This document summarizes a method for optimally scheduling gas turbine compressor washing to minimize fuel consumption and emissions. It describes a dynamic model for estimating compressor degradation over time using a Kalman filter. It then presents an economic optimization model that considers fuel and maintenance costs to determine the optimal schedule and types of compressor washes (online, offline, idle) to maximize efficiency. Simulations showed potential fuel and cost savings compared to a fixed maintenance schedule by optimizing the timing and types of washes over a one-year period.
This document summarizes an analysis of energy losses in compressors used at United Nigerian Textile Limited in Kaduna, Nigeria. The author conducted experiments on the compressors and simulated the results using Hysys simulation software. The experimental results found annual energy savings of 11,700 kWh/yr and cost savings of N99450/yr from reducing losses. Simulation results validated the experimental findings and determined additional compressor performance parameters such as a polytropic efficiency of 74.26%. The analysis provides a method for evaluating energy losses and savings in compressor systems.
The document experimentally investigates the performance, emissions, and combustion of a diesel engine operating in CNG-diesel dual fuel mode with varying CNG injection rates and operating pressures. Tests were conducted at 6 LPM and 13.5 LPM of CNG injected at diesel injection pressures of 200, 220, and 240 bar. Results show brake thermal efficiency increased with higher pressure but decreased with more CNG, while emissions varied with both factors. CO2 and NOx increased at 220 bar then decreased at 240 bar for all fuels, while CO and UHC decreased with higher pressure and more CNG substitution. Peak heat release rate was highest for pure diesel at 240 bar due to better atomization.
An Algorithm of a Convectional Factory Electric Tray Dryertheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Cooling Towers in Process Industries are part of Utilities design. As the name suggests their primary purpose is to provide cooling requirements to industrial hot water from unit operations & unit processes. Examples include chillers and air conditioners. The principle of operation is to circulate hot water through a tower and allow heat dissipation to the ambient. Cooling towers can operate by natural draft or forced draft methods wherein fans are used to increase heat transfer.
This document summarizes the design of an air conditioning system for cooling the cabin of a truck using an air refrigeration cycle. The system uses a turbocharger and waste exhaust gases from the truck engine. Atmospheric air is compressed using the turbocharger and sent to an intercooler to reduce its temperature. It is then expanded in a turbo-expander to further lower its temperature before being supplied to the truck cabin. Thermodynamic and heat transfer analyses are presented to evaluate the performance of the system components and the cooling capacity. The results show that the air refrigeration cycle can provide enough cooling to lower the truck cabin temperature by 10-15°C without significantly impacting engine performance.
Chemical Process Calculations – Short TutorialVijay Sarathy
Often engineers are tasked with communicating equipment specifications with suppliers, where process data needs to be exchanged for engineering quotations & orders. Any dearth of data would need to be computed for which process related queries are sometimes sent back to the process engineer’s desk for the requested data.
The following tutorial is a refresher for non-process engineers such as project engineers, Piping, Instrumentation, Static & Rotating Equipment engineers to conduct basic process calculations related to estimation of mass %, volume %, mass flow, actual & standard volumetric flow, gas density, parts per million (ppm) by weight & by volume.
Engineers often use softwares to perform gas compressor calculations to estimate compressor duty, temperatures, adiabatic & polytropic efficiencies, driver & cooler duty. In the following exercise, gas compressor calculations for a pipeline composition are shown as an example case study.
OPTIMIZATION OF COMPRESSED AIR IN THERMAL POWER PLANT-A NOVEL STUDYvishal laddha
1) The document discusses optimization of compressed air usage at a thermal power plant in India. It was observed that a significant portion (around 1/3rd) of total compressed air consumption was used for igniter cooling in the plant's boilers.
2) By improving the control system optimization of the air compressors and monitoring consumption patterns, the plant was able to reduce compressed air usage by 1135 cubic meters per hour. This led to estimated annual savings of 1.172 crore rupees.
3) The key optimization made was reducing the threshold current limit for the partial loaded air compressor, which improved efficiency. Monitoring consumption patterns also revealed igniter cooling as the major area for optimization, as it accounted
This document summarizes an analysis of energy losses in compressors used at United Nigerian Textile Limited in Kaduna, Nigeria. The author conducted experiments on the compressors and simulated the results using Hysys simulation software. The experimental results found annual energy savings of 11,700 kWh/yr and cost savings of N99450/yr from reducing losses. Simulation results validated the experimental findings and determined additional compressor performance parameters such as a polytropic efficiency of 74.26%. The analysis provides a method for evaluating energy losses and savings in compressor systems.
The document experimentally investigates the performance, emissions, and combustion of a diesel engine operating in CNG-diesel dual fuel mode with varying CNG injection rates and operating pressures. Tests were conducted at 6 LPM and 13.5 LPM of CNG injected at diesel injection pressures of 200, 220, and 240 bar. Results show brake thermal efficiency increased with higher pressure but decreased with more CNG, while emissions varied with both factors. CO2 and NOx increased at 220 bar then decreased at 240 bar for all fuels, while CO and UHC decreased with higher pressure and more CNG substitution. Peak heat release rate was highest for pure diesel at 240 bar due to better atomization.
An Algorithm of a Convectional Factory Electric Tray Dryertheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Cooling Towers in Process Industries are part of Utilities design. As the name suggests their primary purpose is to provide cooling requirements to industrial hot water from unit operations & unit processes. Examples include chillers and air conditioners. The principle of operation is to circulate hot water through a tower and allow heat dissipation to the ambient. Cooling towers can operate by natural draft or forced draft methods wherein fans are used to increase heat transfer.
This document summarizes the design of an air conditioning system for cooling the cabin of a truck using an air refrigeration cycle. The system uses a turbocharger and waste exhaust gases from the truck engine. Atmospheric air is compressed using the turbocharger and sent to an intercooler to reduce its temperature. It is then expanded in a turbo-expander to further lower its temperature before being supplied to the truck cabin. Thermodynamic and heat transfer analyses are presented to evaluate the performance of the system components and the cooling capacity. The results show that the air refrigeration cycle can provide enough cooling to lower the truck cabin temperature by 10-15°C without significantly impacting engine performance.
Chemical Process Calculations – Short TutorialVijay Sarathy
Often engineers are tasked with communicating equipment specifications with suppliers, where process data needs to be exchanged for engineering quotations & orders. Any dearth of data would need to be computed for which process related queries are sometimes sent back to the process engineer’s desk for the requested data.
The following tutorial is a refresher for non-process engineers such as project engineers, Piping, Instrumentation, Static & Rotating Equipment engineers to conduct basic process calculations related to estimation of mass %, volume %, mass flow, actual & standard volumetric flow, gas density, parts per million (ppm) by weight & by volume.
Engineers often use softwares to perform gas compressor calculations to estimate compressor duty, temperatures, adiabatic & polytropic efficiencies, driver & cooler duty. In the following exercise, gas compressor calculations for a pipeline composition are shown as an example case study.
OPTIMIZATION OF COMPRESSED AIR IN THERMAL POWER PLANT-A NOVEL STUDYvishal laddha
1) The document discusses optimization of compressed air usage at a thermal power plant in India. It was observed that a significant portion (around 1/3rd) of total compressed air consumption was used for igniter cooling in the plant's boilers.
2) By improving the control system optimization of the air compressors and monitoring consumption patterns, the plant was able to reduce compressed air usage by 1135 cubic meters per hour. This led to estimated annual savings of 1.172 crore rupees.
3) The key optimization made was reducing the threshold current limit for the partial loaded air compressor, which improved efficiency. Monitoring consumption patterns also revealed igniter cooling as the major area for optimization, as it accounted
This document analyzes and compares the performance of gasoline direct injection (GDI) engines to port fuel injection (PFI) engines using a two-region thermodynamic model. The analysis examines parameters such as pressure, fuel consumption, and work produced during the compression and expansion phases. Key findings include:
1) GDI engines produce considerable work compared to PFI engines, despite higher fuel consumption.
2) A two-region thermodynamic model is used that divides the cylinder volume into burned and unburned regions to model combustion.
3) The analysis finds that while GDI engines have higher fuel utilization, they still generate significant work relative to PFI engines.
Nonlinear multivariable control and performance analysis of an air-handling unitAli Abedi
This document summarizes a study that investigates nonlinear control approaches for an air-handling unit (AHU). A nonlinear multi-input multi-output dynamic model of an AHU is developed. Both indoor temperature and relative humidity are controlled by manipulating air and cold water flow rates. Two nonlinear control approaches, gain scheduling and feedback linearization, are designed and compared. Results show feedback linearization achieves faster tracking of setpoints but with more overshoot, while gain scheduling has less oscillation in control inputs and likely lower energy use. Feedback linearization is also more robust to model parameter uncertainty.
The document analyzes the impact of high ambient temperatures on the performance of gas turbine power plants in tropical climate zones. It presents a mathematical model to study the effect of ambient temperature on a gas turbine plant in northern Saudi Arabia. The model shows about a 20% reduction in power output when temperatures rise from the design condition of 15°C to actual summer highs of 50°C. The document then evaluates the economic justification for adding an absorption chiller to the plant to recover lost power, finding the payback period would be 1.14 years or less. It recommends gas turbines with inlet air cooling for future plants in hot climates.
A QUICK ESTIMATION METHOD TO DETERMINE HOT RECYCLE REQUIREMENTS FOR CENTRIFUG...Vijay Sarathy
Turbomachinery Engineers often conduct studies to determine if a hot gas bypass is required for a given centrifugal compressor system. This would mean building a process model and simulating it for Emergency Shutdown conditions (ESD) & Normal Shutdown conditions (NSD) to check if the compressor operating point crosses the surge limit line (SLL). A quick estimation method that uses dimensionless number called the inertia number can be used to check prior to the study, if a Hot gas bypass (a.k.a. Hot Recycle) is required in addition to an Anti-surge line (ASV or a.k.a Cold Recycle).
OPTIMIZATION OF AN OPEN CYCLE GAS TURBINE POWER PLANT USING EXERGOECONOMICSijmech
The purpose of current study is to analyze the performance of an open cycle gas turbine power plant using
the concepts of exergoeconomics. Exergoeconomic technique involves the use of Second law of
thermodynamics and assigns monetary values to the thermodynamic quantity known as exergy. Analyses
based on exergoeconomic criteria are done for the open cycle gas turbine power plant turbine. The
methodology is illustrated using the example of a 25 MW open cycle gas turbine power plant. Optimization
has been done for the open cycle gas turbine power plant as tradeoffs between the unit product cost of the
compressor and combustion chamber as functions of compressor pressure ratio and unit product costs of
combustion chamber and gas turbine as functions of turbine inlet temperature.
Theoritical investigations of injection pressure in a four stroke di diesel e...IAEME Publication
This document discusses theoretical investigations of injection pressure in a four-stroke diesel engine using alcohol as fuel. A computer program was developed to simulate the engine's performance with diesel and at different alcohol injection pressures ranging from 180 to 160 bar. The results showed that an injection pressure of 165 bar for alcohol produced higher brake thermal efficiency and indicated thermal efficiency that were closest to diesel fuel. It was concluded that alcohol can be used in diesel engines with an injection pressure of 165 bar as it allows an optimum amount of alcohol to be injected while compensating for alcohol's low viscosity.
This document summarizes an article from the International Journal of Mechanical Engineering and Technology. It describes the development of a mathematical and simulation model to analyze the effects of engine design parameters on the performance and emissions of spark ignition engines. The model can be used to simulate engine operation and assist with engine design. It examines three engine designs (under square, square, and over square) running on gasoline, LPG, and CNG. The model calculates output power, efficiency, fuel consumption, ignition delay, exhaust temperature, heat loss, and exhaust emissions for each design/fuel combination. It found square engines (stroke/diameter ratio of 1) to be the most efficient and suitable design overall.
This document provides an overview of energy recovery wheels, which are devices that transfer energy between exhaust and supply air streams in HVAC systems. It discusses the principles of sensible and total heat transfer using energy recovery wheels and how they can improve energy efficiency. The document also covers factors like effectiveness, airflow balancing, air transfer between streams, frost control methods, and limitations of using energy recovery wheels with hazardous exhaust air.
Basic Unit Conversions for Turbomachinery Calculations Vijay Sarathy
Turbomachinery equipment like centrifugal pumps & compressors have their performance stated as a function of Actual volumetric flow rate [Q] & Head [m/bar]. The following tutorial describes how pump/compressor head can be expressed in energy terms as ‘kJ/kg’. Turbomachinery head expressed in kJ/kg describes, how many kJ of energy is required to compress 1 kg of gas for a given pressure ratio. The advantage of using energy terms to estimate absorbed power is that it is based on the amount of ‘mass’ compressed which is independent of pressure and temperature of a fluid.
This document summarizes the results of a thermal analysis of a gas turbine power plant to improve performance efficiency. The analysis considers three gas turbine cycles: simple cycle, intercooled cycle, and regenerative cycle. For the intercooled cycle, the analysis found that thermal efficiency increases with higher intercooler effectiveness and decreases with higher ambient temperature. Compressor work also decreases with higher intercooler effectiveness. For the regenerative cycle, thermal efficiency decreases with higher ambient temperature and lower regenerator effectiveness. The regenerative cycle has higher thermal efficiency than the simple cycle under the same operating conditions. The analysis provides equations to calculate key performance parameters for optimizing efficiency.
The mean key variable to control the burner performance for safe and efficient operations is the amount of excess air or oxygen which flow to react with fuel. In accordance with Typical Draft Profile in a Natural Draft furnace the numerical results of computing fluid dynamics model of the air flow from the environment to the burner by adjusting the area of air box prototype openings will give the necessary information to adjust the performance of burner as optimum. The model of air box prototype is useful tool in applying similarity theory on another burners̕ designs which depends the air box tools to calibrate the quantity of combustion air flow to the burner.
ELECTRIC MOTOR (EM) DRIVEN COMPRESSOR IN ASPEN HYSYS DYNAMICSVijay Sarathy
Aspen HYSYS Dynamics provides the option of simulating centrifugal compressors with an Electric Motor (EM). In order to setup the electric motor, the following calculations can be made to arrive at the Electric Motor Input data in Aspen HYSYS Dynamics.
OPERATING ENVELOPES FOR CENTRIFUGAL PUMPSVijay Sarathy
The following tutorial provides a step by step procedure to predict the allowable operating range or “Operating Envelope” for a centrifugal pump’s range of operation.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Use of Hydrogen in Fiat Lancia Petrol engine, Combustion Process and Determin...IOSR Journals
To our path towards green economy, Hydrogen is often regarded to have a potential growth in the
coming future. However, the high cost of operation of fuel cell has often been a setback. If we could make use of
hydrogen gas as a fuel directly, the scope of development broadens. Owing to these aspects, this work primarily
focuses on the simulation technique of an Internal Combustion Spark Ignition Engine powered by Hydrogen gas.
The simulations of various stages have been carried out using the discrete approach, thereby investigating the
pressures and temperatures at various instants in the cycle. For the relative performance discussion we have
simulated the different cycles as ideal cycle, air fuel cycle and actual cycle. The resultant cyclic graph indicates
various discrepancies between ideal, air fuel and actual cycle. This analysis serves as a tool for a better
understanding of the variables involved and helps in optimizing engine design and fixing of various parameters,
including the determination of valve timings. Besides this, backfire, is the commonly faced problem with the
hydrogen engines. To reduce this effect, a fuel injectoris used for adding the gaseous fuel to the combustion
chamber.
ECONOMIC INSULATION FOR INDUSTRIAL PIPINGVijay Sarathy
Thermal Insulation for Industrial Piping is a common method to reduce energy costs in production facilities while meeting process requirements. Insulation represents a capital expenditure & follows the law of diminishing returns. Hence the thermal effectiveness of insulation needs to be justified by an economic limit, beyond which insulation ceases to effectuate energy recovery. To determine the effectiveness of an applied insulation, the insulation cost is compared with the associated energy losses & by choosing the thickness that gives the lowest total cost, termed as ‘Economic Thickness’.
The following tutorial provides guidance to estimate the economic thickness for natural gas piping in winter conditions as an example case study.
Investigation of particulate control in thermal power plant usingIAEME Publication
This document analyzes particulate control in a thermal power plant using an electrostatic precipitator (ESP). It investigates ESP performance at the Korba East Phase III power plant in India. Mathematical models are used to predict emission levels based on ESP dimensions and migration velocity. The models show that higher migration velocity and fly ash percentage lead to lower required collection area to meet emissions standards. Upgrading ESPs with advanced control systems is a more cost effective solution than increasing ESP size.
Java gas turbine simulator engine component mathematica models by john a. reedJulio Banks
This document describes the mathematical models used in the Java Gas Turbine Simulator. It presents the one-dimensional, lumped parameter thermodynamic models for various engine components including compressors, combustors, turbines, ducts, and mixing volumes. For each component, the relevant conservation equations for mass, momentum and energy are presented along with equations for determining properties of the working fluid such as temperature, pressure and enthalpy. Curve fits of data are used to calculate fluid properties. The component models are combined to simulate the overall propulsion system.
Optimization of performance and emission characteristics of dual flow diesel ...eSAT Journals
Abstract
Depleting sources of fossil fuels coupled with after effects of exhaust gases on environment i.e. global warming and climate change has necessitated the need for development and use of alternate biodegradable fuels. In this present study optimization of performance and emission characteristics has been carried out using dual flow of CNG and Diesel with varying EGR under varying load by Taguchi method. Optimum values of output response parameters have been calculated with the help of regression equation and influence of various factors on output response has carried out with the help of analysis of variance.
Keywords: Taguchi method, CNG, EGR, biodegradable fuels
Gas Compression Stages – Process Design & OptimizationVijay Sarathy
The following tutorial demonstrates how to estimate the required number of compression stages and optimize the individual pressure ratio in a multistage centrifugal compression system.
Energy conservation in compressed air systemsmohammed kazi
The document analyzes the efficiency of compressed air systems (CAS) through an exergy analysis. It finds that while dryers and regulators have acceptable efficiencies, the compressor efficiency is low and application efficiencies are very poor, resulting in most energy being wasted. The critical point for efficiency is the application side. Future optimization should focus on applications, where the largest potential for energy savings exists. A case study of one company found their leak rates were initially overestimated but were reduced through improved modeling and measurement techniques.
This paper investigates excessive fuel consumption of
compressor drivers caused by common compressor faults.
Pressure versus volume (PV) analysis techniques will identify
deficiencies, quantify fault severity, and will be used to
estimate the resulting excessive fuel consumption. Empirical
fuel measurements of the drivers are analyzed before and
after the fault correction and are used to calculate immediate
economic savings from repairs. Performance and capacity
improvements are also analyzed, providing a complete economic
picture of maintenance and operational payback.
This document analyzes and compares the performance of gasoline direct injection (GDI) engines to port fuel injection (PFI) engines using a two-region thermodynamic model. The analysis examines parameters such as pressure, fuel consumption, and work produced during the compression and expansion phases. Key findings include:
1) GDI engines produce considerable work compared to PFI engines, despite higher fuel consumption.
2) A two-region thermodynamic model is used that divides the cylinder volume into burned and unburned regions to model combustion.
3) The analysis finds that while GDI engines have higher fuel utilization, they still generate significant work relative to PFI engines.
Nonlinear multivariable control and performance analysis of an air-handling unitAli Abedi
This document summarizes a study that investigates nonlinear control approaches for an air-handling unit (AHU). A nonlinear multi-input multi-output dynamic model of an AHU is developed. Both indoor temperature and relative humidity are controlled by manipulating air and cold water flow rates. Two nonlinear control approaches, gain scheduling and feedback linearization, are designed and compared. Results show feedback linearization achieves faster tracking of setpoints but with more overshoot, while gain scheduling has less oscillation in control inputs and likely lower energy use. Feedback linearization is also more robust to model parameter uncertainty.
The document analyzes the impact of high ambient temperatures on the performance of gas turbine power plants in tropical climate zones. It presents a mathematical model to study the effect of ambient temperature on a gas turbine plant in northern Saudi Arabia. The model shows about a 20% reduction in power output when temperatures rise from the design condition of 15°C to actual summer highs of 50°C. The document then evaluates the economic justification for adding an absorption chiller to the plant to recover lost power, finding the payback period would be 1.14 years or less. It recommends gas turbines with inlet air cooling for future plants in hot climates.
A QUICK ESTIMATION METHOD TO DETERMINE HOT RECYCLE REQUIREMENTS FOR CENTRIFUG...Vijay Sarathy
Turbomachinery Engineers often conduct studies to determine if a hot gas bypass is required for a given centrifugal compressor system. This would mean building a process model and simulating it for Emergency Shutdown conditions (ESD) & Normal Shutdown conditions (NSD) to check if the compressor operating point crosses the surge limit line (SLL). A quick estimation method that uses dimensionless number called the inertia number can be used to check prior to the study, if a Hot gas bypass (a.k.a. Hot Recycle) is required in addition to an Anti-surge line (ASV or a.k.a Cold Recycle).
OPTIMIZATION OF AN OPEN CYCLE GAS TURBINE POWER PLANT USING EXERGOECONOMICSijmech
The purpose of current study is to analyze the performance of an open cycle gas turbine power plant using
the concepts of exergoeconomics. Exergoeconomic technique involves the use of Second law of
thermodynamics and assigns monetary values to the thermodynamic quantity known as exergy. Analyses
based on exergoeconomic criteria are done for the open cycle gas turbine power plant turbine. The
methodology is illustrated using the example of a 25 MW open cycle gas turbine power plant. Optimization
has been done for the open cycle gas turbine power plant as tradeoffs between the unit product cost of the
compressor and combustion chamber as functions of compressor pressure ratio and unit product costs of
combustion chamber and gas turbine as functions of turbine inlet temperature.
Theoritical investigations of injection pressure in a four stroke di diesel e...IAEME Publication
This document discusses theoretical investigations of injection pressure in a four-stroke diesel engine using alcohol as fuel. A computer program was developed to simulate the engine's performance with diesel and at different alcohol injection pressures ranging from 180 to 160 bar. The results showed that an injection pressure of 165 bar for alcohol produced higher brake thermal efficiency and indicated thermal efficiency that were closest to diesel fuel. It was concluded that alcohol can be used in diesel engines with an injection pressure of 165 bar as it allows an optimum amount of alcohol to be injected while compensating for alcohol's low viscosity.
This document summarizes an article from the International Journal of Mechanical Engineering and Technology. It describes the development of a mathematical and simulation model to analyze the effects of engine design parameters on the performance and emissions of spark ignition engines. The model can be used to simulate engine operation and assist with engine design. It examines three engine designs (under square, square, and over square) running on gasoline, LPG, and CNG. The model calculates output power, efficiency, fuel consumption, ignition delay, exhaust temperature, heat loss, and exhaust emissions for each design/fuel combination. It found square engines (stroke/diameter ratio of 1) to be the most efficient and suitable design overall.
This document provides an overview of energy recovery wheels, which are devices that transfer energy between exhaust and supply air streams in HVAC systems. It discusses the principles of sensible and total heat transfer using energy recovery wheels and how they can improve energy efficiency. The document also covers factors like effectiveness, airflow balancing, air transfer between streams, frost control methods, and limitations of using energy recovery wheels with hazardous exhaust air.
Basic Unit Conversions for Turbomachinery Calculations Vijay Sarathy
Turbomachinery equipment like centrifugal pumps & compressors have their performance stated as a function of Actual volumetric flow rate [Q] & Head [m/bar]. The following tutorial describes how pump/compressor head can be expressed in energy terms as ‘kJ/kg’. Turbomachinery head expressed in kJ/kg describes, how many kJ of energy is required to compress 1 kg of gas for a given pressure ratio. The advantage of using energy terms to estimate absorbed power is that it is based on the amount of ‘mass’ compressed which is independent of pressure and temperature of a fluid.
This document summarizes the results of a thermal analysis of a gas turbine power plant to improve performance efficiency. The analysis considers three gas turbine cycles: simple cycle, intercooled cycle, and regenerative cycle. For the intercooled cycle, the analysis found that thermal efficiency increases with higher intercooler effectiveness and decreases with higher ambient temperature. Compressor work also decreases with higher intercooler effectiveness. For the regenerative cycle, thermal efficiency decreases with higher ambient temperature and lower regenerator effectiveness. The regenerative cycle has higher thermal efficiency than the simple cycle under the same operating conditions. The analysis provides equations to calculate key performance parameters for optimizing efficiency.
The mean key variable to control the burner performance for safe and efficient operations is the amount of excess air or oxygen which flow to react with fuel. In accordance with Typical Draft Profile in a Natural Draft furnace the numerical results of computing fluid dynamics model of the air flow from the environment to the burner by adjusting the area of air box prototype openings will give the necessary information to adjust the performance of burner as optimum. The model of air box prototype is useful tool in applying similarity theory on another burners̕ designs which depends the air box tools to calibrate the quantity of combustion air flow to the burner.
ELECTRIC MOTOR (EM) DRIVEN COMPRESSOR IN ASPEN HYSYS DYNAMICSVijay Sarathy
Aspen HYSYS Dynamics provides the option of simulating centrifugal compressors with an Electric Motor (EM). In order to setup the electric motor, the following calculations can be made to arrive at the Electric Motor Input data in Aspen HYSYS Dynamics.
OPERATING ENVELOPES FOR CENTRIFUGAL PUMPSVijay Sarathy
The following tutorial provides a step by step procedure to predict the allowable operating range or “Operating Envelope” for a centrifugal pump’s range of operation.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Use of Hydrogen in Fiat Lancia Petrol engine, Combustion Process and Determin...IOSR Journals
To our path towards green economy, Hydrogen is often regarded to have a potential growth in the
coming future. However, the high cost of operation of fuel cell has often been a setback. If we could make use of
hydrogen gas as a fuel directly, the scope of development broadens. Owing to these aspects, this work primarily
focuses on the simulation technique of an Internal Combustion Spark Ignition Engine powered by Hydrogen gas.
The simulations of various stages have been carried out using the discrete approach, thereby investigating the
pressures and temperatures at various instants in the cycle. For the relative performance discussion we have
simulated the different cycles as ideal cycle, air fuel cycle and actual cycle. The resultant cyclic graph indicates
various discrepancies between ideal, air fuel and actual cycle. This analysis serves as a tool for a better
understanding of the variables involved and helps in optimizing engine design and fixing of various parameters,
including the determination of valve timings. Besides this, backfire, is the commonly faced problem with the
hydrogen engines. To reduce this effect, a fuel injectoris used for adding the gaseous fuel to the combustion
chamber.
ECONOMIC INSULATION FOR INDUSTRIAL PIPINGVijay Sarathy
Thermal Insulation for Industrial Piping is a common method to reduce energy costs in production facilities while meeting process requirements. Insulation represents a capital expenditure & follows the law of diminishing returns. Hence the thermal effectiveness of insulation needs to be justified by an economic limit, beyond which insulation ceases to effectuate energy recovery. To determine the effectiveness of an applied insulation, the insulation cost is compared with the associated energy losses & by choosing the thickness that gives the lowest total cost, termed as ‘Economic Thickness’.
The following tutorial provides guidance to estimate the economic thickness for natural gas piping in winter conditions as an example case study.
Investigation of particulate control in thermal power plant usingIAEME Publication
This document analyzes particulate control in a thermal power plant using an electrostatic precipitator (ESP). It investigates ESP performance at the Korba East Phase III power plant in India. Mathematical models are used to predict emission levels based on ESP dimensions and migration velocity. The models show that higher migration velocity and fly ash percentage lead to lower required collection area to meet emissions standards. Upgrading ESPs with advanced control systems is a more cost effective solution than increasing ESP size.
Java gas turbine simulator engine component mathematica models by john a. reedJulio Banks
This document describes the mathematical models used in the Java Gas Turbine Simulator. It presents the one-dimensional, lumped parameter thermodynamic models for various engine components including compressors, combustors, turbines, ducts, and mixing volumes. For each component, the relevant conservation equations for mass, momentum and energy are presented along with equations for determining properties of the working fluid such as temperature, pressure and enthalpy. Curve fits of data are used to calculate fluid properties. The component models are combined to simulate the overall propulsion system.
Optimization of performance and emission characteristics of dual flow diesel ...eSAT Journals
Abstract
Depleting sources of fossil fuels coupled with after effects of exhaust gases on environment i.e. global warming and climate change has necessitated the need for development and use of alternate biodegradable fuels. In this present study optimization of performance and emission characteristics has been carried out using dual flow of CNG and Diesel with varying EGR under varying load by Taguchi method. Optimum values of output response parameters have been calculated with the help of regression equation and influence of various factors on output response has carried out with the help of analysis of variance.
Keywords: Taguchi method, CNG, EGR, biodegradable fuels
Gas Compression Stages – Process Design & OptimizationVijay Sarathy
The following tutorial demonstrates how to estimate the required number of compression stages and optimize the individual pressure ratio in a multistage centrifugal compression system.
Energy conservation in compressed air systemsmohammed kazi
The document analyzes the efficiency of compressed air systems (CAS) through an exergy analysis. It finds that while dryers and regulators have acceptable efficiencies, the compressor efficiency is low and application efficiencies are very poor, resulting in most energy being wasted. The critical point for efficiency is the application side. Future optimization should focus on applications, where the largest potential for energy savings exists. A case study of one company found their leak rates were initially overestimated but were reduced through improved modeling and measurement techniques.
This paper investigates excessive fuel consumption of
compressor drivers caused by common compressor faults.
Pressure versus volume (PV) analysis techniques will identify
deficiencies, quantify fault severity, and will be used to
estimate the resulting excessive fuel consumption. Empirical
fuel measurements of the drivers are analyzed before and
after the fault correction and are used to calculate immediate
economic savings from repairs. Performance and capacity
improvements are also analyzed, providing a complete economic
picture of maintenance and operational payback.
The document discusses pneumatic calculations for an LSF PU Hall. It covers calculating key parameters like pressure, flow rate, temperature and air quality. Specific calculations covered include air production ratio, pressure drop, air purification percentage, air consumption, required pressure and temperature at different stages. The document also provides calculations for specific pneumatic components and their air consumption. It analyzes the current system and provides solutions to improve efficiency, such as installing an air dryer and active carbon filters. Maintaining data through regular analysis of air supply, quality, consumption and leak detection is recommended to optimize the system.
This document discusses low cost automation using pneumatic systems. It begins with an overview of automation and pneumatics, explaining that pneumatics can provide low cost automation solutions through reducing labor costs, machine investment costs, and increasing productivity. The document then covers various pneumatic components and applications, advantages and disadvantages of pneumatics, pneumatic standards, classifications of pneumatic elements, and examples of pneumatic circuits.
The document discusses opportunities to improve compressed air efficiency at National Engineering Industries Ltd. It identifies several areas for focus: reducing air leakage, improving cleaning/air blowing processes, upgrading machinery to reduce air usage, regulating air pressure and flow, measuring air usage, and training staff. A survey found significant potential savings through initiatives in these areas, like installing valves to isolate air when machines are off, regulating pressure and nozzle size for air blowing, and improving machinery design. Implementation of the recommendations following a PDCA process could realize substantial cost reductions through more efficient compressed air usage.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Performance Gain for Multiple Stage Centrifugal Compressor by usi.pdfbui thequan
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Scheduling of Gas Turbine Compressor Washing
G. Hovland & M. Antoine
To cite this article: G. Hovland & M. Antoine (2006) Scheduling of Gas Turbine
Compressor Washing, Intelligent Automation & Soft Computing, 12:1, 63-73, DOI:
10.1080/10798587.2006.10642916
To link to this article: http://dx.doi.org/10.1080/10798587.2006.10642916
Published online: 01 Mar 2013.
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3. 64 Intelligent Automation and Soft Computing
regular intervals depending on prices of power sold and fuel purchased. The idle state (option D) keeps the
gas turbine running without producing power. This mode can be more economical than a plant shutdown
when the idle period is short. This paper addresses the scheduling problem (options A, B, C and D) of
compressor washing taking maintenance and fuel costs into account.
Figure 1 below shows an overview of the technologies used for the economic scheduling of
compressor washing. The complete optimisation solution consists of several parts which will be briefly
described in this paper. Section 2 presents a relatively simple dynamic model of a compressor. The
compressor is modelled by its input-output behaviour. The model presented relates the state variables
(massflow and pressure) to measured temperatures and the unknown parameter (isentropic efficiency).
Section 3 briefly describes the Kalman filter approach for estimating the time-varying efficiency level.
Section 4 presents a linearised fuel benefit model while section 5 presents the hybrid dynamic model of the
compressor. This model is the link between the physical model and the economic model. The model is
called hybrid since boolean decision variables are introduced in addition to the physical variables of
massflow and pressure. The boolean variables link the economics with the physics. In section 6 a model
predictive optimisation is described. The set of boolean variables is duplicated for each time step, in our
case the time step is one day. The optimisation computes the optimal sequence of the boolean decision
variables from the start to the end of the optimisation period. Section 7 illustrates the economical benefits
of the proposed optimisation. A compressor washing scheduling is performed over a time period of one
year and potential savings compared to a fixed maintenance approach are calculated. The work presented
in this paper is related to the work presented in [3] and [11] where lifetime consumption models and
economic mixed-integer optimisation were used to determine production levels of combined-cycle power
stations.
Figure 1. Overview of methods and models for economic scheduling of compressor washing.
2. COMPRESSOR EFFICIENCY MODEL
The model used consists of two nonlinear ordinary-differential-equations (ODEs) and a static
relationship between isentropic (or polytropic) efficiency, ambient conditions and temperature after the
compressor, see [5]. The model has the following form:
( )pP
L
A
dt
df
ac
c
−= '1 (1)
4. Scheduling of Gas Turbine Compressor Washing 65
( )'
2
01
ambt
p
Ppkf
V
a
dt
dp
−−= (2)
−
=
κ
κ 1
exp
'
'
amb
ambis
P
p
TT (3)
( ) ''
1
' ambambis
is
ac TTTT +−=
η
(4)
where A1 (cross-sectional area), Lc (length), Vp (volume) and kt (friction flow coefficient) are constant
parameters of the compressor. a01 and κ are the sonic air velocity and ratio of specific heats for air,
respectively. The dynamic states f and p are the compressor massflow and pressure. P’amb and T’amb are
ambient pressure and temperature, P’ac and T’ac are pressure and temperature at the compressor outlet. Tis
and ηis are the compressor’s isentropic temperature and efficiency.
The superscripts’ indicate that the measured pressure or temperature has been corrected for ambient
conditions. In order to compare efficiency for different days and reliably evaluate degradation, the
measured temperatures and pressures must be normalised to a reference condition. This reference condition
is usually selected as standard conditions for dry air, see for example [10]. The following variables are
introduced for the standard conditions:
T0 = 287.15 (K)
P0 = 101325 (Pa)
κ0 = 1.4
where T0 is standard temperature, P0 is standard pressure and κ0 is the standard ratio of specific heats. The
following correction procedure is suggested in [10]: First the temperature ratio θ and the pressure ratio δ
are defined.
θ = Tamb / T0
δ = Pamb / P0
The corrected temperatures and pressures are then given by:
0' T
T
T amb
amb ==
θ
(5)
0' P
P
P amb
amb ==
δ
(6)
θ
ac
ac
T
T =' (7)
5.3/)1(
0 1
1
1'
−
−
+=
− κκ
θκ
κ ac
ac
P
P (8)
κ at the compressor outlet is computed from a standard gas table from measured temperature, pressure and
the weight fraction of vapour in the intake air.
The goal of the Kalman filter to be presented in the next section, is to accurately estimate the
compressor isentropic efficiency ηis given the dynamic compressor model and corrected measurements of
temperature and pressure after the compressor.
3. PARAMETER ESTIMATION VIA KALMAN FILTERING
It should be noted that given measurements of temperature and pressure both before and after the
compressor, it is possible to calculate the compressor efficiency directly. However, any noise present in
these measurements will directly influence the efficiency. The Kalman filter significantly reduces the noise
5. 66 Intelligent Automation and Soft Computing
in the efficiency estimate by calculating the isentropic efficiency from the estimated pressure state.
Moreover, the efficiency estimate can be further improved if additional dynamics are introduced for
combustion and turbine stages. Such models would link additional plant measurements with the states
(massflow and pressure) of the compressor.
In order to use the Kalman filter, the model described in section 2 must first be converted to the
following form:
),,( PUXF
dt
dX
= (9)
),,( PUXHY = (10)
where X = (f,p) is the state vector, U = (Tamb’,Pamb’, Pac’) is the input vector, Y = (Tac’) is the measurement
vector and P = (ηis) is the vector of unknown time-varying parameters. Given U and Y, the different
variants of the Kalman filter simultaneously estimate the state vector X and the parameter vector P.
The papers and patents [1], [4], [6], [7] and [8] describe four different variants of the Kalman filter for
continuous tracking of unknown variables, such as the isentropic efficiency, based on nonlinear ODEs. The
four variants are the Extended Kalman Filter (EKF), two Unscented Kalman Filters (UKF) and the
Adaptive Extended Kalman Filter (AEKF). Each of these filters has benefits in different situations. For the
work presented in this paper we have used the EKF. We have found the UKF versions to be better suited
for strong nonlinear models, but the drawback is a higher computational effort compared to the EKF. See
our paper [7] for an extensive comparison of these filters on power plant models. The output of the
parameter estimation is a complete mapping of compressor efficiency (estimated parameter) vs. massflow
and pressure ratios (estimated states). This information is used to find the benefits of online and offline
washes as measured by the efficiency. In addition, the Kalman filters are used to estimate the degradation
of the efficiency during normal plant operation.
Figure 2 shows two different corrected estimates of isentropic efficiency vs. the compressor ratio Pac’ /
Pamb’ for a 300MW combined-cycle power plant. The upper curve was estimated one week after the lower
curve. During the week between the two estimates, the power plant was down for maintenance. The
estimated improvement in isentropic efficiency is about 2% over the entire range of pressure ratios.
Figure 2. Two different efficiency estimates.
4. FUEL BENEFIT MODEL
The optimisation algorithm requires a model of the potential fuel benefits of a washing as a function of
the current compressor efficiency. To compute the benefit in fuel savings after a washing, we derive the
following relations based on the power and isentropic efficiency equations.
6. Scheduling of Gas Turbine Compressor Washing 67
)(
1
Oc
g
fuel PP
C
f += (11)
gaambacc CfTTP )( −= (12)
)(
1
ambis
is
ambac TTTT −=−
η
(13)
where Pc is the power consumed by the compressor, Po is the power output from the plant, Cg is the heat
value of the fuel, fa is the airflow, Tis is the isentropic temperature and ηis is the isentropic efficiency.
If an offline washing is made, the efficiency increases from η to η2 with the following factor.
η
η2
1 =k (14)
Then, the following fuel benefit can be shown from the equations above.
−=∆ 1
1
1kC
P
f
g
c
fuel (15)
Since k1 is a non-linear function of the state variables η and η2, we can not implement this benefit
function directly. Instead, we make the following approximations.
( )2
1
1
2
2
11
2
1
2
1
01
ηη
ηη
ηη
η
η
η
η
−+≈
−
+=
−+==
k
(16)
( )21
1
02
ηη
η
γ
−+≈
k
(17)
where 1/k1 and 1/k2 are the approximated efficiency benefits for offline and online washings, respectively.
The approximations above are valid when η2 ≈ η0, where η0 is the nominal compressor efficiency level.
The fuel benefit function for online washing (k2) is shown in Figure 3. Note that after an offline wash, the
efficiency level is restored to the maximum level and the fuel benefit is reduced to zero.
Figure 3. Efficiency (Top) and Fuel Benefit Model (Bottom).
7. 68 Intelligent Automation and Soft Computing
5. HYBRID DYNAMIC MODEL
The hybrid dynamic model consists of the 14 boolean and continuous variables described below:
δ1 Boolean: Online washing state (0 or 1)
δ2 Boolean: Offline washing state (0 or 1)
δ3 Boolean: Normal operation (0 or 1)
δ4 Boolean: Idle state (0 or 1)
δ5 Boolean: Help state 1 (0 or 1)
δ6 Boolean: Help state 2 (0 or 1)
η Continuous: Compressor efficiency (0 to 1)
η2 Continuous: Recoverable efficiency (0 to 1)
α Continuous: Degradation rate of η
α2 Continuous: Degradation rate of η2
z1 Continuous: Cost of online washing
z2 Continuous: Cost of offline washing
z3 Continuous: Help variable for α
z4 Continuous: Fuel cost due to degradation
In addition to the 14 states, the model consists of a set of linear constraints and an optimisation
criterion. By using only linear constraints, the optimisation problem can be solved by a Mixed-Integer-
Linear-Program (MILP) solver. Examples of efficient commercial solvers for MILP problems are CPLEX
and Xpress-MP. Nonlinear constraints can often be converted to linear constraints by introducing help
variables, such as δ5 and δ6.
The model has a state machine of 4 main boolean states (δ1, δ2, δ3, δ4). The system will always be in
only one of these 4 states. The linear constraint describing this behaviour, is simply δ1 + δ2 + δ3 + δ4 = 1. In
addition to this constraint, the hybrid model consists of the following 22 logical constraints:
1. IF α≤0 THEN δ5=0 ELSE δ5=1
2. IF α2≤0 THEN δ6=0 ELSE δ6=1
These two constraints relate the boolean variables δ5 and δ6 to the continuous variables α and α2.
3. IF δ1=1 THEN αi+1 = α0
4. IF δ1=1 THEN α2i+1 = α2i - ε2δ6
5. IF δ1=1 THEN ηi+1 = ηi + γ(η2i+1 - ηi)
6. IF δ1=1 THEN η2i+1 = η2i - α2i
These four constraints describe the system after an online wash. The variable α, which describes the
degradation rate of the compressor efficiency η, is reset to it’s initial value. The variables η2 and α2
describe the non-recoverable degradation of a compressor. These variables always decrease, except
when the compressor is in the idle state. The non-recoverable efficiency can be thought of as the
efficiency level of a perfectly clean but ageing compressor. Constraint 5 models the increase in
efficiency after an online wash. The constant parameter γ (between 0 and 1) models the effectiveness
of an online wash. Online washes usually only cleans the first rows of compressor blades before the
water and chemicals evaporate. Hence, the blades at the compressor outlet will not be clean and the
compressor efficiency is not restored to the maximum value. As will be seen from the next set of
constraints, the maximum efficiency that can be restored, is given by the recoverable level η2.
7. IF δ2=1 THEN αi+1 = α0
8. IF δ2=1 THEN α2i+1 = α2i - ε2δ6
9. IF δ2=1 THEN ηi+1 = η2i+1
10. IF δ2=1 THEN η2i+1 = η2i - α2i
These four constraints describe the system after an offline wash and look very similar to the equations
for the online wash. The only difference is the behaviour of η which is restored to the maximum level
given by the recoverable efficiency η2.
11. IF δ3=1 THEN αi+1 = αi - εδ5
12. IF δ3=1 THEN α2i+1 = α2i - ε2δ6
13. IF δ3=1 THEN ηi+1 = ηi – z3
14. IF δ3=1 THEN η2i+1 = η2i – α2i
8. Scheduling of Gas Turbine Compressor Washing 69
These four constraints describe the degradation of the compressor efficiency during normal operation.
z3 is a help variable that prevents subtracting a negative value from the efficiency η. The non-
recoverable degradation is usually much slower and α2 is unlikely to become negative during a typical
optimisation horizon of 10-20 days. Hence, a similar help variable to z3 is not needed for η2.
15. IF δ4=1 THEN αi+1 = αi
16. IF δ4=1 THEN α2i+1 = α2i
17. IF δ4=1 THEN ηi+1 = ηi
18. IF δ4=1 THEN η2i+1 = η2i
These four constraints describe the behaviour of the efficiency levels in the idle state. All values
remain at their previous level.
19. z1 = δ1 P1
20. z2 = δ2 P2
21. z3 = δ1 α
22. z4 = P5(η2i - ηi)
The constraints 19, 20 and 22 describe the costs associated with the various states. The constants P1
and P2 are the cost associated with chemicals, labour and lost power production of online and offline
washes, respectively. P5 describes the cost of the extra fuel needed to operate the compressor at an
efficiency level below the recoverable level η2.
The logical propositions above consist of IF-THEN-ELSE statements and multiplications of a boolean
and a continuous variable. These types of logical constraints can easily be converted to a set of linear
constraints, see for example [2]. Automatic tools for converting logical constraints to linear constraints
exist, for example HYSDEL from ETH [9].
Examples of the two most common logical propositions are given below.
Proposition 1: Product z = δf(x), where δ is boolean and f(x) continuous, is equivalent to:
0max ≤− δfz (18)
0min ≤+− δfz (19)
minmin )( fxffz −≤−− δ (20)
maxmax )( fxffz ≤++− δ (21)
where fmin < f(x) < fmax.
Proposition 2: IF f(x)<0 THEN δ=0 ELSE δ=1 is equivalent to:
minmin )()( fxff −≤−+− δκ (22)
κδκ ≤+−− )()( max xff (23)
where κ is a small positive number.
6. MODEL PREDICTIVE CONTROL
Model predictive control (MPC) can effectively be used together with a linear hybrid dynamic model.
In our application, the 14 hybrid dynamic model states are duplicated for each future time step in the MPC
controller, as illustrated in Figure 4. The time steps for the compressor washing application are days. Day 2
represents the unknown states for tomorrow, day 3 for the following day, etc. Day N represents the last day
considered by the MPC optimiser. N is referred to as the MPC horizon. Several variables have to be
predicted from day 1 to day N. In the compressor washing application, these variables are P1, P2 and P5
which include future fuel and power prices.
Day number 1 represents the initial state of the plant. For example, if the plant operates in normal
production, δ3=1 and δ1=δ2=δ4=0 for day 1. The continuous efficiency variable η for day 1 is set to the
9. 70 Intelligent Automation and Soft Computing
Figure 4. Duplication of hybrid model states.
current estimate from the Kalman filter. The variables corresponding to the degradation rates and the
recoverable efficiency level are based on parameter estimates on historic data including a number of online
and offline washes.
Given all the initial states, the 23 iterative logical constraints from section 4 relate all future states to
the states for Day 1. The logical constraints will eliminate the majority of all possible state sequence
combinations.
The optimisation problem can be stated as follows:
Minimise Σ z1 + z2 + z4
Subject to the 23 constraints from section 4
where the sum is taken over days 1 to N. Given the linear objective function and the fact that the 23 logical
constraints can be formulated as linear constraints, the above problem can be formulated as a MILP. Since
the logical constraints are iterative, it would be possible to formulate all constraints as functions of the
initial states for day 1 and hence avoid the duplication of states. This is the approach taken by the Hysdel
software from ETH [9]. However, because of the efficient pre-solve features of many MILP solvers, the
solution times of the condensed MILP and the duplicated MILP are similar.
7. BENEFIT ANALYSIS
In this section we briefly present the economic benefits that can be expected from the optimised
compressor washing schedule compared to a fixed schedule. Figure 5 and Figure 6 show forecasted fuel
and power prices in US$/MWh. These prices are used at daily intervals for the future days 1 to N by the
MPC optimiser. Figure 7 shows the optimised compressor efficiency level.
The solid line in Figure 7 illustrates the recoverable efficiency level η2. The curve below the solid line
illustrates the variable η. The discrete jumps in the efficiency level are caused by online washes. During
this one year prediction, the plant has a continuous 100% power production and no planned shutdowns.
Hence, no offline washes are chosen due to the excessive costs associated with plant shutdowns and lost
power production.
It is also worth noting that during periods of high fuel costs, the MPC optimiser suggests a higher
frequency of online washes than during periods of low fuel costs.
Table I shows the economic results of the MPC optimised compressor washing schedule compared to
fixed schedules. If no washing is performed, the efficiency level deteriorates and the cost of 100% refers to
the cost of extra fuel to maintain power production at 100%. When performing online washes every day,
the cost of 49% mainly refers to chemicals and labour. When performing washes every Sunday, the costs
are a combination of extra fuel costs and chemical and labour costs associated with the washing operation.
As can be seen from Table I, the MPC optimised schedule is clearly better than any of the fixed
schedules. Because of high volatility of fuel and power prices, no fixed washing schedule is expected to be
close to MPC optimised schedules. The economic benefit figures in Table I were forecasted for a 300MW
10. Scheduling of Gas Turbine Compressor Washing 71
Figure 5. Forecasted fuel prices for one year ahead.
Figure 6. Forecasted power prices for one year ahead.
Figure 7. Optimised compressor efficiency level.
11. 72 Intelligent Automation and Soft Computing
Table I. Relative Costs Associated with the Different Washing Schedules
Wash Frequency Relative Costs
MPC Optimiser 34%
No Washing 100%
Online Wash Every Day 49%
Online Wash Every Sunday 62%
power plant that runs at 100% power production level for one year. The benefits are expected to be
somewhat lower for plants that have regular shutdowns, for example on weekends. On such days, offline
washes are obvious maintenance operations that can be performed at very low additional cost. However,
during periods of high fuel prices, it can still be of significant economic benefit to perform online washes
between fixed offline washing schedules.
Compared to simpler washing strategies, the optimisation approach presented in this paper provides
several benefits. For example, instead of scheduling washes immediately before expected increases in gas
prices, the MPC approach takes several other factors into account, such as power prices and costs of the
scheduling operations. In addition, the MPC handles multiple price events inside the MPC horizon. For
example, if there is a significant expected increase in gas prices followed by a significant decrease a few
days later, it is not easy to determine manually what the optimal scheduling sequence would be. Scheduling
based on fixed degradation rates will also perform worse than the MPC approach, as degradation depends
heavily on varying daily and seasonal conditions, such as ambient temperatures and pollutants in the air.
By updating the efficiency levels based on plant measurements, the accuracy of the models is significantly
improved. Without accurate efficiency models, the MPC approach will only yield sub-optimal washing
schedules. Figure 7 illustrates the benefits of the MPC approach. When fuel is relatively cheap (days 0 to
100), washing schedules are performed infrequently and mainly determined by the amount of real-time
degradation. When fuel is expensive (days 200 to 250) the scheduling is performed more frequently.
Finally, when power prices are high (day 360), the compressor efficiency is allowed to drop lower than
normal (0.7% below the non-recoverable level, vs. 0.3% under normal conditions).
8. CONCLUSIONS
In this paper a new approach to scheduling of online and offline compressor washing has been
presented. The new method is based on a Model Predictive Control (MPC) optimisation that takes expected
future fuel and power prices into account. The MPC optimiser is built on a hybrid dynamic systems model
that describes the natural degradation of compressor efficiency and the discrete jumps in efficiency after
washing operations. The hybrid dynamic model contains discrete states for the operational mode of the
compressor and continuous states for the efficiency levels. The parameters of the efficiency model are
estimated from thermodynamic models and the extended Kalman filter based on historic plant data. The
proposed scheduling approach clearly shows the achievable economical benefits compared to the
traditional way of scheduling, especially for plants that are in continuous operation.
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ABOUT THE AUTHORS
G. Hovland received the MSc degree from the Norwegian University of Science and
Technology, 1993, and the PhD degree from the Australian National University, 1997.
From 1997 to 2003 he worked as a research scientist at ABB Corporate Research in
Oslo (Norway), Västerås (Sweden) and Baden (Switzerland). Since January 2004 he
has been at the University of Queensland, Australia as a senior lecturer in the areas of
mechatronics, automation and control systems.
M. Antoine received the MSc degree in mechanical engineering from the University
of Brussels, Belgium, in 1982. He joined Virginia Polytechnic Institute, Blacksburg,
as research assistant in the Department of Aerospace and Ocean Engineering. In
1985, he joined the Brown Boveri control systems division, Baden, Switzerland.
Currently product manager at ABB Power Technology Systems, Baden, where he is
responsible for plant monitoring and optimization systems.