Modeling, identification and detection of faults in industrial boiler(July2015)

Navaseelan Paul David, B. Swaminathan
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引用次数: 1

Abstract

The Boiler plays vital role in electric power plants, fertilizer industries, petrochemical and in other industries. In such industries the boiler actuates turbines, compressors for generating electric power, pneumatic power respectively. The overall operation and efficiency of any plant is depending on the quality of steam produced in terms of its flow rate, pressure and temperature and also reliability. Any kind of fault or failure of the boiler may reduce the quality of production and tend to shut down the operation of entire plant. Hence early detection of faults will enhance availability of steam and reduce plant shutdown. In order to diagnose the faults, complete operation of all the loops like feed water circuit, air fuel circuit, steam circuit and cooling water circuit are studied and possible failures at the input side, inside the boiler and output side of the boiler are studied. One such 130 tons per hour capacity water tube boiler in Petrochemical industry is studied. The required data for complete transient part of its operation is collected for identification of the boiler model. In this paper simple models using first principle balance equations were developed for the subsystems of the boiler like furnace, boiler tubes and drums and heat exchangers. The mathematical models are also obtained based on measured data during real time operation of the boiler. Then the parameters are identified by choosing proper model structure like non-linear ARX and Hammerstein-Wiener and it is validated with real time plant data. The model based fault detection using Kalman filter algorithm is presented in this paper among the different methods being practiced. In this method, Kalman filter estimates all the process variables at the input side, output side and inside the boiler. Residual is generated as the difference between measured and estimated values of these variables. If the residual generated surpasses threshold value indicates fault in the boiler.
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工业锅炉故障建模、识别与检测(2015.07)
锅炉在电厂、化肥、石油化工等行业中起着至关重要的作用。在这些工业中,锅炉分别驱动涡轮机、压缩机产生电力和气动动力。任何电厂的整体运行和效率都取决于其流量、压力和温度所产生的蒸汽质量以及可靠性。锅炉的任何一种故障或故障都可能降低生产质量,并有可能使整个工厂停产。因此,及早发现故障将提高蒸汽的可用性,减少工厂停工。为了诊断故障,研究了给水回路、空气燃料回路、蒸汽回路和冷却水回路等所有回路的完整运行情况,并研究了锅炉输入侧、锅炉内部和锅炉输出侧可能出现的故障。研究了一种130吨/小时容量的石油化工水管锅炉。收集了锅炉整个瞬态部分运行所需的数据,用于锅炉模型的识别。本文利用第一性原理平衡方程,建立了锅炉的炉膛、锅炉管鼓和换热器等子系统的简单模型。并根据锅炉实时运行的实测数据建立了数学模型。然后通过选择合适的非线性ARX和Hammerstein-Wiener模型结构进行参数辨识,并用现场实时数据进行验证。在现有的几种故障检测方法中,提出了基于模型的卡尔曼滤波故障检测方法。在该方法中,卡尔曼滤波对输入侧、输出侧和锅炉内部的所有过程变量进行估计。残差是这些变量的实测值和估计值之间的差值。如果产生的余量超过阈值,说明锅炉有故障。
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