The literature contains many studies relating gas turbine axial compressor degradation with impurities entering the inlet flow path. The collision and adhering of these impurities onto compressor blades can produce both recoverable and non-recoverable degradation in the compressor, leading to lost overall efficiency. More recently, the topic of how to optimally schedule when compressor washes should occur has been investigated. In many of the scheduling optimization studies, the cost associated with the change in fuel flow for a given fouled state is characterized indirectly as part of an overall fuel calculation for a given fouled state. In this paper, we aim to quantitatively answer the question of what this small quantity of fuel due to compressor fouling actually is. Additionally, we show how this can be included in solving for optimal schedules of compressor washes and estimate the CO2 emissions attributable to compressor fouling. We begin with a look at the literature on compressor fouling to identify common methods and assumptions of how fouling can manifest or be imposed. We then derive a first principles, steady state equation to relate compressor performance parameters with fuel flow. We validate this equation at nominal conditions with data obtained from Solar Turbine’s test cells. Additionally, we develop a generic industrial gas turbine model using the Numerical Propulsion System Simulation software and apply the fouling constraints found from the literature review to run a fouling simulation. The results further validate our equation and provide insight into the quantity of fuel resulting from compressor fouling. We end the paper with a look at how the derived model can be used in the problem of optimizing compressor wash schedules as well as give an estimate on increases in CO2 emissions due to compressor fouling.
The intersection of machine learning methods and gas turbine sensor data has expanded rapidly in the last decade to include numerous applications of regression, clustering, and even neural network algorithms. Learning algorithms have pushed traditional engine health management into the realm of prognostic health management. This paper starts with a review of several common computational methods used to monitor the condition of gas turbines currently employed by both industry and academia. Sources of application of machine learning algorithms from outside the gas turbine industry are also brought in. Focus is generally placed on industrial gas turbines with an industry standard monitoring system. The authors explore beyond gas path analysis with a novel use of machine learning algorithms to engine component classification. The paper concludes with a case study of applying learning algorithms to machine data to identify different fuel valves.
Gas transmission networks have been and continue to be the most efficient method for transporting natural gas. As Hydrogen begins to emerge as one possible solution of renewable energy and starts mixing into gas networks, now more than ever, efficient operation is paramount. Part of the efficient pipeline operation puzzle is knowing how much power is available at a given compressor station, which constrains the head and flow that can be produced. This paper derives and implements driver constraints relating to gas pipeline optimization problems such as throughput maximization and compressor station power minimization at the pipeline level. In the literature the driver is often neglected in implementations of pipeline optimization problems. Often, only the compressor map is considered during the optimization procedure. In this work we develop the necessary physical relations and constraints between the gas compressor and driver. The addition of these constraints ensures that solutions are not only bounded by surge and stonewall curves as well as compressor speed, but also by available power from the driver given current ambient conditions. We also derive a method to fit constraints to observed data. The modified optimization problem results in more accurate solutions of pipeline optimization problems, giving operators more accurate data with which to make business decisions and operate pipelines.
Many of the components on a gas turbine are subject to fouling and degradation over time due to debris buildup. For example, axial compressors are susceptible to degradation as a result of debris buildup on compressor blades. Similarly, air-cooled lube oil heat exchangers incur degradation as a result of debris buildup in the cooling air passageways. In this paper, we develop a method for estimating the degradation rate of a given gas turbine component that experiences recoverable degradation due to normal operation over time. We then establish an economic maintenance scheduling model, which utilizes the derived rate and user input economic factors to provide a locally optimal maintenance schedule with minimized operator costs. The rate estimation method makes use of statistical methods combined with historical data to give an algorithm with which a performance loss rate can be extracted from noisy data measurements. The economic maintenance schedule is then derived by minimizing the cost model in user specified intervals and the final schedule results as a combination of the locally optimized schedules. The goal of the combination of algorithms is to maximize component output and efficiency, while minimizing maintenance costs. The rate estimation method is validated by simulation where the underlying noisy data measurements come from a known probability distribution. Then, an example schedule optimization is provided to validate the economic optimization model and show the efficacy of the combined methods.
Gas transmission networks have been and continue to be the most efficient method for transporting natural gas. As Hydrogen begins to emerge as one possible solution of renewable energy and starts mixing into gas networks, now more than ever, efficient operation is paramount. Part of the efficient pipeline operation puzzle is knowing how much power is available at a given compressor station, which constrains the head and flow that can be produced. This paper derives and implements driver constraints relating to gas pipeline optimization problems such as throughput maximization and compressor station power minimization at the pipeline level. In the literature the driver is often neglected in implementations of pipeline optimization problems. Often, only the compressor map is considered during the optimization procedure. In this work we develop the necessary physical relations and constraints between the gas compressor and driver. The addition of these constraints ensures that solutions are not only bounded by surge and stonewall curves as well as compressor speed, but also by available power from the driver given current ambient conditions. We also derive a method to fit constraints to observed data. The modified optimization problem results in more accurate solutions of pipeline optimization problems, giving operators more accurate data with which to make business decisions and operate pipelines.
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