高炉炉顶温度分布模式识别神经元网络的研究

高炉煤气流的控制是高炉操作的重要方面,本文开发了自动识别炉喉温度分布模式的自组织神经元网络,利用大量炉顶十字测温数据归纳整理出25种温度分布模式,并自动将当前的温度分布归类为其中的某一种模式。将模型识别的温度分布模式与炉体热负荷等检测信息进行综合分析,能够对高炉的煤气流分布做出更准确的判断,为工长调整装料制度提供有意义的指导。     

基于模糊推理的高炉煤气流分布模式识别方法

高炉生产过程极其复杂,煤气流分布状态更是难以进行直接而准确的检测。通过对国内目前高炉监测手段的特点及适用性的分析,同时结合高炉煤气流分布的规律,提出了一种综合考虑多种检测信息和应用模糊推理技术的高炉煤气流识别方法。采用这种模式识别方法能够较为容易地在推理过程中引入高炉专家的操作经验知识,从而更合理地描述高炉内部煤气流的...     

高炉径向煤气流分布模型

介绍了一个在线的评价高炉径向煤气流分布的模型，该模型是在炉喉区的摩尔和能量流平衡的基础上，并依靠煤顶煤气温度和由固定在料面上探针测量的煤气温度建立起来的，而煤气通量的分布则是卡尔曼滤波器来评价。该方法证明可以得到芬兰高炉煤气流分布的短期动力学并可探测出突然的重大变化。     

高炉煤气流分布控制技术

调整控制好高炉内煤气流的分布,保持高炉长期安全稳定顺行,是真正实现低成本炼铁的重要途径。分析了原燃料条件变化、操作炉型变化以及事故对煤气流分布的影响,并提出了改进措施。通过采取上下部调剂、保持炉缸热量充沛、日常操作标准化等措施,实现了高炉炉内煤气流的合理分布,提高了煤气利用率,降低了高炉燃料消耗,保持了高炉的长期稳定、顺行。     

高炉煤气分布的在线模型

高炉煤气分布的在线模型［芬兰］ＭａｔｓＮｉｋｕｓＨｅｎｒｉｋＳａｘｅｎ１前言高炉煤气径向分布是个重要的变量，它不仅影响炉身的热量及质量传输，也影响炉衬寿命。矿石和焦炭的径向分布对料床的透气性影响很大，可通过控制炉料的分布来控制气流分布。由于高炉内部的...     

高炉合理煤气流分布探讨

提出了合理煤气流分布的概念，在分析国内外高炉煤气流分布基本型式及特征的基础上，探讨了南钢目前高炉煤气流的分布现状及对技术经济指标的影响，分析了影响煤气流分布的因素，提出了煤气流分布由合理向最佳的发展方向及需要的条件。     

首钢2号高炉煤气流分布的调整

针对首钢2号高炉煤气分布不合理的状况,进行了布料调整试验,改变其过分发展的边缘、中心煤气流分布,使高炉煤气流分布趋于合理,从而提高煤气利用率,降低炉顶温度,延长炉衬寿命.     

高炉煤气径向分布模型

本文介绍了高炉煤气径向分布在线预报模型。该模型以高炉炉喉部位的物料平衡和热平衡为基础,利用固定于料面上的料面仪测定炉顶温度和煤气温度分布,并且通过Ｋalman过滤器对煤气流分布进行了测定。以通过跟踪芬兰高炉煤气流分布的瞬间动态分布及其突发性主要变化对该模型进行了验证。     

简论锰铁高炉煤气流合理分布问题

本文论述了锰铁高炉内煤气流控制发展简况,现阶段炉喉 CO_2分布为“双峰漏斗式”的合理性以及获得“双峰漏斗式”曲线的基本条件与主要措施。简述了采用新技术后煤气流分布的趋向。     

A Neural Network Recognition Method of Shape Pattern

The final objective of shape control is to makethe output shape data achieve the objective shapedata,then the adjusting magnitude of shape controlactuating mechanism can be defined in accordancewith the deflection value of the output shape dataand the objective shape data.In shape controlsystem,the first thing is to make shape faultpattern recognition based on the speciality andrequirement of shape control actuating mechanism ofrolling mill.The main assignment of shape patternrecognition is to…     

Fuzzy Neural Model for Flatness Pattern Recognition

For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition.     

模式识别及人工神经网络算法在16Mn钢生产中应用

介绍了模式识别与人工神经网络算法应用于16Mn微合金化钢的生产工艺优化,并给出了各目标的最佳工艺参数供生产时参考.     

高炉布料数学模型的开发及应用

为研究炉料在炉内的分布,对武钢1号高炉在开炉前进行了料面测量,在获得的炉料堆积规律的基础上开发了高炉布料数学模型,利用开发的数学模型模拟炉料下落的轨迹,求解料面形状,计算炉料的O/C比分布,利用模型计算结果对不同布料矩阵的布料效果实行量化评估,结合高炉专家系统中有关煤气流分布的信息,可以及时对布料矩阵作出调整.     

神经网络在汉语语音识别中的应用

论述了基于神经网络模型的特定人汉语语音识别,并建立了一基于3层BP神经网络的汉语语音识别系统.对汉语10个数字(1~10)进行识别实验,获得了较满意的识别结果.     

变结构神经网络在板形信号模式识别方面的应用

提出了基于模糊距离的变结构神经优化算法,并将其用于板形信号的模式识别过程,有效地解决了板宽变化时神经网络拓扑结构不变的问题,提高了识别速度和精度,从而成为一种新的智能板形信号识别方法。     

自调整动态神经网络模型及其在带材板形预测中的应用

 建立了一个自调整动态神经网络预测模型，它在BP网络模型基础上，对网络的自身结构及学习规则进行了动态优化。网络能自组织和自学习自己的结构，即在学习过程中，网络可根据具体问题自动调整本身的结构，从而使结构达到最优。在此基础上，建立了板形神经网络预测模型，经某带钢厂四辊冷轧实测数据仿真验证表明，该模型具有很高的预测精度。     

A New Flatness Pattern Recognition Model Based on Cerebellar Model Articulation Controllers Network

In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and local minimum in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, the model is time-consuming and complex. Thus, a new approach of flatness pattern recognition is proposed based on the CMAC （cerebellar model articulation controllers） neural network. The difference in fuzzy distances between samples and the basic patterns is introduced as the input of the CMAC network. Simultaneously, the adequate learning rate is improved in the error correction algorithm of this neural network. The new approach with advantages, such as high learning speed, good generalization, and easy implementation, is efficient and intelligent. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously im proved.     

高炉炉顶煤气连续分析系统的开发与应用

分析比较了各种煤气分析方法的优缺点,指出炉顶煤气连续分析系统开发的难点在于样气的净化处理,阐述了样气净化装置的工作原理,介绍了炉顶煤气连续分析系统的构成、安装、功能和使用注意事项。在马鞍山钢铁股份有限公司新1号高炉上应用实践表明：该系统对炉况判断、冷却设备检漏、喷煤速率变化监测和热风炉烧炉都有重要意义。     

Evaluation of Burden Descent Model for Burden Distribution in Blast Furnace

Mathematical models for burden descending process have been applied to obtain whole burden structures in blast furnace,whereas the accuracy of those burden descent models has not been sufficiently investigated.Special evaluation method based on timeline burden profiles was established to quantitatively evaluate the error between ex-perimental and modeled burden structures.Four existing burden descent models were utilized to describe the burden structure of a 1/20 scaled warm blast furnace.Input modeling conditions including initial burden profile,descending volumes in each time interval,and normalized descending velocity distribution were determined via special image pro-cessing technology.Modeled burden structures were evaluated combined with the published experimental data.It is found that all the models caught the main profile of the burden structure.Furthermore,the improved nonuniform de-scent model (Model IV)shows the highest level of precision especially when burden descends with unstable velocity distribution tendency.Meanwhile,the traditional nonuniform descent model (Model III)may also be desirable to model the burden descending process when the burden descending velocity presents a linear tendency.Finally,the uni-form descent model (Model I)might be the first option for roughly predicting burden structure.