Surge and stall are the two main types of instabilities that often occur on the compressor system of gas turbines. The effect of this instability often leads to excessive vibration due to the back pressure imposed to ...Surge and stall are the two main types of instabilities that often occur on the compressor system of gas turbines. The effect of this instability often leads to excessive vibration due to the back pressure imposed to the system by this phenomenon. In this work, fouling was observed as the major cause of the compressor instability. A step to analyze how this phenomenon can be controlled with the continuous examination of the vibration amplitude using a computer approach led to the execution of this work. The forces resulting to vibration in the system is usually external to it. This external force is aerodynamic and the effect was modeled using force damped vibration analysis. A gas turbine plant on industrial duty for electricity generation was used to actualize this research. The data for amplitude of vibration varied between -15 and 15 mm/s while the given mass flow rate and pressure ratio were determined as falling between 6.1 to 6.8 kg/s and 9.3 to 9.6 respectively. A computer program named VICOMS written in C++ programming language was developed. The results show that the machine should not be run beyond 14.0 mm vibration amplitude in order to avoid surge, stall and other flow-induced catastrophic breakdown.展开更多
The ability to predict the behaviour of a gas turbine engine and optimize its performance is critical in eco-nomic, thermal and condition monitoring studies. Having identified fouling as one of the major sources of co...The ability to predict the behaviour of a gas turbine engine and optimize its performance is critical in eco-nomic, thermal and condition monitoring studies. Having identified fouling as one of the major sources of compressor and therefore gas turbine deterioration, a computer-based engine model was developed to optimize the performance of gas turbines. The paper thus presents an analysis of compressor hand cleaning, on and offline compressor washing to actualize the technique using a computer program in Visual Basic programming language with data collected over a period of fifteen weeks for 2 gas turbine plants GT1 and GT2. The results of the data collected, when collated, shows that after washing, the overall operational efficiency changed from 39.2% to 46.25%. To optimize the performance of gas turbine engines, it is therefore recommended that operators should perform a combination of compressor hand cleaning, offline and online washing simultaneously.展开更多
Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and ...Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the compressor.In ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of machines.Furthermore,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor technologies.In this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM sensor.As the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and noise.The histogram of residuals can be used for sensor abnormality monitoring.In general,sensor states can be monitored by observing the shifting of the mean in the histogram of residuals.The sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition mean.This method is compact and would be relevant to monitor irregularity of the sensors.展开更多
文摘Surge and stall are the two main types of instabilities that often occur on the compressor system of gas turbines. The effect of this instability often leads to excessive vibration due to the back pressure imposed to the system by this phenomenon. In this work, fouling was observed as the major cause of the compressor instability. A step to analyze how this phenomenon can be controlled with the continuous examination of the vibration amplitude using a computer approach led to the execution of this work. The forces resulting to vibration in the system is usually external to it. This external force is aerodynamic and the effect was modeled using force damped vibration analysis. A gas turbine plant on industrial duty for electricity generation was used to actualize this research. The data for amplitude of vibration varied between -15 and 15 mm/s while the given mass flow rate and pressure ratio were determined as falling between 6.1 to 6.8 kg/s and 9.3 to 9.6 respectively. A computer program named VICOMS written in C++ programming language was developed. The results show that the machine should not be run beyond 14.0 mm vibration amplitude in order to avoid surge, stall and other flow-induced catastrophic breakdown.
文摘The ability to predict the behaviour of a gas turbine engine and optimize its performance is critical in eco-nomic, thermal and condition monitoring studies. Having identified fouling as one of the major sources of compressor and therefore gas turbine deterioration, a computer-based engine model was developed to optimize the performance of gas turbines. The paper thus presents an analysis of compressor hand cleaning, on and offline compressor washing to actualize the technique using a computer program in Visual Basic programming language with data collected over a period of fifteen weeks for 2 gas turbine plants GT1 and GT2. The results of the data collected, when collated, shows that after washing, the overall operational efficiency changed from 39.2% to 46.25%. To optimize the performance of gas turbine engines, it is therefore recommended that operators should perform a combination of compressor hand cleaning, offline and online washing simultaneously.
文摘Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the compressor.In ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of machines.Furthermore,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor technologies.In this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM sensor.As the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and noise.The histogram of residuals can be used for sensor abnormality monitoring.In general,sensor states can be monitored by observing the shifting of the mean in the histogram of residuals.The sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition mean.This method is compact and would be relevant to monitor irregularity of the sensors.
