吊挂式钢碹碴结构的分析与应用

详细介绍了熔窑的横向钢结构、钢碹碴结构等,并结合本窑大碹的特点对钢碹碴的受力和工作原理进行详细的分析、说明。此结构使碹砖砌筑面均匀受力,减小大碹上表面出现温差胀缝,增加大碹的气密性。提高玻璃熔窑的使用寿命和安全性。     

用回归分析法预测玻璃熔窑大旋横向膨胀率

玻璃熔窑大旋硅砖膨胀率的控制是整个玻璃熔窑点火烤窑的关键.当前,由于测试手段的落后和缺乏科学的计算方法,对大中型玻璃熔窑大旋在不同温度下的横向膨胀率,特别对硅砖晶型转变关键温度区域的膨胀率缺乏准确数据,而只能根据窑顶膨胀记录尺变化情况和大旋拉条的松紧来判断大旋的膨胀情况.能否找出一种科学的计算方法,预测出特定窑型大旋在不同温度下的膨胀率,为点火烤窑制定升温曲线、控制升温速率和及时调整拉条提供一定的根据呢?一九八○年六月我厂六机平板窑点火烤窑时,我们收集了几千个温度和大旋膨胀变化数据,采用回归分析法,计算出了在任意温度下大旋膨胀率的经验公式.     

玻璃熔窑大碹的受力及运行

研究了玻璃熔窑大碹的受力状况。从单块碹砖受力规律推演到连续多块碹砖受力的受力支撑线。受力支撑线模式随着拉条的松紧可以从最小模式演变到最大模式。熔窑大碹是冷态气密性砌筑的。从冷态大碹到热态大碹过渡时,硅质碹砖在大碹内侧膨胀量很大,引起熔窑大碹的弧度特征改变。如果采用自适应碹脚梁支撑,可以把冷态大碹弧度支撑过渡到热态的大碹支撑模式。如果不能适应大碹热态弧度的碹脚支撑,则大碹碹砖会产生缝隙。缝隙分布与最大支撑线和最小支撑线模式有关;对于最大支撑线模式,缝隙主要分布在碹顶附近。     

千吨级大跨度玻璃熔窑大碹的设计和施工

日熔化量千吨以上的玻璃熔窑具有单位能耗和单位投资成本低的特点,能提高能源效率,减少污染排放,符合碳达峰和碳中和的目标。介绍了千吨级大跨度玻璃熔窑的大碹耐火材料的选用和大碹的设计,提出了大跨度窑碹的施工质量控制方法。     

玻璃熔窑大碹木碹胎的制作与安装

从碹胎设计、木材订购、放样制作、现场安装、砌筑检查、碹胎拆除6个方面阐述了玻璃熔窑大碹木碹胎的制作与安装过程及注意事项,以达到保证玻璃熔窑大碹砌筑质量、安全、进度的目的。     

浅谈玻璃熔窑大碹保温

从大碹保温传热理论入手,阐述了合理的大碹保温结构,并探讨了熔窑运行周期各个阶段,如何根据大碹状况调整保温结构,为玻璃企业提升熔窑大碹保温效能提供借鉴.     

浅谈玻璃熔窑大碹保温

从大碹保温传热理论入手,阐述了合理的大碹保温结构,并探讨了熔窑运行周期各个阶段,如何根据大碹状况调整保温结构,为玻璃企业提升熔窑大碹保温效能提供借鉴。     

煤气熔窑舌头碹的维护方法

燃煤气玻璃熔窑的舌头碹是热负荷最高的部位。舌头碹的烧损直接威胁熔窑的寿命,因此燃煤气玻璃熔窑需经常对小炉舌头碹进行局部热修或整体更换,以减轻因舌头碹问题给熔化带来的不利影响。本文介绍了某公司经过多年实践经验摸索出的集中煤气熔窑舌头碹的维护方法,以期与同行共同探讨。     

高碹顶玻璃熔窑全氧燃烧火焰空间的三维数值模拟

以优化玻璃熔窑结构,提高全氧燃烧玻璃熔窑寿命为目的,建立了高碹顶玻璃熔窑全氧燃烧火焰空间中燃烧过程的三维数学模型。从模拟结果可以看出,与普通碹顶全氧燃烧玻璃熔窑相比,高碹顶熔窑改善了窑内气体流动与温度分布,碹顶温度低且分布均匀,窑内烟气环流多、在窑内停留时间长,热效率高,减少了碹顶水蒸气浓度,很好地保护了窑墙和碹顶耐火材料,延长了全氧燃烧玻璃熔窑的使用寿命;有利于促进全氧燃烧技术在玻璃工业上广泛应用。     

窑碹结构的推力作用线

以玻璃熔窑的熔化部大碹为例对窑碹结构的推力作用线做了说明,并对两条具有极限特征的窑碹推力作用线做了重点说明。介绍了碹结构压应力的计算公式,对烤窑中可能出现的最大推力作用线和松拉条时可能出现的最小推力作用线进行了分析。     

基于改进支持向量机的高炉一氧化碳利用率预测方法

高炉冶炼是一个具有非线性、大时滞、大噪声、分布参数等特征的高度复杂生产过程。针对目前高炉现场以焦比为能耗评价指标却无法提供实时指导的问题, 研究以一氧化碳利用率为能耗评价指标, 提出一种基于改进支持向量机的高炉一氧化碳利用率预测方法。首先分析高炉炼铁过程机理, 结合互信息法得出影响一氧化碳利用率的相关操作因素。然后鉴于生产数据含噪高的特点, 采用小波去噪方法去除数据噪声干扰, 并且利用灰色相对关联度分析方法对操作参数进行时序配准, 消除时滞影响, 建立高炉一氧化碳利用率预测模型。在建模过程中, 将自适应粒子群与支持向量机回归方法相结合, 以克服模型参数选择的随机性, 提高了模型预测精度。现场实际数据的预测结果表明所提出方法的有效性, 能够实时精确地预测高炉一氧化碳利用率, 为后续高炉的优化操作和节能减排提供了及时有效的决策支持。     

Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

This article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.     

基于PSO-SVR算法的燃煤锅炉结渣特性预测

针对燃煤锅炉结渣特性的有限样本、非线性和高维数问题,提出了一种基于粒子群优化（PSO）和支持向量回归（SVR）的预测模型。对于支持向量回归机在建模中存在的参数选取问题,采用改进的粒子群算法（PSO）对模型参数进行优化,该方法结合了PSO的快速全局优化能力和SVR的结构风险最小化理论,精确地逼近非线性映射关系的能力。仿真结果表明：相比遗传算法（GA）SVR预测模型和模拟退火（SA）SVR预测模型,PSO-SVR模型预测燃煤锅炉结渣特性具有较高的准确率。     

支持向量回归在乙烯裂解产物收率软测量中的应用

乙烯裂解产物收率的实时预报对于裂解炉的生产具有重要意义。针对有效的样本数据较少的问题,采用支持向量回归方法建立裂解产物收率的软测量模型。对于支持向量机中模型参数的选取,采用了微粒群优化算法进行参数寻优,提高了建模效率和模型精度。基于现场数据的建模实验结果表明,基于支持向量回归方法的乙烯裂解产物收率软测量模型预报精度较高,趋势跟踪性能良好。     

基于GA-SVR的水泥分解炉分解率软测量方法

针对水泥生产企业无法对分解炉分解率进行实时在线检测的情况,结合厂家DCS系统中现有的数据,使用灰色关联分析法观察相关变量与分解炉分解率的关系。利用遗传算法对SVR中的惩罚参数C、核函数核宽g、损失系数ε进行参数寻优,使用寻优结果对分解炉分解率进行SVR软测量建模。不同建模方法间的比较结果表明:使用GA-SVR所建的水泥分解炉分解率模型的预测结果与真实值拟合程度高、测量误差小,能够达到稳定水泥分解炉分解率的目的。     

Support vector regression with parameter tuning assisted by differential evolution technique: Study on pressure drop of slurry flow in pipeline

This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important process engineering problems. The method incorporates hybrid support vector regression and differential evolution technique (SVR-DE) for efficient tuning of SVR meta parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.     

基于支持向量机与高斯分布估计的低NOx排放

燃烧优化的核心在于建立有效而快速的建模工具及寻优算法,以便于在线应用。为了研究新方法的适用性以及克服常用算法的缺点,本文利用支持向量回归建立了大型四角切圆燃烧电站锅炉NO_x排放特性模型。利用大量的热态实炉试验NO_x排放数据对模型进行了训练和验证。结果表明,支持向量回归模型能获得较神经网络模型更加准确的预测结果,相对于神经网络,支持向量回归能更好处理大样本量数据的非线性问题。随后,采用一种基于高斯概率密度 (GPDD)的分布估计优化算法对NO_x排放模型进行了寻优。研究发现,与遗传算法相比,GPDD具有更好的寻优能力与更快的收敛速度。结合支持向量回归与高斯概率密度分布(GPDD)算法能有效降低燃煤锅炉NO_x排放量,不到1 min的优化时间便于在线应用。研究结论可为该算法在实际电厂中推广应用提供参考依据。     

基于SVR-GA模型的浆态管流压力差的预测

This paper describes a robust support vector regression （SVR） methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique （SVR-GA） for efficient tuning of SVR meta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the lit- erature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.     

The sparse least square support vector regression for estimating illumination chromaticity

The fusion of estimating algorithms for illumination chromaticity is an important strategy in computational color constancy and it has attracted the much attention of domestic and foreign scholars. Some effective approaches have been proposed to build the framework of the fusion, such as Committee‐based Color Constancy, BP neural network, and machine learning regression. In this article, the framework of least square support vector regression (LS‐SVR) is applied to illumination estimation fusion and provides a novel sparse method of LS‐SVR. According to the experience of previous research, the complexity in training LS‐SVR is obviously lower than it in training SVR, but due to all of the characters in images are chosen as the support vectors, the computation in LS‐SVR takes more time. In order to improve accuracy and reduce time consumption, this study uses sparse strategy that only weighted samples that illumination chromaticity of image have higher weight than other images can be selected as the support vectors. Training via a part of the images with high‐weight, the sparse LS‐SVR can achieve a satisfactory result. Experiment with real images shows that this sparse LS‐SVR method performs better than SVR, LS‐SVR and current some other color constancy algorithms especially in the aspects of computing speed and accuracy.     

Simultaneous data reconciliation and joint bias and leak estimation based on support vector regression

Process data measurements are important for process monitoring, control, optimization, and management decision making. However, process data may be heavily deteriorated by measurement biases and process leaks. Therefore, it is significant to simultaneously estimate biases and leaks with data reconciliation. In this paper, a novel strategy based on support vector regression (SVR) is proposed to achieve simultaneous data reconciliation and joint bias and leak estimation in steady processes. Although the linear objective function of the SVR approach proposed is robust with little computational burden, it would not result in the maximum likelihood estimate. Therefore, to ensure accurate estimates, the maximum likelihood estimate is applied based on the result of the SVR approach. Simulation and comparison results of a linear recycle system and a nonlinear heat-exchange network demonstrate that the proposed strategy is effective to achieve data reconciliation and joint bias and leak estimation with superior performances.