基于神经网络的转炉炼钢终点控制

转炉炼钢是一种非常重要的炼钢方法,终点控制是转炉炼钢后期的重要操作.由于冶炼过程温度极高,很难进行准确及时地测量,无法形成通常意义下的反馈控制.采用RBF神经网络预报转炉炼钢终点温度和碳含量,在此基础上提出了基于神经网络的动态终点控制方法来确定在补吹阶段需要的吹氧量和加入的冷却剂量,克服了传统控制方法中基于热平衡和氧平衡控制模型不准确的缺点,提高了终点命中率.     

一种转炉炼钢动态终点预报的新方法

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…     

一种基于增量式神经网络的转炉静态控制模型

针对中小型转炉不宜增设副枪、难以对钢水成分和温度进行连续检测、难以建立动态模型的实际情况,本文将传统增量模型和神经网络模型有机结合,提出了一种基于增量式神经网络的转炉静态控制模型,对钢水终点进行控制。在该模型引入了RBF神经网络对钢水终点温度和碳含量进行实时预报,使得对增量式神经网络控制模型的训练以预报模型的输出值与所要求的钢水终点温度和碳含量之差为最小,克服了常规静态控制模型存在的不足,改善了控制效果,提高了炼钢一倒命中率。     

基于变量选择的转炉炼钢终点预报模型

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.     

基于PSO-ICA 和RBF 神经网络的转炉炼钢终点预报模型

提出将微粒群优化算法和独立成分分析引入到径向基函数神经网络模型用于转炉炼钢终点预报．利用 微粒群优化算法的全局遍历特性和快速不动点算法的局部寻优能力,改进了传统的独立成分分析算法,解决了其目 标函数易陷入局部最优和独立特征排序不确定的问题,压缩冗余信息并降低输入维数．将提取出的独立特征输入径 向基函数神经网络,预报终点温度和碳含量．对转炉生产实测数据进行了仿真,结果表明该模型能有效提高预报精 度,保证预报的可靠性．     

转炉炼钢终点磷的智能预报

提出了一种用于预报转炉炼钢终点磷含量的智能方法, 在该方法中, 采用模糊推理和遗传算法, 其中模糊推理用于估算转炉熔池的磷含量, 而模糊推理模型中的各个系数则由遗传算法辨识与优化. 为了提高熔池磷的估算精度, 同时还设计了一种神经网络以补偿来自模糊推理过程的误差. 仿真结果表明了该方法的有效性.     

基于混合递阶遗传神经网络的转炉温度预报

针对转炉控制中对吹炼终点温度的控制问题,提出了基于混合递阶遗传RBF神经网络(HGA-RBF)的转炉炼钢终点温度预报模型.研究了RBF网络的特点,用递阶遗传算法克服了网络的结构和参数选择的随机性问题;并结合最小二乘法,提高了收敛速度.仿真结果表明,此算法在一定程度上提高了RBF网络的优化收敛速度和训练测试精度.某钢铁公司提供的实际冶炼数据试验,也证明了该模型预报精度较高,对提高生产的质量有重要意义.     

基于混合递阶遗传神经网络的转炉温度预报

针对转炉控制中对吹炼终点温度的控制问题，提出了基于混合递阶遗传RBF神经网络（HGA-RBF）的转炉炼钢终点温度预报模型。研究了RBF网络的特点，用递阶遗传算法克服了网络的结构和参数选择的随机性问题；并结合最小二乘法，提高了收敛速度。仿真结果表明，此算法在一定程度上提高了RBF网络的优化收敛速度和训练测试精度。某钢铁公司提供的实际冶炼数据试验。也证明了该模型预报精度较高，对提高生产的质量有重要意义。     

基于多任务学习的炼钢终点预测方法

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。     

微机在线应用中的实用通讯技术

<正> 随着计算机的迅速发展,控制系统也发生了很大的变化,逐渐从离线转为在线化,由单机控制发展到计算机分级控制。分级控制中最基本的要求就是计算机之间的数据通讯问题。以氧气顶吹转炉炼钢为例。此种炼钢技术在生产中已充分显示其重要作用。由于氧气顶吹转炉吹炼周期很短,必须准确地判断吹炼终点,提高终点的命中率,炉龄和质量。由自由炼钢转向科学炼钢,必须采用计算机进行控制和管理。计算机可根据静态/动态模型,按附加料模型,枪位模型来控制附加料的加人数量和时间,控制变枪和枪位并对检测到的过程信息进行修正,以达到终点目标有较高的命中率。在这个系统中,牵涉到在线检测和收集数据及对     

Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction

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.     

Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine

This study concerns with the control of basic oxygen furnace (BOF) steelmaking process and proposes a dynamic control model based on adaptive-network-based fuzzy inference system (ANFIS) and robust relevance vector machine (RRVM). The model aims to control the second blow period of BOF steelmaking and consists of two parts, the first of which is to calculate the values of control variables, viz., the amounts of oxygen and coolant requirement, and the other is to predict the endpoint carbon content and temperature of molten steel. In the first part, an ANFIS classifier is primarily constructed to determine whether coolant should be added or not, then an ANFIS regression model is utilized to calculate the amounts of oxygen and coolant. In the second part, a novel robust relevance vector machine is presented to predict the endpoint. RRVM solves the problem of sensitivity to outlier characteristic of classical relevance vector machine, thus obtaining higher prediction accuracy. The key idea of the proposed RRVM is to introduce individual noise variance coefficient to each training sample. In the process of training, the noise variance coefficients of outliers gradually decrease so as to reduce the impact of outliers and improve the robustness of the model. Simulations on industrial data show that the proposed dynamic control model yields good results on the oxygen and coolant calculation as well as endpoint prediction. It is promising to be utilized in practical BOF steelmaking process.     

转炉炼钢智能控制方法及应用

将模糊推理、自适应、自学习、专家系统等技术与转炉炼钢动态控制方法相结合,提出了转炉炼钢智能动态控制的新方法.该控制方法由动态吹炼过程补吹氧量与冷却剂量预设定模型、熔池碳含量和温度预测模型及停吹专家系统等组成,并成功应用于某钢厂转炉炼钢过程,大大提高了转炉炼钢过程熔池命中率.     

Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine

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.     

Optimal iterative learning control for end-point product qualities in semi-batch process based on neural network model

An optimal iterative learning control (ILC) strategy of improving endpoint products in semi-batch processes is presented by combining a neural network model. Control affine feed-forward neural network (CAFNN) is proposed to build a model of semi-batch process. The main advantage of CAFNN is to obtain analytically its gradient of endpoint products with respect to input. Therefore, an optimal ILC law with direct error feedback is obtained explicitly, and the convergence of tracking error can be analyzed theoretically. It has been proved that the tracking errors may converge to small values. The proposed modeling and control strategy is illustrated on a simulated isothermal semi-batch reactor, and the results show that the endpoint products can be improved gradually from batch to batch. Supported by the National Natural Science Foundation of China (Grant Nos. 60404012, 60874049), the National High-Tech Research & Development Program of China (Grant No. 2007AA041402), the New Star of Science and Technology of Beijing City (Grant No. 2006A62), and the IBM China Research Lab 2008 UR-Program