表面式三段给水加热器简化传热模型
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摘要
热工系统的数值模型一直以来难以在实际生产过程中应用,主要由于模型复杂度高,计算量大,对计算精度与计算效率的要求难以协调。为了提高传统表面式三段给水加热器计算模型的综合性能,该文以疏水冷却段为主要分析对象,根据加热器壳侧水位确定疏冷段有效传热面积,建立了一种简化的给水加热器分段集总参数传热模型,且利用BP神经网络对对流传热系数的计算过程进行了优化。利用某600MW机组运行数据对模型进行了验证,且就计算速度的问题同常规分段参数模型进行了对比分析,结果表明,该模型在保证计算精度满足工程需求的情况下,具有较高的计算效率,且变工况适应性强,满足大型火电机组加热器实时性能监视的要求,大大提高了相关复杂系统传热模型的实用性。
The difficulty for the application of thermal system numerical model in the actual production process is mainly due to the complex, huge amount of calculation computation and the difficulty in coordination between the demand of calculation accuracy and computational efficiency. In order to improve the comprehensive performance of the traditional three-stage water supply heater calculation model, we took the drain water cooling section as the analysis object, determined effective heat transfer area based on the water level of the heater shell and establishd a simplified feedwater heater lumped parameter heat transfer model. Meanwhile, the calculation process of convective heat transfer coefficient was optimized by BP neural network.The model is validated by the operating data from a 600 MW unit, and the calculation efficiency of model was compared with the conventional segmentation parameter model. The results show that the model has high calculation efficiency, while the calculation accuracy is satisfied to the engineering requirements, and has adaptability to variable conditions, meeting the requirements of real-time performance monitoring on feed-water heater in large-scale thermal power units, improving the practicality of heat transfer model in relevant complex systems.
The difficulty for the application of thermal system numerical model in the actual production process is mainly due to the complex, huge amount of calculation computation and the difficulty in coordination between the demand of calculation accuracy and computational efficiency. In order to improve the comprehensive performance of the traditional three-stage water supply heater calculation model, we took the drain water cooling section as the analysis object, determined effective heat transfer area based on the water level of the heater shell and establishd a simplified feedwater heater lumped parameter heat transfer model. Meanwhile, the calculation process of convective heat transfer coefficient was optimized by BP neural network.The model is validated by the operating data from a 600 MW unit, and the calculation efficiency of model was compared with the conventional segmentation parameter model. The results show that the model has high calculation efficiency, while the calculation accuracy is satisfied to the engineering requirements, and has adaptability to variable conditions, meeting the requirements of real-time performance monitoring on feed-water heater in large-scale thermal power units, improving the practicality of heat transfer model in relevant complex systems.
引文
[1]杨涛,胥建群,周克毅,等.考虑水位影响的蒸汽动力发电机组给水加热器变工况特性[J].中国电机工程学报,2014,34(2):231-239.Yang Tao,Xu Jianqun,Zhou Keyi,et al.Varying condition characteristic of feedwater heater considering liquid level in steam power generating unit[J].Proceedings of the CSEE,2014,34(2):231-239(in Chinese).
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[11]虞佶,凌峰,顾琼彦.高压加热器传热系数计算方法优化[J].电站辅机,2012,33(2):1-5.Yu Ji,Ling Feng,Gu Qiongyan.Optimization of the calculating method for HP heater's heat transfer coefficient[J].Power Station Auxiliary Equipment,2012:33(2):1-5(in Chinese).
[12]杨世铭,陶文铨.传热学.第4版[M].高等教育出版社,2006:243-263.Yang Shiming,Tao Wenquan.Heat transfer theory[M].Beijing:Higher Education Press,2006:243-263(in Chinese).
[13]张燕秦,徐向东.用于热力系统建模的基于粗糙集的模糊神经网络[J].清华大学学报(自然科学版),2004,44(8):1083-1086.Zhang Yanqin,Xu Xiangdong.Rough set-based fuzzy-neural network model design for thermodynamic systems[J].Journal of Tsinghua University,2004,44(8):1083-1086(in Chinese).
[14]马良玉,马永光,王兵树.不同负荷下高加给水系统程度迥异故障诊断的新方法[J].中国电机工程学报,2010,30(2):115-121.Ma Liangyu,Ma Yongguang,Wang Bingshu.A new approach to diagnose variable-degree faults under different operating points for high-pressure feedwater heater system[J].Proceedings of the CSEE,2010,30(2):115-120(in Chinese).
[15]李欢欢,司风琪,徐治皋.一种基于鲁棒自联想神经网络的传感器故障诊断方法[J].中国电机工程学报,2012,32(14):116-121.Li Huanhuan,Si Fengqi,Xu Zhigao.A sensor fault diagnosis method based on robust auto-associative neural network[J].Proceedings of the CSEE,2012,32(14):116-121(in Chinese).
[16]王小川.MATLAB神经网络43个案例分析[M].北京航空航天大学出版社,2013:2-5.Wang Xiaochuan.Matlab neural network analysis of 43cases[M].Beihang University Press,2013:2-5(in Chinese).
[17]Heo G,Song K L.Internal leakage detection for feedwater heaters in power plants using neural networks[J].Expert Systems with Applications,2012,39(5):5078-5086.
[18]Kang Y K,Kim H,Heo G,et al.Diagnosis of feedwater heater performance degradation using fuzzy inference system[J].Expert Systems with Applications,2017,69:239-246.
[19]胡申华,严俊杰,李杨.二元混合蒸气凝结液界面温度的计算[J].中国电机工程学报,2011,31(11):68-73.Hu Shenhua,Yan Junjie,Li Yang.Interface temperature of condensate for binary mixtures[J].Proceedings of the CSEE,2011,31(11):68-73(in Chinese).
[20]陈西平,黄荣海,严俊杰,等.竖直微细管外酒精-水蒸气Marangoni凝结传热特性研究[J].工程热物理学报,2012,33(10):1731-1734.Chen Xiping,Huang Ronghai,Yan Junjie,et al.Research on Marangoni condensation heat transfer of ethanol-water vapor on external wall of micro-vertical tube[J].Journal of Engineering Thermophysics,2012,33(10):1731-1734(in Chinese).
[21]彭银波,张亚军,李卫华,等.雷诺数关系式对膜状凝结湍流区传热计算的影响[J].原子能科学技术,2017,51(7):1181-1187.Peng Yinbo,Zhang Yajun,Li Weihua,et al.Effect of reynolds correlations on heat tranfer calculation of film condensation in turbulent flow[J].Atomic Energy Science and Technology,2017,51(7):1181-1187(in Chinese).
[2]王妍,李颜,于军辉,等.基于人工神经网络的回热系统主要故障预测模型[J].热能动力工程,2011,26(4):424-427.Wang Yan,Li Yan,Yu Junhui,et al.Model for predicting the major faults of a regenerative system based on an artificial neural network[J].Journal of Engineering for Thermal Energy&Power,2011,26(4):424-427(in Chinese).
[3]董卫国,徐则民.火电厂给水加热器的运行、维护和检修[M].北京:中国电力出版社,1997:146-148.Dong Weiguo,Xu Zeming.Operation,maintenance and repair of thermal power plant feedwater heater[M].Beijing:China Electric Power Press,1997:146-148(in Chinese).
[4]蔡锡琮.高压给水加热器[M].北京:水利电力出版社,1995:136-138.Cai Xicong.High-pressure feed-water heater[M].Beijing:Hydraulic Power Press,1995:136-138(in Chinese).
[5]张欣刚,王雷,徐治皋,等.高压加热器泄漏的静态仿真计算及其故障特征分析[J].热力发电,2005,34(10):13-17.Zhang Xingang,Wang Lei,Xu Zhigao.et al.Static emulating calculating and fault feature analysis for highpressure Heater leakage[J].Thermal Power Generation,2005,34(10):13-17(in Chinese).
[6]Barszcz T,Czop P.A feedwater heater model intended for model-based diagnostics of power plant installations[J].Applied Thermal Engineering,2011,31(8-9):1357-1367.
[7]Hussaini I S,Zubair S M,Antar M A.Area allocation in multi-zone feedwater heaters[J].Energy Conversion&Management,2007,48(2):568-575.
[8]陈二燕,邢秦安,刘继平.回热加热器动态模型计算精度的对比研究[J].工程热物理学报,2013(7):1335-1338.Chen Eryan,Xing Qin’an,Liu Jiping.The comparison of the dynamic calculation accuracy about feedwater heater[J].Journal of Engineering Thermophysics,2013,34(7):1335-1338(in Chinese).
[9]丁常富,关鑫源,郭江龙.基于运行参数的加热器疏水端差简易计算模型[J].电站辅机,2011,32(3):102-104.Ding Changfu,Guan Xinyuan,Guo Jianglong.Simple calculation model of heater terminal temperature difference based on operating parameters[J].Power Station Auxiliary Equipment,2011(in Chinese).
[10]Hossienalipour S M,Karbalaee M S,Fathiannasab H.Development of a model to evaluate the water level impact on drain cooling in horizontal high pressure feedwater heaters[J].Applied Thermal Engineering,2016,110:590-600.
[11]虞佶,凌峰,顾琼彦.高压加热器传热系数计算方法优化[J].电站辅机,2012,33(2):1-5.Yu Ji,Ling Feng,Gu Qiongyan.Optimization of the calculating method for HP heater's heat transfer coefficient[J].Power Station Auxiliary Equipment,2012:33(2):1-5(in Chinese).
[12]杨世铭,陶文铨.传热学.第4版[M].高等教育出版社,2006:243-263.Yang Shiming,Tao Wenquan.Heat transfer theory[M].Beijing:Higher Education Press,2006:243-263(in Chinese).
[13]张燕秦,徐向东.用于热力系统建模的基于粗糙集的模糊神经网络[J].清华大学学报(自然科学版),2004,44(8):1083-1086.Zhang Yanqin,Xu Xiangdong.Rough set-based fuzzy-neural network model design for thermodynamic systems[J].Journal of Tsinghua University,2004,44(8):1083-1086(in Chinese).
[14]马良玉,马永光,王兵树.不同负荷下高加给水系统程度迥异故障诊断的新方法[J].中国电机工程学报,2010,30(2):115-121.Ma Liangyu,Ma Yongguang,Wang Bingshu.A new approach to diagnose variable-degree faults under different operating points for high-pressure feedwater heater system[J].Proceedings of the CSEE,2010,30(2):115-120(in Chinese).
[15]李欢欢,司风琪,徐治皋.一种基于鲁棒自联想神经网络的传感器故障诊断方法[J].中国电机工程学报,2012,32(14):116-121.Li Huanhuan,Si Fengqi,Xu Zhigao.A sensor fault diagnosis method based on robust auto-associative neural network[J].Proceedings of the CSEE,2012,32(14):116-121(in Chinese).
[16]王小川.MATLAB神经网络43个案例分析[M].北京航空航天大学出版社,2013:2-5.Wang Xiaochuan.Matlab neural network analysis of 43cases[M].Beihang University Press,2013:2-5(in Chinese).
[17]Heo G,Song K L.Internal leakage detection for feedwater heaters in power plants using neural networks[J].Expert Systems with Applications,2012,39(5):5078-5086.
[18]Kang Y K,Kim H,Heo G,et al.Diagnosis of feedwater heater performance degradation using fuzzy inference system[J].Expert Systems with Applications,2017,69:239-246.
[19]胡申华,严俊杰,李杨.二元混合蒸气凝结液界面温度的计算[J].中国电机工程学报,2011,31(11):68-73.Hu Shenhua,Yan Junjie,Li Yang.Interface temperature of condensate for binary mixtures[J].Proceedings of the CSEE,2011,31(11):68-73(in Chinese).
[20]陈西平,黄荣海,严俊杰,等.竖直微细管外酒精-水蒸气Marangoni凝结传热特性研究[J].工程热物理学报,2012,33(10):1731-1734.Chen Xiping,Huang Ronghai,Yan Junjie,et al.Research on Marangoni condensation heat transfer of ethanol-water vapor on external wall of micro-vertical tube[J].Journal of Engineering Thermophysics,2012,33(10):1731-1734(in Chinese).
[21]彭银波,张亚军,李卫华,等.雷诺数关系式对膜状凝结湍流区传热计算的影响[J].原子能科学技术,2017,51(7):1181-1187.Peng Yinbo,Zhang Yajun,Li Weihua,et al.Effect of reynolds correlations on heat tranfer calculation of film condensation in turbulent flow[J].Atomic Energy Science and Technology,2017,51(7):1181-1187(in Chinese).