矸石电厂煤泥输送管道堵塞预测研究
发布时间：2018-09-03 13:53
【摘要】：煤泥作为煤炭废料,矸石电厂将其作为燃料在循环流化床锅炉内掺烧,通过铺设管道进行煤泥输送。煤泥输送管道的堵塞问题一直是影响煤泥输送系统生产效率和安全的重要因素之一。对堵塞进行故障预测和定位将能显著提高煤泥输送系统的运行性能,也是保障循环流化床锅炉正常与安全运行的重要课题。本文研究了基于小波变换和极限学习机的煤泥输送管道堵塞预测及定位方法,主要开展了以下研究工作:针对煤泥输送系统的监测数据易受到干扰影响的问题,分析现场监测数据的特点,对监测数据进行缺失数据补齐和数据去噪预处理。采用三次指数平滑法对数据进行补齐处理,采用小波变换法进行数据去噪,比较四种不同阈值方法的去噪效果,并将小波空域相关法用于信号去噪,取得了更好的去噪效果,实现了煤泥输送管道监测数据的预处理,并为后期的压力分布建模、堵塞预测及定位奠定基础。针对复杂管道煤泥输送时阻力损失的问题,分析煤泥输送管道的阻力损失机理及影响因素,确定阻力损失是剪切应力和摩擦阻力共同作用的结果;建立基于机理分析法的复杂管道(水平直管、倾斜管道、垂直管道)的压力分布模型。对管道内的一段煤泥进行受力分析,建立力的平衡方程,并在非线性参数约束条件下对其进行求解,得出复杂管道压力分布的数学模型,其服从复杂的指数关系,并在确定模型摩擦阻力系数中改进了常用的的计算方法;分析压力分布的多变量影响因素,建立基于量子遗传(QGA)BP神经网络的QGA-BP的压力分布模型。针对煤泥输送管道堵塞预测问题,提出基于粒子群优化核函数极限学习机(PSOKELM)的煤泥输送管道堵塞预测方法,该方法在分析煤泥输送管道堵塞预测机理的基础上,确定堵塞预测的特征量,将支持向量机核函数引入极限学习机,并通过粒子群算法进行参数优化。利用黄陵煤矸石热电厂实际测试数据进行仿真实验,并与粒子群算法优化支持向量机(PSOSVM)预测模型和核函数极限学习机(KELM)预测模型进行比较,结果证明基于PSOKELM的预测模型在预测速度和准确性方面均优于PSOSVM预测模型,在预测精度上优于KELM预测模型。针对煤泥输送管道堵塞定位问题,分析瞬态正负压波法的定位原理,利用小波变换空域相关法对正负压波信号去噪,提出基于小波包预处理经验模态分解和小波变换模极大值法相结合(WPEMD-WTM)的正负压波突变点检测方法,确定正负压波时间差,结合压力波波速进行堵塞点定位,并通过仿真验证了该堵塞定位方法的有效性和准确性。针对煤泥输送管道堵塞故障的报警问题,研究堵塞故障异常分析方法。通过计算压力预测值及预测区间,结合压力样本数据的统计特征,判断压力异常状况,确定警示阈值,划分警示等级,根据不同等级,采取不同的安全控制措施,并验证了堵塞故障安全控制方法的合理性。本文提出的煤泥输送管道压力分布模型,可对管道设计和膏体泵选择提供依据;基于核函数极限学习机的堵塞预测模型和基于小波分析的堵塞定位方法,能对于煤泥输送系统的堵塞故障问题提供新的安全控制决策和手段。
[Abstract]:Slime is a kind of coal waste, which is burned in a circulating fluidized bed boiler as fuel in a gangue power plant. Slime transportation is carried out by laying pipelines. The blockage of the pipelines has always been one of the important factors affecting the production efficiency and safety of the sludge transportation system. The operation performance of the feeding system is also an important issue to ensure the normal and safe operation of CFB boilers. This paper studies the prediction and location method of slurry pipeline blockage based on wavelet transform and extreme learning machine. The characteristics of field monitoring data are analyzed, and the missing data are complemented and the data are denoised by cubic exponential smoothing method and wavelet transform method. Good denoising effect has realized the pretreatment of monitoring data of slime transportation pipeline, and laid the foundation for modeling the pressure distribution in the later period, predicting and locating the blockage. The pressure distribution model of the complex pipeline (horizontal straight pipe, inclined pipe, vertical pipe) based on the mechanism analysis method is established. The force analysis of a section of slime in the pipeline is carried out, the force balance equation is established, and the mathematical model of the pressure distribution of the complex pipeline is obtained under the condition of nonlinear parameter constraints. The model obeys complex exponential relation and improves the commonly used calculation method in determining the friction coefficient of the model; analyzes the multi-variable influencing factors of pressure distribution, establishes the pressure distribution model of QGA-BP based on the quantum genetic algorithm (QGA) BP neural network. Kernel function extreme learning machine (PSOKELM) is used to predict the blockage of coal slurry pipeline. Based on the analysis of the blockage prediction mechanism of coal slurry pipeline, the characteristic quantity of blockage prediction is determined. The kernel function of support vector machine is introduced into the extreme learning machine, and the parameters are optimized by particle swarm optimization. The test data are simulated and compared with PSOSVM and KELM prediction models. The results show that PSOKELM prediction model is superior to PSOSVM prediction model in prediction speed and accuracy, and is superior to KELM prediction model in prediction accuracy. Based on the analysis of the location principle of the transient positive and negative pressure wave method and the denoising of the positive and negative pressure wave signal by the spatial correlation method of wavelet transform, a detection method of the catastrophe point of the positive and negative pressure wave based on wavelet packet pre-processing empirical mode decomposition and wavelet transform modulus maximum method (WPEMD-WTM) is proposed to determine the time of the positive and negative pressure wave. The validity and accuracy of this method are verified by simulation. Aiming at the alarming problem of the blocking fault of the coal slurry pipeline, the analysis method of abnormal blocking fault is studied. The prediction value and prediction interval are calculated, and the statistical characteristics of the pressure sample data are combined to judge the blocking fault. According to different levels, different safety control measures are adopted, and the rationality of the safety control method for blocking fault is verified. The pressure distribution model of the slurry pipeline proposed in this paper can provide a basis for pipeline design and paste pump selection; based on kernel function limit learning machine The blockage prediction model and the blockage location method based on wavelet analysis can provide a new safety control decision and means for the blockage fault of coal slurry transportation system.
【学位授予单位】：西安科技大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM621
，
本文编号：2220161
[Abstract]:Slime is a kind of coal waste, which is burned in a circulating fluidized bed boiler as fuel in a gangue power plant. Slime transportation is carried out by laying pipelines. The blockage of the pipelines has always been one of the important factors affecting the production efficiency and safety of the sludge transportation system. The operation performance of the feeding system is also an important issue to ensure the normal and safe operation of CFB boilers. This paper studies the prediction and location method of slurry pipeline blockage based on wavelet transform and extreme learning machine. The characteristics of field monitoring data are analyzed, and the missing data are complemented and the data are denoised by cubic exponential smoothing method and wavelet transform method. Good denoising effect has realized the pretreatment of monitoring data of slime transportation pipeline, and laid the foundation for modeling the pressure distribution in the later period, predicting and locating the blockage. The pressure distribution model of the complex pipeline (horizontal straight pipe, inclined pipe, vertical pipe) based on the mechanism analysis method is established. The force analysis of a section of slime in the pipeline is carried out, the force balance equation is established, and the mathematical model of the pressure distribution of the complex pipeline is obtained under the condition of nonlinear parameter constraints. The model obeys complex exponential relation and improves the commonly used calculation method in determining the friction coefficient of the model; analyzes the multi-variable influencing factors of pressure distribution, establishes the pressure distribution model of QGA-BP based on the quantum genetic algorithm (QGA) BP neural network. Kernel function extreme learning machine (PSOKELM) is used to predict the blockage of coal slurry pipeline. Based on the analysis of the blockage prediction mechanism of coal slurry pipeline, the characteristic quantity of blockage prediction is determined. The kernel function of support vector machine is introduced into the extreme learning machine, and the parameters are optimized by particle swarm optimization. The test data are simulated and compared with PSOSVM and KELM prediction models. The results show that PSOKELM prediction model is superior to PSOSVM prediction model in prediction speed and accuracy, and is superior to KELM prediction model in prediction accuracy. Based on the analysis of the location principle of the transient positive and negative pressure wave method and the denoising of the positive and negative pressure wave signal by the spatial correlation method of wavelet transform, a detection method of the catastrophe point of the positive and negative pressure wave based on wavelet packet pre-processing empirical mode decomposition and wavelet transform modulus maximum method (WPEMD-WTM) is proposed to determine the time of the positive and negative pressure wave. The validity and accuracy of this method are verified by simulation. Aiming at the alarming problem of the blocking fault of the coal slurry pipeline, the analysis method of abnormal blocking fault is studied. The prediction value and prediction interval are calculated, and the statistical characteristics of the pressure sample data are combined to judge the blocking fault. According to different levels, different safety control measures are adopted, and the rationality of the safety control method for blocking fault is verified. The pressure distribution model of the slurry pipeline proposed in this paper can provide a basis for pipeline design and paste pump selection; based on kernel function limit learning machine The blockage prediction model and the blockage location method based on wavelet analysis can provide a new safety control decision and means for the blockage fault of coal slurry transportation system.
【学位授予单位】：西安科技大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM621
，
本文编号：2220161
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