献