Methods and Tools For Analysis and Optimization of Power Plants
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Mohsen Assadi
Division of Power Plant Technology Department of Heat and Power Engineering Lund Institute of Technology
Doctoral Thesis Methods and Tools for Analysis and !., , .: .,.... ,,, , Optimization of Power Plants L:, .,,,,, ,,y~.,,, .: ::, + ,.
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Mohsen Assadi
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Akademisk avhandling ,, som for avlaggande av teknisk doktorsexamen vid tekniska fidculteten vid Lunds Universitet kommer att firsvaras vid offentlig disputation .,,,. fiedagen den 6 oktober 2000 kl. 1015 i sal M:B, M-huse; Ole R6mers v~g 1, Lunds Tekniska Htigskola ----- A————— . ——. —.. . —-—. .-. --.....—-–—. ------
Olganimioss DOcmneutname LUND UNIVERSITY DOcrOIULDISSERTATION DivisionofThermalPowerEngineering Date of issue DepartmentofHeatandPowerEngirteeting September11,2000 LundInstituteofTechnology COD8M ISR3’JLUTIvlDN/TMVK--lO2l--SE Author(s) Spoosxingorganization Mohsenkisadi Titleandsubtitle MethodsandToolsforAnalysisandOptimiition ofPowerPlants Abstract
Modemsncieties’timctiorralityisstronslydependentontheelectricity.Eilicientjenvironmentfriendly, and economical pnwer productionhasbeeninfocus for a Iong time. The introduction of computers and drereby computer-aided tools for pm-design studies,optimization and choice of the best opcmtinnal strategies, haa changed the conditions for pnwer production tremendously. The moat noticeable advantage of the intmducrinn nf the computer-aidedteds in the field of power generation, haa been the ability enstudy the plant’s performanceprior tn the construction phase. The results of these studies have nrade it pnasible to change and adjust the plant layout to snatchthe pre-detined requirements. Further developmentof computem in recent yeara haa opened up for implementationof new features in the existing tools and also for the development of new tnols for specific applications, like thernmdynamic and economic optimization, prediction nf the refraining component life time, and fault diagnostics, resulting in impmvenrent of the plant’s performance, availability and reliability. ? The mnat common tmls for predeaign sludies am heat and mass balance programs. Further thermodynamic and economic optimization of plant layouta, generad by the heat and mass balance programs, ean be accomplished by using pinch pmgmms, esersy analysis and tieorro=onotica. Suweihnceand fault diagnostics nf existing systems can be performed by using tnnls like cmufitionmonitoringsystems and artificial neural networks. The increased number of tools and their various cnnatructionand application arms make tbe choice of the most adequate tnol for a cefiin application difiiculL In this thesis the development of different categories of tnnls and tec~lques, and their application arm am reviewed and presented. Case studies on both existing and theoretical power plant layouts have been performed using different commercially available tools to illuminate their advantagea and shortcomings. Tbe development of pnwer plant technologyand the rqdremurts for new tnola and measurementsystems have been briefly reviewed. This thesis contains alan programming techniques and calculation methods concerning part-load calculations using Ineal Iinearizatinn, which has been implemented in an in-house heat and rnaaa balance pmgmm developed by the author. Results nf calculations performed by the in-home pmgmm have been compared with results fmm cnmmcrcial pmgmnrs. The comparison shows gond cmraiaterrcy[1, 2]. Methcds suggested by the author increase the numerical stability, reduce the calculation time, and impmve the user-fiiendlinesaby facilitating free choice of input data.
Key words Pre-design, Heat and massbalance,Gashsnbine ~=tioa systemand/orindexterms(iiany)
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——— -———-—--—. —-—-. .—- Methods and Tools for Analysis and Optimization of Power Plants
Mohsen Assadi
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LUND UNIVERSITY Lund Institute of Technology
October 2000 Doctoral Thesis Division of Power Plant Technology Department of Heat and Power Engineering Lund Institute of Technology SE-221 00 Lund$weden .-.
Mohsen Assadi ISBN 91-628-4372-9 ISSN 0282-1990 ISRN LUTMDN/TMVK-102 l-SE Printed in Sweden, KFS AB Lund 2000 DISCLAIMER
Portions of this document may be illegible in electronic image products. Images are produced from the best available original document.
—.— Abstract
Modern societies’ fimctionality is strongly dependent on the electricity. Efficient environment friendly, and economical power production has been in focus for a long time. The introduction of computers and thereby computer-aided tools for pre-design studies, optimization and choice of the best operational strategies, has changed the conditions for power production tremendously. The most noticeable advantage of the introduction of the computer-aided tools in the field of power generation, has been the ability to study the plant’s petiormance prior to the construction phase. The results of these studies have made it possible to change and adjust the plant layout to match the pre-defined requirements.
Further development of computers in recent years has opened up for implementation of new features in the existing tools and also for the development of new tools for specific applications, like thermodynamic and economic optimization, prediction of the remaining component life time, and fault diagnostics, resulting in improvement of the plant’s performance, availability and reliability.
The most common tools for pre-design studies are heat and mass balance programs. Further thermodynamic and economic optimization of plant layouts, generated by the heat and mass balance programs, can be accomplished by using pinch programs, exergy analysis and thermoeconomics. Surveillance and fault diagnostics of existing systems can be performed by using tools like condition monitoring systems and artificial neural networks.
The increased number of tools and their various construction and application areas make the choice of the most adequate tool for a certain application difficult. In this thesis the development of different categories of tools and techniques, and their application area are reviewed and presented. Case studies on both existing and theoretical power plant layouts have been performed using different commercially available tools to illuminate their advantages and shortcomings. The development of power plant technology and the requirements for new tools and measurement systems have been briefly reviewed.
This thesis contains also programmingg techniques and calculation methods concerning part-load calculations using local linearization, which has been implemented in an in- house heat and mass balance program developed by the author. Results of calculations performed by the in-house program have been compared with results from commercial programs. The comparison shows good consistency [1, 2]. Methods suggested by the author increase the numerical stability, reduce the calculation time, and improve the user-friendliness by facilitating fi-eechoice of input data. ——--— -.—— ————.—.———. —— . . .- .
ii List of Papers
1. Assadi M., Johansson K. B., Applying Pinch Method and ExergY Analysis to a BIO- IGIL4T Power Plant, 2nd Co&erence on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction, PRES’99, pp. 139-144, Published by Hungerian Chemical Society, Budapest, Hungary, May 31 - June 2, 1999.
2. Assadi M., H5gglund T., A computational investigation of a combined cycle using an SI-engine, a Rankine bottoming cycle and a fuel cell, European Fuel cell news, Newsletter of the European Fuel Cell Group, Ltd., Volume 6, Number 2, pp. 5-7, July 1999.
3. Assadi M., Jansson S. A., Blomstedt M., Increasing thermal eficiency of a PFBC power plant using a natural gas fueled gas turbine, The First lntemationa Symposium on Computer Aided Process Engineering, ISCAPE 2000, Cartagena de india, Colombia, January 24-28,2000.
4. Assadi M., Torisson T., Integration of biomass-fueled power plants and SI-en@”nes, a method for increasing power output from existing plants, 4ti International Cotierence of Iranian Society of Mechanical Engineers, ISME 2000, pp. 409-412, Tehran, Iran, May 16-19,2000.
5. Arriagada J., Assadi M., Air bottoming cycle for Gas Turbines, 4ti International Conference of Iranian Society of Mechanical Engineers, ISME 2000, pp. 447-454, Tehran, IrarL May 16-19,2000.
6. Assadi M., Hildebrandt A., A Computational Investigation of a Biomass Fueled Integrated Gasljication Cascaded Humid Air Turbine, Bio-IGCIiMT, to be presented at 14* International Congress of Chemical Engineering, QUBEC 2000, Sao Paulo, Brazil, September 24-27,2000.
7. Assadi M., Mesbahi E., Torisson T., Lindquist T., Arriagada J., Olausson P., A Novel Correction Technique for Simple Gas Turbine Parameters, submitted to ASME TURBOEXPO 2001, New Orleans, USA.
8. Mesbahi E., Assadi M., Torisson T., Lindquist T., A Unique Correction Technique for Evaporative Gas Turbine (EvGT) Parameters, submitted to ASME TURBOEXPO 2001, New Orleans, USA.
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iv \
Acknowledgements
This work was earned out at the Department of Heat and Power Engineering. I would like to express my gratitude to all colleagues at the department. I would like to especially thank my supervisor Professor Tord Torisson for his support, encouragement and important suggestions and my old friends and colleagues Ph. D. Jens IUingmann and Ph. D. Hamid Nasri for all interesting discussions, Ph.D. Ehsan Mesbahi, and M.SC. Pernilla Olausson for their comments to the thesis. I would also like to thank my firmc6e Nada and my daughter Anahita for their support and patience.
This research was financed by the National Swedish Board for Industrial and Technical Development and the National Swedish Energy Authority (STEM). Their financial support is gratefully acknowledged. .- .-——.. .—— —— —.——— ...... — . ...-
Nomenclature
ABC: Air Bottoming Cycle ANN: Artificial Neural Network CC: Combined Cycle CHAT: Cascaded Humid Air Turbine CHP: Combined Heat and Power CMS: Condition Monitoring System DS: Directionally Solidified EvGT: Evaporative Gas Turbine FC: Fuel Cell GT: Gas Turbine HAT: Humid Air Turbine HEN: Heat Exchanger Network HMBP: Heat and Mass Balance Program HRSG: Heat Recovery Steam Generator PFBC: Pressurized Fluidized Bed Combined cycle RC: Rankine Cycle SI-engine: Spark Ignition Engine SOFC: Solid Oxide Fuel Cell STIG: Steam Injected Gas Turbine TBC: Thermal Barrier Coating TJT: Turbine Inlet Temperature TNUIC Total Number of UnKnowns
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vi Contents
Nomenclature ...... vi 1. Introduction ...... 1 t. 1.1. Objectives...... 2 1.2. Limitations ...... 2 1.3. Metiods ...... 3 1.4. Dispositionof the thesis...... 3 2. Power plant systems ...... 5 2.1. Steamcycle ...... 5 2.2. Gas cycle ...... 6 2.3. Combined cycle ...... 6 2.4. New concepts ...... 7 3. Gas turbine development ...... 9 3.1. Introduction...... 10 3.2. Blade cooling ...... ll 3.3. Materials development...... 13 3.4. Thermal barrier coatings ...... l4 4. Tools and methods for power plant analyses and optimization ...... 17 4.1. Heat and mass balance programs (HMBPs) ...... 17 4.1.1. General layout ...... 18 4.1.2. Mathematical calculation methods ...... 19 4.1.3. Program structure ...... 19 4.1.4. Part-load calculations ...... 21 4.2. Optimization ...... 23 4.2.1. Parameter stidy ...... 23 4.2.2. Pinch tecbolo~ ...... 24 4.2.3. Exergy and tiemoeconotics ...... 25 4.2.4. Condition monitoring and artificial neural networks ...... 26 5. Discussion and future work ..*....eo.o.e.*..*.eo.e*ee*.e*...... 29 6. Conclusions ...... 33 7. Summary of papers ...... 35 Appendix 1...... 41 Appendix 2.e.**o...... ***...... *...****...... 45 Reference list ...... 51
vii 1. Introduction
The fimctionality of modem societies is strongly dependent on the electrical power. Today’s generation of heat and power is dominated by fossil fuel based energy conversion technology. Major fuel resources for combustion based energy conversion are oil, gas and coal. The major drawback for this type of technology is the unavoidable emission of harrnfid species to the environment. Emissions related to combustion based processes, such as S0. and NO., cause acid rain, and emission of C02 is believed to contribute to global warming. Emission reduction techniques have been in focus for many years now. Technical developments concerning combustion devices have resulted in lower emissions from all kinds of fossil fueled power plants. During the last decades two approaches for emission reduction have been usedj namely increased power plant efficiency, and reduced emission formation during the combustion process.
To be able to get a perspective over the research field in the area of power generation until today, and to identi~ research areas for the foreseeable fhture, it is necessary to understand the mechanisms affecting the heat and power generation market.
The higher I%el price after the oil crisis, the enormous development of personal computers, the end of the cold war, global climate changes and the deregulation of the electricity market are some of the most important factors that have had a large influence on the development of the electricity generation devices and related research field.
Power plant efficiency enhancement is important for two reasons: . economizing the world’s limited fossil fuel reserves, and . lowering the specific emission per produced power unit.
Increased power plant efficiency has been possible due to both sofhvare development, resulting in more effective tools for pre-design studies, and hardware development allowing for higher firing temperatures.
The software development experienced a boom during the late 1980s and early 1990s because of the revolutionary development of personal computers. More flexible and powerful tools for theoretical pre-design studies like heat and mass balance programs and three-dimensional flow path analysis became available. Using these tools has made it possible to design and analyze power plant solutions prior to the construction phase, so that the most effective and cost-optimal solution can be selected.
Afier the end of the cold war, advanced materials and cooling technologies used in military aircraft was made available for industrial gas turbines. They resulted in a major efficiency increase in gas turbine based power plants, mainly due to a higher allowable maximum temperature. A combination of advanced technologies, three-dimensional flow path analysis and gas turbine adjustment to combined cycle plants have resulted in electrical efficiencies in the order of 60°/0. Further increases in plant efficiency, emission control, availability and condition based maintenance are (most likely) going to dominate power generation research areas in the near future. The development of computer capacity and speed of data processing are opening up continuous operation surveillance and data acquisition. It is necessary for the competitiveness of advanced power generation units to keep the availability high and the maintenance costs low. It is also important to be able to handle an enormous amount of data in an intelligent way, to predict faults before they occur, and to estimate the time to the next stop for maintenance.
Development of tools for analysis and optimization of power plants has been highly affected by changes in the condition of the global market. Comprehension of the sof?svaredevelopment process requires knowledge of demands and abilities of the actual time period. Therefore an analysis of computer aided tools and their development, isolated from the development of engine components and materials and changes in economical conditions of the global market, will be incomplete. Chapter three deals with gas turbine development in recent years, and is included in this thesis to illuminate new demands for analysis tools that have arisen from the technical development of power plant systems.
This thesis mostly illuminates the following two research areas: . the development of calculation programs for pre-design studies, with suggestions for further improvement of these tools, and ● new tools for continuous surveillance and data analysis that increase plant reliability and availability, and decrease maintenance costs.
1.1. Objectives
The objective of this thesis is to contribute to deeper understanding of methods and tools for analysis and optimization of power plant systems at the pre-design stage and at the operational phase, and to develop more effective methods for par-load calculation of steam and gas based power plant systems in a heat and mass balance progmm.
1.2. Limitations
The methods and tools studied in this thesis are based on thermodynamic and economical parameters. Reliability related methods, based on material properties and life time, are discussed but not taken into consideration in the studied tools. Tools based on three-dimensional calculations and dynamic simulation are outside the scope of this work.
2 1.3. Methods
The present work started with a literature study over relevant tools. The literature study has been reported in the authors Iicentiate thesis [1]. An in-house program for heat and mass balance calculations at both design point and part-load was developed by the author, and compared to other commercial heat and mass balance programs [1, 2]. The part-load calculation metho~ used in the in-house program, is unique and was published for the first time in the author’s licentiate thesis. The calculation method is summarized in chapter 4 and in appendix 2 in this thesis.
In order to illuminate the requirements for the analysis tools, not only at the present time but also in the fiture, a study concerning the development of hardware in steam and gas cycles, as well as in power plants based on new concepts, has been performed.
The theoretical work in the thesis has been supplemented by experimental data from the gas turbine laboratory at the department, and the existing power plants in Europe and in the USA.
Many of the articles in this thesis have been written by the author in close co-operation with colleagues from industry and other universities. Co-authors from Sydkrafi ABB Stal, Wartsila NSD, Hannofer University and Newcastle University are represented in the papers.
1.4. Disposition of the thesis
A short introduction and the scope of the work are presented in chapter one.
In chapter two, there is a brief description of the systems to which the studied methods and tools are applied.
Chapter three presents a survey of the interesting hardware development affecting the future development of the methods and tools studied in the thesis.
The most important concept analysis tools, i.e. the heat and mass balance programs, are discussed in chapter four. This chapter also includes a brief review of power plant optimization and operational tools.
Discussion and fhture work are presented in chapter five, and conclusions are presented in chapter six.
Chapter seven provides a discussion about the papers this thesis is based on.
At the end of the thesis, there are eight papers enclosed in the form they have been published. Papers seven and eight focus on the operational phase of the power plants, while the first six papers deal with problems in the retrofit and pre-design phase.
2. Power plant systems
Power plant systems can be divided into two major categories, steam and gas cycles. Power cycles consist of different components comected to each other, like pump, compressor, heat exchanger, expander, etc. The simplified working principle of a power cycle is tha~ a working medium is pressurized, heated up, and expanded, thereby genemting power.
Power cycles of different types have been described and analyzed in many books [3,4, 5]. In this chapter, some of these cycles are briefly described.
2.1. Steam cycle
The simplest steam cycle using water as the working medium, the Rankine Cycle (RC), consists of a pump, where the feed water is pressurize a boiler, where the water is boiled and high-pressure steam is generated, a steam turbine, where the high-pressure steam is expanded, and a condenser where the low-pressure steam is condensed before entering the pump. The steam turbine is coupled to a generator, converting the mechanical shall power to electricity.
A real steam plant may contain many other components, e.g. feed water pre-heaters, and can utilize reheat and several pressure levels to improve the cycle efficiency.
Working fluids other than water have also been used in steam cycles, to utilize low- temperature heat sources and/or to keep the size and thereby the efficiency of the steam turbine in small plants (~OOkW) at an acceptable level [6, 7].
A mixture of water and ammonia has also been used as the working fluid in a steam cycle, known as the Krdina cycle. The major idea of this cycle is to minimize exergy losses that occur during heat transfer. Since a mixture of two different fluids boils and condenses at a sliding temperature, instead of at a constant temperature as in the case of pure substances, exergy losses during heat transfer are reduced [8, 9].
Since heat from combustion gases is transferred to the water circuit by heat exchangers in the boiler, the steam plant provides a large fiel flexibility. Nuclear power plants are also based on steam cycles. The heat source in the nuclear power plants is radioactive material, and fission in the reactor replaces the combustion process in the boiler.
Using a liquid as the working medium is also very economical, due to the small amount of power needed to pressurize the liquid in a pump compared to pressurizing gases.
As mentioned above, the most important advantage of the steam cycle is the large fiel flexibility (see papers 3,4 and 6). Some of the disadvantages with a steam cycle are the limited maximum allowable temperature that curtails the maximum cycle efficiency, the plant size because of the low specific power, the need for low-temperature heat sink for condensation, and the slow startup and load following capability. 2.2. Gas cycle
Major components of a gas cycle are the compressor, where the combustion air is pressurized, the combustion chamber, where fiel is combusted using the pressurized air as oxidizer, and the expander, where the hot combustion gases are expanded. Mechanical shail power from the expander can be converted to electricity in a generator, or used for mechanical drive.
The most common type of gas cycle is the open simple gas turbine. More complex gas cycles have also been designed and built to improve their performance. Using inter cooler, afier cooler, sequential combustion, etc. are measures taken to improve the plant’s efficiency resulting in increased cycle complexity [10, 11, 12, 13].
Closed loop gas cycles using a working medium other than air, like Helium and C02, have also been studied. Closed gas cycles, using C02 as working medium, were built and tested in Europe during the 1950s and 1960s. The major idea with closed gas cycles is to increase fuel flexibility. Since the hot combustion gases are directly expanded in the open gas cycle, fhel quality becomes a crucial factor for the lifetime of the components in the hot gas path, specially in the modem and highly cooled gas cycles. In the closed loop, heat is transferred to the working medium through a heat exchanger. The increased fuel flexibility here has a drawback caused by the limitation in the maximum allowable Turbine Inlet Temperature (TIT). When heat is transferred to the working medium by a heat exchanger, the maximum TIT is decided by the material temperature of the heat exchanger. One other advantage of the closed loop cycle is the possibility to use working media with special thermodynamic properties, e.g. Helium [11].
A higher maximum allowable temperature means a higher cycle efficiency. The maximum allowable temperature in a modem gas cycle is 2-3 times higher than the steam cycle’s. This can be explained by, for instance, the short distance the hot gases at maximum temperature are transported in the gas cycle before expansion, the steam dissociation at temperatures higher than 850° C and the construction of the control valves, etc., in the steam cycle.
Some of the advantages of gas cycles are fast startup and load following, small footprint low investment costs, etc. A more detailed description of gas turbines and their current status is given in chapter 3.
2.3. Combined cycle
The Combined Cycle (CC) consists of both steam and gas cycles. The gas cycle, working at a higher temperature is called the topping cycle, and the steam cycle, utilizing the remaining heat in the exhaust gases, is called the bottoming cycle [14]. Low exhaust gas temperature in simple gas turbines results in increased thermal efficiency. However, modem heavy duty gas turbines have been developed to match the bottoming cycle, in the sense that the exhaust gas temperature is increased to provide
6 better bottoming cycle efficiencies. Today, combined cycles are approaching 60% electrical efficiency.
An additional gas turbine developmen~ resulting in a better integration in the combined cycle, is steam cooling. Further efficiency improvement requires higher TIT. Increased TIT demands more cooling air, while keeping the emissions at a low level requires more air to the combustion process. To get around these conflicting demands, steam has been used for cooling stationary components, like the transition piece and the vanes of the gas turbines [15]. General Electric has announced a new generation gas turbine to be in service at the end of 2002, called H-class, that utilizes steam cooling even for rotating blades [16, 17, 18].
2.4. New concepts
Integration methods between gas and steam cycles other than the combined cycle have also been studiec+ resulting in interesting concepts, e.g. the STeam Injected Gas turbine (STIG), the Cheng cycle, the Evaporative Gas Turbine (EvGT), also known as Humid Air Turbine (HAT), and the Cascaded Humid Air Turbine (CHAT).
The major idea with these power cycles is to recover the heat from the exhaust gases, using water steam or humidified air, and put it back into the system.
In STIG and Cheng cycles, heat is recovered from the exhaust gases by rising steam in a heat recovery steam generator. The steam is injected into the combustion chamber, reducing the NO. generation and increasing the power output. However, in the EvGT and the CHAT cycles, heat at high temperature is recovered in a recuperator, and at lower temperature in an economizer, generating hot water. The hot water is then brought in contact with the compressor discharge air, in a humidifier, resulting in saturation humidification of the air flow. The humidified air is then fin-ther heated in the recuperator before entering into the combustion chamber (see papers 6, 8).
Since the presence of steam during combustion has shown to suppress the NOX formation, these concepts have turned out to be interesting in the sense that they both reduce the NOX emission and increase the efficiency. The EvGT seems to be the most promising concept that can achieve efficiencies comparable to the CC at lower investment costs and emissions [19]. For more detailed description of these cycles, the reader is referred to the publications mentioned in the reference list [20, 21,22,23, 24].
To achieve higher efficiency at lower emission levels, some other combinations of power production units are also studied. These systems are based on fuel cells and \ .. combined cycles. A gas turbine, as well as a Spark Ignition (SI) engine, can be used as the topping cycle of the CC in these systems. Paper 2 presents results from a study, where a system based on SI-engine, flel cell and a steam bottoming cycle is investigated. The fhel cell model used in this study is simplified, and contains only the material properties of the connected streams. However, results from detailed studies, concerning fuel cell modeling [25, 26], show that higher accuracy in calculated results requires at least a two dimensional model of the fiel cell.
7 —
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8 3. Gas turbine development
The development of gas turbine technology during the last decade has made combined cycle efficiencies near 60% possible today, and hybrid plants, with fuel cell and gas t&bine, with efficiencies higher than 70% will be achieved in the near future [27].
Besides high efficiency, a combined cycle offers other benefits, like a small footprint, short construction time and low investment costs. All these benefits have made combined cycles the most interesting alternative for power production today.
The most advanced piece of equipment in a combined cycle is the gas turbine. Gas turbines, specially heavy duty gas turbines, have been through a tremendous development after the end of the cold war.
Increasing the turbine inlet temperature has been the primary approach taken to improve gas turbine efficiency. The TIT has increased steadily from about 540”C in the 1940s to over 1425°C today. Increased TIT has become possible by the development of new materials and the innovative cooling techniques for the critical components, such as coating the turbine blades with ceramic layers and cooling the blades with the discharge air from the compressor.
Ionil LzY Iwil 1970 1980 mm 21M0 alo Year
Figure 1: Increased TIT overtime, [28]
This development has also resulted in more expensive components, requiring new tools for continuous monitoring and data analysis to prevent a forced outage. New measurement systems have been developed to measure the critical components’ temperature of the hot gas path during operation. These systems are based on optical access to the components, optical pyrometry. The pyrometer collects the infi-ared radiation from blades and converts it to a temperature, using the emissivity of the material. Measured data are monitored so that the operator can follow the changes in material temperature during operation. One of the disadvantages with these monitoring
9 systems is the large amount of data collected continuously during operation. The operator will not be able to follow all the changes and simultaneously analyze the ,? . upcoming problems. Therefore new tools for intelligent data analysis have been developed to assist the operator to identi~ engine faults.
These tools are generally based on either mathematical modeling of the engine components combined with heat and mass balance calculations or statistical data analyses identi&ing the relationship between system input and output, like artificial neural networks. A more detailed description of these tools and methods is presented in chapter 4.
Minimizing the maintenance costs also requires prediction of the engine components’ remaining lifetime. The extended blade life management requires predictive models for assessment of coating degradation and remaining life. Life management involves life prediction via calculation and monitoring. Here, aerothermal analyses are combined with the measured data to validate calculation models for prediction of components’ remaining life.
Life-limiting degradation of gas turbine blades includes oxidation of the coating and spallation of the protective A1203 scale that forms on the surface of aluminide and MCrAIY *coatings. AI additional degradation mode is interdiffusion of elements of the coating and substrate, which reduces the amount of Al available to form the protective oxide scale. Prediction of the remaining life for Thermal Barrier Coatings (TBCS) requires, among other factors, information about the thickness of the ~-NiAl layer that delivers Alto build the protective oxide layer [29, 30].
In this chapter, the effect of technology development on gas turbine improvement will be focused on. Cooling techniques and materials development, concerning both the substrate superalloy and the coating, will be briefly discussed.
3.1. Introduction
Technology transfer from military jet engines to heavy duty gas turbines, after the end of the cold war, has contributed to a large extent to the rapid development of these turbines. However, improved casting technique, allowing complex blade constructions and cooling systems, materials development and coating technique have also contributed to the gas turbine development.
As mentioned earlier, increased TIT results in higher cycle efficiency. Since air cooling of the hot gas path reduces the TIT, accurate thermodynamic modeling of the gas turbine cooling system is an important issue, specially for modem gas turbines that utilize a large amount of cooling air[31, 32].
*MCrAIY:M representseitherCobaltorNickel,Chrome,AIuminu~ andYttrium
10 Other factors that influenced gas turbine improvement are using three-dimensional hot gas path modeling, replacing the silo combusters with annular combustion chambers, having less number of blades and stages to achieve the same performance, developing the premix burners and dry low NOX burners, and adjusting the gas turbine exit temperature for better combined cycle performance.
The most important factors that have made gas turbine development possible are
. Development of material and production techniques: - directionally solidified and single crystal blades - better thermal barrier coating material and application technique - advanced cooling techniques, such as convection, film and impingement cooling
● Development of three-dimensional calculation tools foc - flow path analyses, improved geometry and higher component efficiencies – thermomechanical calculations, reduced number of blades at higher output
All these improvements have resulted in increased thermal efficiency for gas turbines, reducing the fuel costs for the same power output but tley have also resulted in more expensive components, like blades and vanes. To provide protection for these expensive components, condition monitoring and intelligent data analyses have become the new research and development areas with condition based maintenance as a key factor.
3.2. Blade cooling
Advanced casting techniques make it possible to produce blades and vanes with complex internal cooling channels in one single crystal or directionally solidified structure.
Further temperature increases, and consequently higher efficiencies, require more effective cooling or a better coating that can keep the material temperature under its limits.
Air cooling is the most common cooling method used in modem gas turbines. The coolant, used to reduce material temperature, can provide cooling by
● convection where the coolant circulates through internal channels and is released into the hot exhaust gas path through holes in the blades’ trailing edges and in the top of the blades. Ribs or turbulators on the channels’ walls improve the heat transfer through increased turbulence intensity.
● generation of an externally isolating layer of air over the blade, called film cooling. The cooling film is generated when the air from internal cooling channels is drawn out through small holes on the blade surface. The density of cooling holes over the whole blade depends on the thermal load on the blade parts. A shower head is usually placed in the blade’s leading edge, where the stagnation point occurs.
11 . impingement in the sense that cooling air, slotted through cooling channels inside the blades, is released through holes building jet streams against the wall, separating the hot gas from the cooling air, to remove the isolating boundary layer and enhance the heat transfer.
Usually, a combination of these three cooling methods is used in modem gas turbine blades to provide the most effective blade cooling. The objective is to reduce the amount of cooling air needed to keep the material temperature under its limits, else a consumption of a larger amount of air for cooling purposes will result in less air available during the combustion process leading to increased NOXemissions.
The negative effects of open-circuit cooling on gas turbine performance (e.g. delusion of hot gases, disturbance of the flow pattern), make it important to limit the amount of cooling air mixed with the main combustion gases. This has led to closed-circuit cooling, where the coolant is drawn through cooling channels without mixing with the hot gases. Using steam for gas turbine blade cooling will reduce the need for cooling air that is higher than 20% of the compressor discharge in modern gas turbines. Since closed-circuit cooling does not dilute the hot gas, it permits higher ftig temperatures for a given combustor exit temperature, leading to higher efficiency. Using steam as coolant allows an increase in the TJT by about 100 “C without an increase in the combustion temperature [18]. Steam is also a more effective heat transfer medium than air. Difficulties related to steam cooling refer to fouling in the cooling channels and the thermal stresses caused by the large temperature gradients.
Effective convective cooling means relatively large temperature gradients. Fihn cooling means that the hot gas temperature, as experienced by the blade, decreases. This means that the temperature gradient over the wall is lower for the same mean metal temperature. Instea& high stress concentrations appear at the cooling holes. A combination of convective cooling by steam and fihn cooling by air will result in better cooling and a homogeneousmaterial temperature [15].
Cooling holes are problem areas because of the locally high strain ranges. The base metal/coating interaction makes the problem more complex. The manufacturing process of the holes is also important since it may produce micro cracks and other defects close to the inner walls of the holes.
Disadvantages with blade cooling refer to the differences in material thickness in different parts of the blade and the extensive cooling near the cooling holes on the blade surface, resulting in concentration of thermal stresses. For example, cooling ribs lead to stress concentration. The middle part of the cooled blades is usually stiffer than the trailing edge. Therefore, the material temperature at the trailing edge should not be too high compared to the temperature in the middle part of the blade; otherwise, there will be a large mechanical strain range with compressive stress at high temperature. Stress relaxation during the running of the turbine will lead to tensile stresses on shutdown, causing crack initiation and crack propagation [29].
12 3.3. Materials development
Materials developmen~ concerning both substrate superalloys and the thermal barrier coating, has had a large impact on gas turbine improvement.
Creep is the failure mode most sensitive to temperature. A 50°C non-consemative error in average temperature results in a four- to five-fold reduction in creep life. The creep strength of Directionally-Solidified (IX) superalloy is about three times higher than that of the conventional polycrystalline materials, and superalloy in single crystals exhibit almost nine times higher creep strength than conventional polycrystalline materials [30].
TMFWe of DimiiOdlySOMtid, singleCrystal,8dEqui-wd CWings
m Pofyxistd 9x ~ cofum!mr.c@al nm single
3x .,.!% .; : ,. lx
Cltepshnglh mmalFa!&wI?.e+sti CO&n Resklam Figure 2: Life of blades and vanes produced by different casting techniques [30].
In polycrystalline material, grain boundaries promote creep deformation through grain boundary sliding, resulting in creep cracking. DS casting reduces intergramdar creep cracking by aligning grains and grain boundaries parallel to the axial loading on the blade, caused by the rotational movement. In single crystals, the whole blade is one crystal without any grain boundaries, so boundary sliding is not possible in single crystals, and thereby creep strength is considerably improved (see Fig. 2).
Grain boundaries are also especially vulnerable to corrosion and oxidation attack. To improve the resistance against corrosion and oxidation, certain alloying elements are added to the superalloy. These alloying elements have some negative effects on mechanical properties, such as ductility, and also lower material melting temperature. In single crystals, these alloying elements are not needed, resulting in a higher allowable temperature.
A polycrystalline material has isotropic behavior in all directions, regardless of load direction. The anisotropic nature of the single crystal material can be utilized for minimizing thermal stress in the axial direction. The idea is to avoid having both high thermal and rotational loading stresses in the axial direction.
Dii3ision of alloying elements through interaction with the hot gas, changes the composition and therefore the basic behavior of the material.
13 The second generation superalloys are characterized by the introduction of Rhenium, which decreases the difisivity. Lower diffusivity improves the stability of the microstructure, and therefore increases the allowable working temperature.
In cooled turbine blades and vanes, surface temperature is typically higher than the average metal temperature, resulting in surface degradation. In order to decrease the surface temperature and increase the resistance against hot corrosion and oxidation, blades and vanes can be coated by different materials.
3.4. Thermal barrier coatings
The hot gas path components in a gas turbine are basically exposed to two types of chemical attack hot corrosion and oxidation. Various salts, most of them containing sulfur, are present in the gas flow, attacking the material particularly when the salts are fluid. Therefore hot corrosion is a problem at intermediate temperatures. The oxidation accelerates, however, at higher temperatures. Two basic classes of coatings have been used to protect the metal blades against corrosion and oxidation, namely difiision aluminide coating and overlay coating.
Thermal barrier coating is increasingly applied to industrial gas turbines, providing a temperature shield for metal blades, and protecting them from the high firing temperatures. TBC is used to improve the efficiency by higher operating temperatures, to reduce the need of active cooling, and to extend the life at current firing temperatures.
Typicrd TBC systems consist of two coats, a metallic bond coat and a ceramic top coat. The bond coat, generally MCrAIY, is designed to provide oxidation and corrosion resistance. It is also a good adhesive base for the ceramic top coat. The ceramic top coat provides a high temperature shield for the blades. The bond coat forms a thin, slow- growing M203 scale during service, which provides protection against further oxidation. Unfortunately, this coating can also contribute to spalling in some applications.
TBCS which consist of a porous layer of zirconium oxide (Zroz) stabilized by yttria (y2@) and magnesia (MgO), exhibit lower thermal conductivity. As a result, they decrease the metal surface temperature and eliminate hot spots, thus reducing thermal fatigue stress.
Hot corrosion, due to alkali sulfate ingestioq is frequent in land-based turbines and can lead to yttria depletion in the TBC and corrosion of the bond coat resulting in early failures.
The oxide layer is normally where cracks initiate, since it typically has quite different thermal properties compared to the metal layers, and because oxides are brittle. Cracks may also transfer oxygen and other harmfid substances down to the base material where they might significantly contribute to crack propagation and failure.
14 The primaryadvantage of TBCs is their ability to reduce the blade metal temperature by about 11O“C, for a 0.25mm coating. TBCS can also dampen thermal transients. One of the disadvantages of the TBCS is their surface roughness, which decreases aerodynamic efficiency. Another shortcoming is failure by spalling at or near the interface between the bond and ceramic coat. The major reason for spallation is compressive stresses due to thermal cycling and the growth of oxide scale at the interface [33].
Further increase in gas turbine efficiency at lower NO. emissions requires more advanced TBCS with lower conductivity. Multilayer coatings seem to be a promising tool to achieve the required petiormance improvements. Phonon transport is the predominant conduction mechanism in dielectric materials, like TBCS [34]. The basic idea with multilayer TBCS is based on the enhancement of the phonon scattering to reduce conduction. One of the advantages of multilayer coatings is that the altering layers can be selectively designed to reflect radiant energy.
Test results show that multilayers composed of yttria stabilized zirconia and alumina reduce the thermal conductivity effectively. Muhilayer TBC has a thermal conductivity of about half that of conventional TBCS and also rejects up to 70% of incoming mdiant energy [33].
The use of alloyed ceramics is beneficial for TBC applications, because the presence of impurity atoms in solid solution decreases the thermal conductivity. Decreased thermal conduction is the result of phonon scattering due to differences in mass of the substituted elements, differences in binding forces of the substituted atom compared with the original lattice, and the elastic strain field around the substituted atom. The amount of scattering that occurs, and the associated reduction in thermal conductivity, is proportional to the volume fraction of impurity elements that are added to the parent material.
15
4. Tools and methods for power plant analyses and optimization
Computer-aided tools for power plant analysis and optimization are designed to handle different types of problems. These tools c- however, be placed in two major categories, one utilizing thermodynamic based models, like heat and mass balance programs, and the other using statistical relationships between input and output of a specific system, like artificial neural networks.
Tools from both categories have their advantages and shortcomings, and it is important to select the proper tool for a specific type of study. Heat and mass balance programs are very usetid, specially during pre-design studies, and can be used for performance analysis during operation (see papers 2, 3, 4, 5, 6). Heat and mass balance programs are also the major part of the condition monitoring systems. Dynamic modeling and simulation can be performed using these types of tools with time dependent calculation procedures.
Tools based on artificial neural networks, using the statistical relationship between input and output values of a specific system at different operational conditions to generate a mathematical model, have been used for a wide range of applications. Condition monitoring, sensor validation, fault diagnostics, dynamic modeling and control are some of the applications of these tools in power plant systems (see papers 7 and 8).
4.1. Heat and mass balance programs (HMBPs)
Heat and mass balance programs comprise the heat and mass balance calculations of components in power plants and industrial processes. Results of these calculations are needed, for example, to determine plant efficiency.
The most important application field for HMBPs is probably the investigation of a new power plant concept’s potential, prior to the construction phase. Modeling the process schema without restrictions, and studying its thenuodynamic and economic conditions in pre-design studies, saves both time and money. Therefore program flexibility, modularity, mathematical stability and user-fiiendliness have been in focus during the last decades.
Before the computer age, the calculation process for power plants was a difficult and time-consuming task and the possibilities to investigate new concepts’ advantages were very limited. The first generation of computer programs for heat and mass balance calculations were designed so that a few well-knowq pre-defied plant layouts could be studied. The major objective of these programs was to study the effect of changes in current parameter values on the plant performance. When changes that were needed to improve the plant efficiency were identitled it was easy to validate the results by adjusting the parameters in the existing plant. The lack of flexibility to create completely new plant layouts or to modifi the existing plants was a serious limitation.
17 The rapid development of computer technology during the last decade has had a large impact on HMBP development. The most important effects of increased calculation speed and data storage capacity in modern computers are the possibilities to implement graphical interface, new programming methods and advanced mathematical calculation procedures.
In this chapter, programmingg aspects concerning mathematical modeling and program structures are discussed, and methods for further improvement of HMBPs, implemented in an in-house program developed by the author, are presented. A brief discussion concerning part-load calculations based on local linearization, suggested by the author, is also presented.
4.1.1. General layout
A HMBP based on a graphical interface is generally built as a menu controlled program. Components such as heat exchangers, turbines, etc. are presented as icons in a screen menu. The user is able to compose the desired process plant, using a drag and drop technique.
A component in the HMBP is presented as a graphical icon with incoming and outgoing streamlines. Working medium and electrical effect are transported by the streamlines. In- and out-flows are presented as a set of property data, describing the current status of the working medium. Material properties of the working medium, presented as tables or program codes, are delivered at call.
To create a system, the user connects components by streamlines. Connecting two streams means that stream data are compared and copied over, so that one streamline containing the whole data set is generated. During generation of the plant’s flow diagram, components and points receive numbers that identi~ their generation sequence. These numbers will be referred to as point or component numbers. A systematic data presentation, adopted to the material’s properties in the streamlines, makes the data transfer between connected points easier [1]. ,. The icon itself represents the component and contains a set of equations. These equations model the component’s thermodynamic behavior. The mathematical model presenting a component can be simple or detailed. The modular program structure makes continuous updating of the mathematical models and use of several parallel models for the same component possible. Figure 3 illustrates a general component model.
I I
Figure 3: Component model as presented in the computer program.
18 4.1.2. Mathematical calculation methods
As mentioned earlier, every component of the HMBP is presented as a set of equations. ., During the calculation process, these equations are treated by one of the following three methods: the sequential, the equation-oriented or the semi-parallel method [1].
In the sequential method, the calculation process starts in a component and follows the material flow downstream of the system. Generally it is assumed that calculation of one component generates enough data to calculate the next component downstream of the previous one. An important advantage of this method is a straightforward programming procedure. One disadvantage of this method is the unavoidable iteration process caused by recirculating flows within the system. These iterations can be very time-consuming and sometimes end with a divergence.
The equation-oriented method was developed to avoid problems related to the iterative process and mathematical difficulties in the sequential method. In this method all equations representing the entire system are collected in a matrix and solved simultaneously. One difficulty with this method is the need for “good” starting values to initiate the system matrix, to avoid mathematical instability. Experiences with this type of system have shown that lacking good guesses during the initiation process can result in severe mathematical difficulties. One approach to avoid this kind of problem is to build the plant in smaller segments and use the resolved data from the segments as the starting values for initiation of the matrix.
The semi-parallel metltod is a combination of the two previously mentioned methods. Using the sequential method to solve as many unknown parameters as possible before generation of the system matrix, decreases the size of the matrix and thereby also the mathematical instabilities. The combination of the free choice of variables, i.e. data to initiate the system with, and the semi-parallel method results in the fastest and most stable program algorithm for a HMBP.
4.1.3. Program structure
Creating a hierarchical structure gives the programmer the possibility of implementing control routines and monitoring the calculation process. The idea is to generate a program structure that makes it possible to address a specific variable at any time during the calculation. The most important advantage gained by this programming method is the possibility of identi@ing numerical difficulties, e.g. division by zero, and generating an error message addressing a specific equation in a specific component, where the problem has occurred. An exact localization of the error prior to a mathematical crash down increases the user’s ability to handle the problem quickly and efficiently [1].
19 4.1.3.1. Variables
The smallest unit of a mathematical system is the variable. The most important information carried by the variable is its numerical value. The variable can also be defined in such a way that it contains special properties to ease the programming process and the calculation control. One of these properties can be a status identifier that obtains one of the three following values. . Knowm the user has initiated a value for the variable . Unknovvm the variable has no numerical value and must be calculated ● Calculated: the variable has received a numerical value as a result of calculations
Variables can easily be sorted using a status identifier, such as one that identifies unknown variables after a calculation loop.
Another identifier needed, e.g. for automatic transformation of the unit system, carries the following information. ● Current value: numeric value of the variable, converted to the unit system used during the calculations. . Symbolic value: numeric value with a specific unit, given to the variable by the user.
The data initiated by the user can be converted to a pre-defined unit system before the calculation process. However, calculation results are presented in the unit system used during the initiation [1].
4.1.3.2. Free choice of data
High flexibility and user-friendliness in combination with calculation control and emor location can be implemented in HMBPs in different ways. Free choice of parameters during system initiation is one of the most important factors to increase flexibility and user-friendliness. It means that all variables, representing streams and components, are presented in the data initiation form, and the user can fi-eely select the parameters to be initiated as input to the system.
Free choice of data requires that every single equation is presented as many times as the number of parameters it includes, so that every parameter can be expressed as a function of the other parameters of the equation. Of course this solution metiod enlarges the program code enormously with all the disadvantages that would follow. This solution can be avoided by presenting the basic mathematical operations as subroutines, like addition, subtractio~ etc. The idea is to calculate a value for any of three parameters, in e.g. a multiplication like “A = B. C “, when two variables are known. This method makes it possible to control the calculation procedure and report upcoming errors prior to a calculation crash. The mathematical operations are then expressed in a sequence of basic operations [1].
An example is given in Appendix 1.
20 4.1.3.3. Calculation procedure
A combination of the sequential calculation method and the equation-solving method improves the calculation’s speed and its reliability. One approach to combine these methods is illustrated by Figure 4. ,.. ! ,,,
Figure 4: The calculation sequence. Every circle represents a component and the ellipse represents the whole system.
When the power plant system is initiated by input data and the number of unknown variables is equal to the number of system equations, the number of unknown variables in every stream and every component is calculated and stored in two vectors. The lengths of these vectors are equal to the number of points and components within the system. The number of unknown parameters, i.e. parameters that have not received any numerical values, in each point and component are stored at the vector position matching the current point or component number in the flow diagram. Using a program code, built on the previously described method, makes it possible to start the calculation in one component and repeat the calculation loop until the number of unknowns in the component or in the connected points is not decreasing. This procedure is then repeated for the next component in the system. When the last component is calculate~ the Total Number of UnKnowns (TNUK) within the system is updated. A comparison between this value and the previously stored TNUK gives a signal to begin a new round of calculatio~ if TNUK is changed. Otherwise the calculation is completed and the calculation results are monitore~ or the unsolved equations are collected in a matrix and solved simultaneously. It is worth mentioning that the size of this matrix is very small, and thereby very easy to solve [1].
4.1.4. Part-load calculations
Pm-load calculations in HMBPs are generally carried out using a parallel set of component models, modeling the part-load behavior of the components. These models are based on equations that use different values of constants to model different part-load levels. Tables or curves, delivering the specific data and constants for each component, are available within the program.
An alternative approach for part-load calculation is local linearization. This method is based on the assumption that the system parameters vary linearly between two part-load levels close to each other. Performing part-load calculations based on local linearization requires derivatives of the system equations in respect to parameters that can determine other properties of the system unambiguously. A detailed study of the material property data, such as a steam table, shows that there are only two parameters, namely pressure and enthalpy, that can be used to determine other parameters of the working material in the entire validation range [1]. Representing the derivatives of system equations in
21 respect to pressure and enthalpy makes it possible to calculate the changes in material properties between two different load levels. However, carrying out a complete mass and heat balance calculation requires information about changes in mass flow rate. Therefore, the differentiation of the system equations must be performed in respect to pressure, enthalpy and mass flow rate.
Every component is thereby represented by a set of equations for full load calculations and the differentiated set of the same equations for part-load calculations. To perform part-load calculations, components of the system transfer their set of differentiated equations to a matrix that is completed by equations representing the part-load regulation conditions.
Starting from the full load conditions, where all parameters of the entire system are know necessary changes of pressure, enthalpy and mass flow rate in every point and every component of the system can be calculated in order to determine the current value of these three parameters at the new load level. Using the calculation method presented in section 4.1.3.3, every parameter of the system can be calculated at this new load level. These data are used as the new starting point for the next par-load calculation. As illustrated in Figure 5, the accuracy of this calculation method is dependent on the step size betsveen the load levels. Decreasing the step size increases the number of calculations. However, using the previously presented calculation method on a modern computer allows decreasing the step size to a level so that the best possible accuracy is achieved within an acceptable amount of time [1].
Parameter 4 value
Figure 5: Illustration of the effect of step size on the accuracy of the results, using linearization. The error (the gap between approximated value and the real data) increases by increased step size (Ax).
22 4.2. Optimization
Processes embody many technical considerations. Some investment and operating decisions are dictated by process requirements, regulatory mandates, safety standards, etc. However, many options in equipment selection, plant configuration and operating practices remain. The choices made can affect capital and operating costs. Finding the minimum total cost is the ultimate goal.
Generally, optimization of power plant systems aims at optimizing either the plant’s layout or the plant’s parameter values, to maximize the plant’s electrical efficiency. Selecting the best power plant configuration to achieve the highest efficiency is usually carried out by “trial and error”, using the knowledge of earlier experiences and system limitations. There are also optimization tools based on mathematical programming that can search for the “best solution” manipulating the problem variables. These programs require definition of the objective fimction and the constraints of the system to find the best economical solution [35].
The most common optimization technique used in HMBPs is based on an automatic parameter variation tool, usually connected to standard programs for data presentation, e.g. Excel, that performs repeated calculations of the plant’s model, changing one parameter each time. The calculated values of the objective fimctiom here the electrical efficiency, can be plotted against the studied parameter. The parameter value giving the highest plant efficiency will be used as the design parameter value. ,, There are also optimization tools based on exergy analysis, e.g. pinch technology. Pinch technology identifies the fimdamental temperature constrain the “pinch” temperature, which thermodynamically limits energy recovery in the system. Identification of this constraint makes it possible to establish practical standards for the operating and capital costs of energy systems before they are designed or, in the case of existing systems, modified [36].
Pinch technology’s focus is on the heat exchanger network. By defining energy and economic targets for the plant, pinch technology optimizes the network by mhimking the exergy losses during the heat transfer process.
4.2.1. Parameter study
As mentioned before, a parameter study is the most common optimization procedure adopted in HMBPs. In the early days of HMBP development the parameter study was carried out manually by the user so that the value of one parameter of the plant was varied and the objective Iimctioq usually the plant efficiency, was calculated. The user compiled the results to generate a curve showing the variation of the objective fimction by the decision parameter. This process was very time-consuming, containing uncertainties related to the manual treatment of the data. The major reason for manual data processing was the limitations of the computer hardware and software.
23 Parameter studies in modem HMBPs are carried out by defining the upper and the lower limits of the decision parameters and the step size for parameter variation. The program itself performs repeated calculation loops, and the calculation results can be saved in files or monitored in a standard program like Excel. The user can study the impact of parameter changes on the plant’s efficiency and select the list of the design parameter values.
4.2.2. Pinch technology
Conceptually, pinch technology is based on identi&ing targets for a process and recognizing the pinch. First, the best energy and capital petiormance for a given process is predicted. Then, a design which satisfies the targets is invented. The “pinch” temperature thermodynamically limits energy recovery in the system. Knowledge of the process limitations or constraints is used during the design stage to ensure that the projects developed are compatible with the process and its operating procedures.
Studies in pinch technology vary with the objectives, scope of investigation, process complexity, and other factors. However, most fall into two broad categories: scoping studies and detailed analyses.
Scoping studies identi~ opportunities and quantifi the potential for improvement. They generally consider just one operating case and use readily available data. Data gaps or inconsistencies are resolved using engineering judgment or estimates. The extracted data are interpreted and characterized in pinch technology terms to establish enerW and capital cost targets. Process changes capable of reducing the targets are identified. Pinch technology uses conventional data, basically heat and mass balance information. The basic data requirement is a complete and consistent heat and mass balance for the process. Utility data and economic data are also required. Data collection and data extraction are often the most important stages in a pinch study.
To produce a successfid study, pinch technology practitioners must also be skilled engineers who understand the facility’s constraints and needs. Developing pinch technology expertise is not a trivial undertaking. Ailer training in pinch technology concepts, at least 1-2 years of intensive practice is required to become proficient in their applications [36].
Among pinch technology’s most usefid attributes is the targeting capability. Design data are used to generate targets for energy consumption and capital cost, prior to design. The minimum total cost identifies the design philosophy offering the most favorable overall economics.
Simple processes may not be significantly altered by pinch technology studies, although such studies would be correspondingly quick and inexpensive. In systems with only a few heating or cooling duties, the right course of action may seem apparent. However, complex power plant systems can be analyzed systematically by pinch technology. Pinch technology is best applied early, before major design decisions are made, to be able to vary tie plant layout for the best alternative.
24 The most important tools provided by pinch technology for process analyses are composite and grand composite curves.
The composite curves clearly present interesting information about the process. The composite curves allow the minimum energy requirements and the overall heat transfer area to be determined as targets. They show the energy target, where the pinch occurs, and where the design of the heat recovery system is likely to be difficult because of the small temperature driving force. However, composite curves have a shortcoming. They assume that utilities will be used at the temperature extremes of the process. This is not always desirable. The composite curves do not readily show the ways in which these objectives can be achieved. Thus a better tool, the grand composite curve, has been developed for this purpose.
The grand composite curve represents the horizontal separation between composite curves after these have been temperature-shi fied to allow for the pinch temperature difference. The grand composite curve can be used to quickly screen rdternative hot utilities. The appropriate placement principle is based on overall balances. A quantitative tool is also needed to assess exactly how much heat the process can accept ... at exactly what temperature level [36, 37, 38].
4.2.3. Exergy and thermoeconomics
Exergy is a second law concept, fiuthering the goal of more effective ener~ use. Exergy is a property, enabling determination of the usefid work potential of a given : ,, amount of energy at a specified state. Exergy analyses enable the locatio~ cause and magnitude of waste and loss to be determined. Such information can be used to design new efficient plants and increase the efficiency of existing plants.
The work output of a system at a specified state is maximized when the final state is the dead state, defined as the thermodynamic and chemical equilibrium of the system and its environment.
The requirement for a specified dead state, is the major disadvantage of the exergy analyses method. The definition of dead state gives opportunity for arbitmriness and makes a global comparison of different system solutions difficult. However, exergy analyses, implemented in more traditional power plant evaluation tools, like heat and mass balance programs, make a general optimization process easier.
Additionally, exergy is important because it provides the basis for thermoeconomic analysis of power plants. Thermoeconomics combines exergy analysis and economic principles to provide the system designer with information crucial to the design and operation of a cost-effective system. Thermoeconomics can be considered as exergy- aided cost minimization. Evaluating the costs for exergy destruction and loss is very useful for improving the cost effectiveness of the system. The objective of a thermoeconomic analysis might be to calculate the costs of each product generated by a system, to understand the cost formation process, to optimize specific variables in a single component, or to optimize the overall system [3,5, 39].
25 4.2.4. Condition monitoring and artiilcial neural networks
Low maintenance costs, high availability and reliability, as well as increased power plant efficiency, are some of the greatest ambitions for the independent power producers in the deregulated electricity market. Increased power plant efficiency is achieved by implementation of more complex and highly loaded components. To prevent forced outage and damage to these expensive components, and to provide the system with fault diagnostics, a surveillance system, such as a Condition Monitoring System (CMS) and an Artificial Neural Network (ANN), can be utilized [40].
The condition monitoring system is based on thermodynamic models of the power plant components. The CMS uses heat and mass balance programs and component characteristics, like compressor maps, if available. Online measured data are used as input to the HMBPs to evaluate the plant performance and component degradation, and to perform fault diagnostics. To complete the analysis, it is, however, necessary to make assumptions about parameters that are not measured, such as cooling flows. The calculation results are compared to the measured data, and discrepancies between current and expected values are used to diagnose the system performance, and identifi faults orland component degradation.
Since the CMS is utilizing measured data, sensor validation for detection of faulty sensors should be petionned prior to the calculation, to insure that the sensors are functioning. Sensor validation in the CMS is, however, restricted to detection of faulty or disconnected sensors, indicated by measured signals equal to zero. The possibilities for detection of sensor degradation, and data recovery are limited.
Dynamic modeling and simulation in the CMS are not possible, since it takes a long time for a physical model based dynamic HMBP to perform a dynamic simulation. Therefore, online dynamic modeling, based on real time data, cannot be performed in the CMS.
The ANN provides a generic functional relationship between the system’s input and output [41, 42]. A collection of system da~ representing as many operational and environmental conditions as possible, is used for generation of a model that captures the specific functional relationship. The accuracy of the ANN model is apparently proportional to the amount and the range of the data. The model comprises of two or more weighting matrices and a set of mathematical equations that can be programmed in any programming language. The final model can be executed without access to the ANN training program. Results of studies concerning simple gas turbine and evaporative gas turbine static modeling by using ANNs are presented in papers 7 and 8.
The static ANN model is also able to generate performance maps for the studied system, utilizing nodnear interpolation and extrapolation. Performance maps can be used for prediction of system peri?ormance at a wide range of operational conditions.
In static networks the input signal flow is directed to the output with no information feedback path; while in dynamic networks, the output of each layer is fed back as additional input. Real time measured data can be used in dynamic networks for
26 generation of a dynamic model of the system. One of the advantages of a dynamic model is its capability to predict faulty conditions as they are approaching, since it can recognize changes toward a pre-defined fault. As the sensor is degrading, its reading deviates more and more from the expected value. The dynamic model registers changes in sensor readings continuously and is thereby also able to detect the upcoming sensor degradation.
A fault diagnostic ANN may also be trained to tag existing patterns of inputs and outputs to a particular healthy or faulty condition. The fault diagnostic ANN recognizes the pattern of current system input and output, which follows a message indicating a healthy system if the current pattern is matching the healthy tag. In the same manner, the ANN can recognize a certain pattern that is pre-defined as a specific fault. In this case an error message is generate~ informing the operator about the fault. When an unknown system fault occurs, the tool generates an error message, that ANN is not able to identi& the fault, but cm however, be trained to learn on line and recognize the new faulty pattern the next time it occurs.
The ANN based sensor validation is more flexible than the validation model pefiormed by the CMS. The ANN based sensor validation is capable of including the sensors’ degradation and also facilitates data recove~, in the sense that missing data can be rebuilt using the relationship behveen the existing data set. Each operational condition is related to a specific pattern. If the reading of a sensor is slightly deviating ilom the expected pattern, while the rest of the readings are corresponding to it, a sensor degradation is detected and can be reported.
Since the surveillance tools mentioned above provide possibilities for remote monitoring and control of modem distributed power production units, it is most likely that their development will bean interesting research area in the fhture.
.,
27 28 5. Discussion and future work
As shown in previous chapters, there are different categories of commercial programs for analysis and optimization of power plants. Each category, in its turn, contains several alternatives. This chapter includes general conclusions for each category, as well as a discussion concerning the development trends for new tools, needed to fidfil the requirements of the modern power plant systems.
The HMBPs are the primary choice for pre-design studies, and they are also the major part of optimization tools, such as pinch programs, and performance analysis tools, like condition monitoring systems. There are several types of HMBPs, developed for different purposes. One major group is composed of HMBPs developed for modeling and analysis of chemical processes. These programs are usually used by process industries. There are also HMBPs specially developed for power plant analysis. These tools can be divided into two categories, one utilizing an open program environment, where all component models are available for completion and/or modification and the other category, where the main codes are not available, but the user can add models programmed in a specific progr amming language, e.g. fortran.
Programs tailored for power plant analysis are also developed to fulfil different requirements. Some of them contain more accurate part-load calculation models; others are adjusted to specific needs, like modeling district heating systems; and some are easily programmable, giving the users opportunity to adjust the program to very specialized applications. Commercial heat and mass balance programs usually do not contain an accurate model for a gas turbine cooling system. Accurate modeling of the cooling system is an important issue, specially for modern gas turbines that utilize a large amount of cooling air. Since the choice of the tool is determined by its application area and the specific needs of the system, it is not possible to provide general advice concerning the selection of an appropriate tool.
Pinch programs are usefil tools, specially for optimization of Heat Exchanger Networks (HENs). These programs require thermodynamic data for the streams of the HEN- system to be studie~ prior to the analysis. Therefore, stream data must be supplied by a HMBP. Pinch programs show to advantage in sophisticated HEN-systems, and complex power plant systems. The major differences among available pinch programs are the amount of infonnatiou and the ability to facilitate modeling and data presentation in an integrated graphical environmen~ rather than their mathematical construction.
Exergy analysis is a good complement to the energy analysis, performed in the HMBPs. It can be implemented in most of the existing HMBPs, since it requires entropy data that is already available in such programs. Exergy analysis generates information about losses in every component and takes into account the quality of the energy which is not considered in the ener~ analysis. Putting a price label on the losses of the system makes an economical optimization possible. The combination of the exergetic and the economic optimization is treated in tools based on the thermoeconornics. HMBPs can be completed by thermoeconomic analysis after the exergy analysis has been implemented.
29 The development of modem engines and power plants in combination with the changes in the electricity market requires new tools, tailored to fidfil the needs of the power producers today. The economic demands for profitability of the power generation systems requires high efficiency, availability and reliability, as well as low maintenance costs. Condition based maintenance has become a key phrase requiring continuous condition monitoring to diagnose the upcoming engine faults. Two categories of tools have been developed for this purpose. One is based on physical component models and HMBPs, called condition monitoring systems, and the other is based on the statistical relationship between the system’s input and output, called artificial neural nehvorks. Both categories utilize measured data, so the final results are highly dependent on the correctness of the measured data.
Since the most common failure has been shown to be faulty sensors, a primary measure ensuring that sensors are functioning, a sensor validation, should be taken. In the case of CMS, the sensors’ readings are transferred to the calculation process as long as their values are different from zero. A zero reading indicates that the sensor is faulty. Sensor validation applied in the CMS, does not give any possibility of taking the degradation of the sensors into account. However, the ANN can recognize sensor degradation when the measured data at a specific operational condition is slightly different from an expected value. It can also perform data recovery when a sensor is faulty, since there is a certain relationship between the input and output of the system. This relationship can be used for generation of an approximate value for the missing parameter.
An additional advantage of the CMS and the ANN is the capability of fault diagnostics. In the CMS, a group of measured data is used as input to the HMBP to calculate the expected performance data for the system at the current operational condition. If the expected data differs from the measured values, a fault is indicated, and expertise is needed to analyze the results and find the source of the fault. The ANN based systems recognize a certain relationship between the system’s input and output at different operational conditions. Their accuracy is directly proportional to the number of data sets representing these conditions. It is also possible to train ANN models to recognize a certain combination of input and output as a specific system fault. The faulty condition results in an error message directly addressing this fault. New faults, unknown for the system, can also be added to the register of known faults by training the system.
The application of ANNs on a power plant system provides also system performance maps, and static or dynamic models of the system. A system performance map, that is a result of static modeling, can cover possible operational conditions, giving an opportunity to predict system pefiormance at specific conditions. Using dynamic modeling that provides the system with data from earlier time steps, makes it possible to recognize changes in a certain direction toward a specific fault. This recognition can be used to generate a warning signal predicting an upcoming fault.
The mathematical model generated by the ANN can be presented as a set of equations and two or more weighting matrices that are specific for the studied engine. The equations and the weighting matrices can be implemented using any programming language to generate the ANN model of the studied system. It should be mentioned that the ANN tool is applicable only on existing systems.
30 ,
It would be desirable, as a fiture work, to include calculation procedures for evaluation of the investment costs for every power plant modeled in the HMBPs. This kind of analysis evidently requires manufacturer data for calculation of the component costs. Other factors, important for the future work, are the implementation of the life cycle assessment and the prediction of the remaining life time, based on the collected data concerning components’ degradation.
Table 1: A comparison of different tools Programs HMBP Pinch Exergy Thermo- CMS ANN Program analysis economics Component Yes No Yes No Yes Yes analysis System analysis Yes Yes Yes Yes Yes Yes Optimization Yes Yes Yes Yes Yes Yes Operation/ Yes No No No Yes Yes Surveillance (limited) Pre-design Yes Yes Yes Yes No No
Table 1 is a general overview of different categories of tools and should not be considered as a guide for selection of tools. For instance, comparison between CMS and ANN tools using the table can be deceptive, resulting in the false conclusion that these tools are equivalent.
31 \ .. 32 6. Conclusions
The knowledge about methods and tools for analysis and optimization of thermal power plants at the pre-design and the operational phases has been improved.
An in-house heat and mass balance program, suitable for thermal power plants, with a unique method to handle the part-load calculations has been developed and tested.
The technical development in the field of power plant technology, which has to be handled by the studied tools, is identified and discussed.
In order to illuminate advantages and shortcomings of different commercial tools, a number of concepts, based on both conventional and new techniques, have been analyzed.
33
i’ 34 7. Summary of papers
In this section, eight papers containing studies of system modeling and analysis are presented. The primary aim of the studies was to compare capabilities and shortcomings of the tools and identifi the most favorable application areas for each of them. There was also the aim to investigate new power plant configurations and repowering options for I%rther improvement of system performance. The studies have been performed using different commercial and in-house computer-aided tools, namely, heat and mass balance programs: IPSEpro, ASPEN Plus, PROSm pinch programs: Super Target, and Pro-Pi; and an artificial neural network tool, developed at Newcastle University by Ph. D. Ehsan Mesbahi.
IPSEpro is an equation oriented HMBP, providing an open code environment where the user can modify existing component models or create user defined components. New components become an integrated part of the program, as they are compiled.
ASPEN Plus is a sequential modular program, containing a large database for material properties. This program is the most common tool for modeling and analysis of chemical processes. In power plant analysis, ASPEN Plus is very useil.d for modeling the gasification process of solid fiels, as well as biomass.
PROSIA4 is also a sequential modular HMBP with capability for modeling, among other types of power plants, biomass fueled Combined Heat and Power (CHP) systems.
Super Target (ST) is a user-iliendly pinch program with the facilities for heat exchanger network analysis. The other previously mentioned pinch program, Pro-Pi, is similar to the ST concerning the mathematical construction but its graphical capabilities are not as sophisticated as those of ST.
The ANN tool was used for modeling the simple gas turbine and the evaporative gas turbine, operating at the gas turbine laboratory of the Department of Heat and Power Engineering in Lun~ Sweden. Models generated by the ANN tool can be executed without the ANN sofhvare. The systems’ specific weighting matrices and the general mathematical equation can be implemented in any programming language. Further system analysis can be performed by execution of the static models.
Studies concerning gas fueled SI-engines are presented in the papers 3 and 5. The thermodynamic properties of the engine flows were not modeled, but provided by the manufacturer. It should be mentioned that heat and mass balance programs, designed for power plant studies, generally do not contain models for SI-engines. However, in heat and mass balance programs providing an open model environment modeling of any type of the components is possible.
The accuracy of calculated results are primarily affected by simplifications that are implemented during the modeling process. In heat and mass balance calculations, the material properties are also crucial for the accuracy of calculated results. Since previous studies [2], show that the thermodynamic models used in different HMBPs are similar, and that the material data banks generate comparable values for material properties at
35 same physical conditions, the search for differences between HMBPs should be focused on: - the numerical stability that is a consequence of the mathematical calculation method used in the program, - the variety and the number of the component models available, - the user-fiiendliness and capabilities for data presentation and monitoring, rather than differences in numerical values.
The papers presented in this thesis contain numerical results, given by more than one decimal. The number of decimals does not reflect the accuracy of calculated results. However, since the superiority of different layouts utilizing same models and property data is evaluated by comparison of the numerical values, the decimals given here make such a comparison easier.
Paper 1: Applying Pinch Method and Exergy Analysis to a BIO-IGH4TPower Plant.
This paper presents results from a study where the pinch method and exergy analysis were applied to optimize a biomass-fieled integrated gasification humid air turbine. Two different pinch programs, Pro-Pi and Super Targe~ were used. Comparison of calculation results showed good agreement. A simple method to overcome pinch program difficulties in modeling streams that experience combined heat and mass transfer, is also presented. Modeling and calculation of the power plant system were performed by the heat and mass balance program PROSIM.
It is shown that the combination of the pinch method and exergy analysis makes it easier to optimize the system. The pinch method provides the user with usefid information such as composite and grand composite curves. The curves give increased understanding of the heat transfer and the exergy losses occurring within the system. The basic rules of exergy analysis can be used to identi~ measures necessary for the improvement of the system.
The paper was mainly written by the author. Kent Johansso~ from Sycon, contributed to this paper by performing calculations in the pinch program Pro-Pi and participating in the comparison of results from Pro-Pi and Super Target.
Paper 2: A computational investigation of a combined cycle using an SI-engine, a Rankine bottoming cycle and afuel cell.
Results from theoretical studies, concerning hybrid plants containing a Fuel Cell (FC) and a Gas Turbine (GT), have been presented in several publications. All these studies show that a hybrid plant with FC, GT, and steam bottoming cycles can achieve electrical efficiencies in the order of 75’Yo.This paper presents the results of a theoretical study of a hybrid plant consisting of a Solid Oxide Fuel Cell (SOFC), a spark ignition gas fueled engine (lWrtsiki 18V34SG), and a steam bottoming cycle. The SI-engine data were provided by the company W&-tsiEi.
36 The improvement of modem turbo-chargers makes it possible to provide a fuel cell with air, waste gated from the gas engine’s flow surplus. The FC can hereby be pressurized by the air flow, and its efficiency and power output will be increased. Pressurizing a FC to 3 bar, using a compressor with 80% isentropic efficiency, consumes about 25% of the electrical output of the FC. The synergetic effect of the integration of the FC and the SI- engine results in considerably increased efficiency and power output for the hybrid plant.
The hybrid plant has been modeled and simulated in the heat and mass balance program lPSEpro, by varying the amount of airflow flom the turbo-charger and optimizing the pressure level of the bottoming cycle. It is shown that the integration of a SOFC and an existing gas engine results in increased electrical efficiency. The magnitude of this efficiency increase depends on the power output (i.e. size) of the SOFC.
The paper was mainly written by the author. The co-author Thomas Hiigghmd, from W5rtsi15, suggested the method for integration of the fiel cell into the combined cycle.
Paper 3: Increasing thermal eficiency of a PFBCpowerpiant using a natural gas fueled gas turbine.
The highest allowable temperature in a Pressurized Fluidized Bed Combined cycle (PFBC) plant is limited to 850° C for two reasons, to utilize an uncoole~ robust gas turbine, and to avoiding fuel ashes from melting. This temperature limits maximum achievable electrical efficiency of the PFBC-plant. Paper 2 presents a study where possibilities to brake through the efficiency limit by completion of the PFBC-plant with a modem natural gas fieled gas turbine have been investigated. Standard gas turbines and Heat Recovery Steam Generators (HRSG) of different sizes were used to model a hybrid plant in the heat and mass balance program IPSEpro. Plant data for the PFBC- plant was provided by the company ABB STAL (ALSTOM). The steam generated in the HRSG was mixed with reheat steam from the PFBC-plant and expanded in the common part of the hybrid plan~ i.e. the intermediate- and low-pressure steam turbine. Calculation results show that the efficiency of the hybrid plant is higher than the stand- alone PFBC-plant’s. The marginal efficiency, defined as the ratio behveen the increased electrical effect and the increased fuel input, for the hybrid plan~ based on the GTX1OO gas turbine without post combustion, is of the same magnitude as the efficiency of the standard combined cycle based on the same gas turbine. The high marginal efficiency for the hybrid plant is achieved using a simple HRSG with one pressure level, while the standard combined cycle is utilizes a more complex HRSG. This is the most important synergetic effect of the hybrid plant. Additional synergy effects of this concept are increased availability, due to the presence of two independent power generation systems, reduced COZ emission, due to the lower carbon/hydrogen ratio of the natural gas, as well as reduced investment costs, due to the integration into the already existing steam plant, e.g. steam turbine, condenser and auxiliary machinery.
This paper is result of the cooperation with the colleagues horn former ABB STAL. The paper was mainly written by the author. The co-authors contributed with the real plant data and participated in analysis of the calculation results.
37 Paper 4: Integration of biomass-ftleled power plants and SI-eng”nes, a method for increasing power outputfiom existing plants.
This paper presents results of a study performed to increase the power output of existing biomass-fueled combined heat and power plants through extension of the plant by natural gas fieled spark ignition engines. Thermodynamic and economic data for the SI- engine were provided by the company W5rtsilii NSD.
The Swedish government has decided to phase out nuclear power by the year 2010. Bearing in mind that about 46% of the electrical power in Sweden is generated by nuclear power plants, it is easy to understand the proportions of this issue. It is important that the replacement of the electricity from nuclear plants does not increase the emission burden on the environment. Therefore this study was carried out to model typical biomass-fueled power plants in the heat and mass balance program, extend them by a specific SI-engine, and to investigate the synergy effects and investment costs of the integrated system.
The gas fieled SI-engine used in this study is a Wiirtsi15 16V34SG. Calculation results show that the total power output of the integrated system is larger than the sum of the ,. electricity produced separately by the SI-engine and the biomass- fheled combined heat and power plant. It is shown that in the hybrid pkm~ synergy can be achieved for both electrical power output and the electrical efficiency. Further advantages of the hybrid plant are short construction time and the relatively low investment cost per kW installed electrical power. Achieving the highest possible electrical efficiency requires different ways of integration of the CHP plants and the SI-engines, and is dependent on, among other parameters, the size and the coupling schemes of the CHP plants.
The marginal efficiency, defined as the ratio between the increased electrical effect and the increased fiel inpu~ of the integrated systems is in the order of 50%. An economical investigation shows that the current electricity price in Sweden is too low (24$fMWh) to motivate the repowering suggested here. However, it is most likely that the electricity price is going to increase afier a nuclear power phase-out. The studied concepts become economically interesting for the electricity prices higher thin 36$lMWh.
This paper was mainly written by the author. The author chose the plants to be analyzed and provided necessary data. Jarmo Mauno from Wiirtsilli perllormed the heat and mass balance calculations, Andreas Magnusson carried out the economical analysis and the co-author, Tord Torisson, participated in analysis of the results and conclusions.
Paper 5: Air bottoming cycle for Gas Turbines.
This paper presents results of a study where an Air Bottoming Cycle (ABC) has been modeled in the heat and mass balance program PROSIM, and its potential to be an alternative for electricity production in the Scandinavian market has been investigated. Several layouts for combined heat and power plants have been generated. The effect of the gas turbine’s size on the plant efficiency and economy has also been investigated. Calculation results show that the ABC-cycle achieves an electrical efficiency in the order of 46°/0 and the fiel utilization in CHP-plants is more than 80°/0.It is also shown
38 that emission of the greenhouse gas COZ is reduced by 20% compared to conventional CHP-plants. The economical investigation shows that the production price for electricity generated by the ABC-cycle (14 US$/MW) is higher than the price of the power available at the market. ,.
This paper was mainly written by Jaime Arriagada. The author contributed to analysis and evaluation of the studied systems and the calculated results.
Paper 6: A Computational Investigation of a Biomass-Fueled Integrated Gasz~cation Cascaded Humid Air Turbine, Bio-IGClZ4Z
This paper presents results of a study where a power plant containing a cascaded humid air turbine, an atmospheric gasification unit, and an exhaust gas dryer for biomass was modeled and simulated in the heat and mass balance program ASPEN Plus. Calculation results for the natural gas fueled CHAT-cycle correspond to published results. The impact of replacement of natural gas by gasified biomass has also been investigated. It is shown that the electrical efficiency for the biomass-fieled CHAT-cycle is higher than 40% at zero COZemissions.
This paper, which summarize results of Andre Hildebrandt’s final thesis was mainly written by the author. Hildebrant petiormed calculations and modeling of the studied system under authors supervision.
Paper 7: A Novel Correction Technique for Simple Gas Turbine Parameters.
Data normalization for gas turbines is necessary for comparison of test data collected at various environmental conditions. This paper presents results from a study where a simple gas turbine (VOLVO, VT600) has been modeled by an artificial neural network, using measured data at various environmental conditions. Engine pefiormance maps containing pefiormance data at ISO-conditions have been generated by nonlinear interpolation and extrapolation. Comparison of the normalized/experimental daq results provided by thermodynamic models using heat and mass balance programs and results generated by the Artificial Neural Network model shows a high level of consistency.
This study shows that artificial neural networks are useful tools for generation of accurate power plant models and performance maps for specific engines. It also shows that data normalization can be carried out easily and accurately by using performance maps. The engine performance at conditions other than the 1S0 can also be predicted.
The paper, was mainly written by the author and Ehsan Mesbahi, from the University of Newcastle. The author and Mesbahi performed the modeling and analysis of the studied phm~ using an ANN tool. The ANN progr amming was pefiormed by Mesbahi. Several colleagues at the department of Heat & Power Engineering in Lund also contributed to this paper. Pernilla Olausson and Jairne Arriagada performed heat and mass balance calculations in PROSIM and IPSEpro, respectively. Torbjom Lindquist contributed by measurement data, and Tord Torisson participated in analysis of the results.
39 Paper 8: A Unique Correction Technique for Evaporative Gas Turbine (EvGT) Parameters.
In this paper results from a study concerning data normalization for the evaporative gas turbine are presented. The data normalization was pefiormed by using a model genemted by an artificial neural network. The EvGT works with a mixture of air and water steam as working medium, and is thereby strongly affected by the changes in the environmental conditions that affect the water content of the working fluid. The normalization process for the EvGT becomes very difficult since the properties of the working fluid are changing continuously. To achieve high accuracy during the data normalization, it is necessary to take these changes into account. Measurement data from the EvGT plant in Lund, Sweden, have been used for the generation of a mathematical model by an ANN. Performance maps generated by the ANN have been used successfully for data normalization of the EvGT. This study shows that the ANN is capable of capturing the system behavior and modeling the system. The ANN model can be used to generate performance maps, providing prediction of the system performance at a wide range of operational conditions.
The paper, was mainly written by the author and Ehsan Mesbahi, from the Universit y of Newcastle. The author and Mesbahi performed the modeling and analysis of the studied plant, using an ANN tool. The ANN pro -g was perfo~ed bY Mesb*i” The co- authors Torbjom Lindquist and Tord Torisson contributed by measurement data, and analysis of the results respectively.
40 Appendix 1
The possibility to choose the input data freely from a list, containing all system data, , ., increases the user-friendliness of the analysis tools considerably. The tool utilizing free input data, presented in section 4.1.3, requires that the system equations are presented as a sequence of basic mathematical operations (e.g. addition, subtractio~ division, etc.). This tool also performs control of the calculation processes prior to the execution, preventing forbidden mathematical operations, like division by zero, which generates an undesired interrupt. The example below shows the programming technique used in the analysis tool, presented in the section 4.1.
Example: The energy balance for a component presented in Figure 1 can be written as:
ml. hl+-m2. h2=mj. h3 (A)
Figure 4: Component model with in- and out-flows
The energy balance (A) contains six variables. Calculating any one of them, knowing the other five, demands the following mathematical presentation in the program code:
Rewriting equation (A): ml = (m3. h3 - ml. h&il hl = (m3. h3- m2. h#ml m2 = (m3. h3 - ml. hJh2 h2 = (m3. h3 - ml. hl)/ml mj = (ml . hl + ml. hJh3 h3 = (ml. hl +-m~. hj/m3
Apparently, this should result in an unacceptably large program code, demanding lots of man hours and slowing down the calculation speed. To overcome these problems, every calculation step can be presented as a combination of basic mathematical operations. An example for such a basic operation is the DMSION.
To petiorm the division “C= A /B”, allowing free choice of daq operational control, and error tracing back to the equation where the error has appeare~ the following approach is required:
41 The division” C = Al B” can be rewritten as: A=B. C and B=AIC covering all possible ways to present the basic operation, DIVISION. Independent of which two parameters are known, the third one can be calculated in a calculation procedure containing all these three expressions.
A difficulty associated with the free choice of input data is that the user can fix too many parameters prior to the calculation. The variable properties presented in chapter 4 can be utilized to prevent overwriting the user-defined values by calculated ones, in the case that the difference between those two is greater than a pre-defined tolerance. If the difference exceeds the tolerance, an error message is generated. The procedure CHECK used in the program code below, performs the control prior to the assignment of a numerical value to a variable (see the reference [l]).
Procedure Divzkiou (var A,B, C:variabeltype); var D:variabeltype; begin {C=AIB} if (A.knownOunknown) and (B.knownounknown) then {If A and B have received numerical values} begin if B.valueOO then {IfB’s value is not equal to zero} begin D.knowm= calculated; D.value:=A.value/B value; check(D,C); {The procedure CHECK generates an error message if there are any conflicts} end else Message(’ Division by Zero !’); {In the case that B’s value is equal to zero, the error message is shown on the screen) end {The rest of the code handles the rewritten equations} else if (C.knownO unknown) and (13.knowno unknown) then begin D.knowrr= calculated; D.value:=C.value*B value; check(D,A); end else if (C.knownO unknown) and (A.knowno unknown) then begin if C.valueOO then begin D.knowm= calculated; D.value:=A.value/C.value; check(D,B); end
42 else Message(’ Division by Zero !’); end; end; {End of the program code}
Using basic mathematical operations in the form presented above, makes it possible to generate the following program code to calculate the energy equation
ml=(m3. h3-m2. h2)/hl
Multiplication (m3, h3, al); {Replacing the: al=m3.h3, maintaining free choice of data} Multiplication (n12,h2, a2); Subtraction (al, a2, a3); Division (a3, hl, a4); ,... Check (ml, a4);
The enthalpies, used in the energy balance equation, are supplied by material property databases within the program.
43
Appendix 2
Local linearization
Heat and mass balance programs (HMBPs) usually contain component models for part- Ioad calculations, parallel to the models used for the design calculations. The part-load models utilize, besides mathematical equations, tables and curves describing the components’ behavior at part-load. Generally, part-load calculations are pefiormed by iterative calculation procedures, with the uncertain convergence as a consequence.
An alternative approach for part-load crdculation is local linearization. This method is based on the assumption that the system parameters vary linearly in between two part- load levels close to each other. A part-load calculation based on local linearization requires differentiation of the system equations. Selection of differentiation parameters is an important task that should lead to an unambiguous determination of the remaining system parameters at any operational condition. Water steam and exhaust gases are the most common working media for the thermal power plants. A detailed study of the material properties in the steam table shows that there are only two parameters, namely pressure and enthalpy, that can be used to determine the remaining parameters of the working medium in the entire validation range [1].
Equation (1) and (2) present differentiation of the temperature and entropy with respect L to pressure and enthalpy
AT= (dT/dp). Ap + (dT/dh)- Ah (1) As= (ds/dp). @ + (ds/dh). Ah (2)
The temperature and the heat capacity of exhaust gases, are fictions of enthalpy only, since the entropy is a fanction of pressure and enthalpy, i.e.
T= ffi), Cp= ffi), S=f(P,h)
The differentiation takes the following form:
AT= (dT/dh). Ah (3) AC, = (dCP/dh).Ah (4) As= (ds/dh). Ah (5)
Further rewriting of equations 3,4, and 5 gives: dT/dh = l/CP (6) ds/dT = CP/T (7) ds/dh = l/T (8)
45 Entropy changes, caused by changes in pressure, can be presented as follows:
R.ln(pO/p)= R.(lnpO-lnp) (9)
R in equation 9 is the gas constant.
The differentiation results ix b= (ds/dh).Ah - (R/p). Ap (lo)
Linearization of the equations, modeling the “STEAM TURBINEK
The example below presents the application of the linearization technique to the steam turbine.
The total shaft power of a steam turbine can be noted as:
P=iz. (bin-hOU,) (11)
The isentropic efficiency of the steam turbine is defined as:
q,= (hin– hO.r-rdO)/thin - ‘i) (12) where the index (i) denotes the isentropic properties, (in) the property at the turbine inlet, and (out) at the turbine outlet.
Stodola’s equation (13) is needed to perform part-load calculations for the steam turbine:
m A4w pin Pin_O “‘in_O _—— Ii&r (13) ((n+l)ln) ~ - m. pin_o Pin “‘in ~ Pow-o Pin_O ‘h
where MW is the molecular weigh~ m is the steam flow, v is the volumetric flow and (n+l)/n is the polytropic exponent. Indexes in and out, denote the properties at turbine inlet and outlet, and index W“, refers to the design condition.
46 A simplified version of equation 13, presented below, is usually used for part-load calculations:
2 1 Pout lh —=— Pill J-L Pin (14) 2 ~zo Pin-o Po”I_o 1- — Pin_o r[1 The linearized model of the steam turbine contains the following equations:
Rewriting equation 14:
P: = P:., +Kz “rn* (15) where K is:
Differentiating (15) gives:
The entropy is unchanged during an isentropic expansion, i.e.:
S,n =s, that gives: Asin= ‘i (17)
Differentiation gives:
A.stn= (ds/@)p,n,h,n“@in+ (ds/dk)~,n,h,n “‘ii. (18)
As, = (ds/dP)POU,,ii-~,n + (ds/dh)~ou,.h, “‘ii (19)
Equations (17), (18) and (19) @~ i ,:,. .,,, (ds/d’)p,n,h,n‘&,. +(ds/dh)p,n,hin“~,. = (ds/c?P)pOw,fii“@in ‘(ds/dh)Pou,,hi “% (20)
Rewriting equation (12) gives: h, = h,. “(1–n,)+how/~i (21)
47 Differentiating (21) gives:
Ahi= Ahin“ (1–l/??i)+ Aho”,/qi (22)
Equations (20) and (22) give:
(ds/dP)pin,/Iin“@in‘(ds/dh)pin,/Iin‘“in = (23) (ds/d~)pou,,/Ii‘&in+ (ds/dh)pou,,hi“(”i. -(l-l/qi)+ ‘..f/qi)
The mass balance equation for the steam turbine and its differentiation give: hi” = Li~u,* Alilin= Ailou, (24)
Equations (16), (23) and (24) with the following notation give the linearized matrix below, representing the steam turbine:
‘4= (Wldpin,b,n
B = (~S/@)pin,~,n–(ds/dh)pou,,hi“(1- Wi ) c= –(w@pou,,hi
D = -(~s/cJ~)POu,,hi “lh?i
4Pin Ahin AB o CDO &lin ~n O –K2-rn ‘pOu, O 0 (25) 4P.. 00 1 0 0 –1 [ [] Ahou, Alilou
To perform part-load calculations, the system matrix, containing all component matrices, is to be solved. To calculate the system matrix, the design data (at 100% load) are used as the start point. When the step size to the next part-load is determined (e.g. step size equal to 1’%gives 99’% as the next load level), changes in pressure (Ap), enthalpy (M) and mass flow rate (M), needed to achieve the next part-load, are calculated for every point within the power plant system. Decreased step size increases the accuracy of the calculation results. Knowing the magnitude of tie changes gives the possibility of calculating the cun-ent values of pressure, enthalpy and mass flow rate for this part-load level. Calculation of the remaining parameters of the system is then performed, using the method presented in appendix 1. The whole procedure is repeated, using data fi-om the previous load level as the start point, until the desired part-load level is achieved.
48 Results of part-load calculations, covering the entire operational range of an existing system, can be stored and presented as curves and tables, making new calculations unnecessary.
: ..
j“, - ,,, ,
49 WI o Reference list
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53
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List of Papers
1. Assadi M., Johansson K. B., Applying Pinch Method and Exergy Analysis to a BIO- IGIZ4T Power Plant, 2ndConference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction, PRES’99, pp. 139-144, Published by Hungerian Chemical Society, BudapesC Hungary, May31 - June 2, 1999.
2. Assadi M., H5gglund T., A computational investigation of a combined cycle using an SI- enghze, a Rankine bottoming cycle and afuel cell, European Fuel cell news, Newsletter of the European Fuel Cell Group, Ltd., Volume 6, Number 2, pp. 5-7, July 1999.
3. Assadi M., Jansson S. A., Blomstedt M., Increasing thermal ej?ciency of a PFBC power plant using a natural gas fueled gas turbine, The First Iutemationa Symposium on Computer Aided Process Engineering, ISCAPE 2000, Cartagena de india, Colombia, January 24-28,2000.
4. Assadi M., Torisson T., Integration of biomass-fueled power plants and SI-engines, a method for increasing power output from existing plants, 4fi International Conference of Iranian Society of Mechanical Engineers, ISME 2000, pp. 409-412, Telmq Iran, May 16- 19,2000.
5. Arriagada J., Assadi M., Air bottoming cycle for Gas Turbines, 4ti International Conference of Iranian Society of Mechanical Engineers, ISME 2000, pp. 447-454, Tehran, IrruL May 16-19,2000.
6. Assadi M., Hildebrandt A., A Computational Investigation of a Biomass Fueled Integrated Gasljication Cascaded Humid Air Turbine, Bio-IGCH4T, to be presented at 14ti International Congress of Chemical Engineering, QUBEC 2000, Sao Paulo, Brazil, September 24-27,2000.
7. Assadi M., Mesbahi E., Torisson T., Lindquist T., Arriagada J., Olausson P., A Novel Correction Technique for Simple Gas Turbine Parameters, submitted to ASME TURBOEXPO 2001, New Orleans, USA.
8. Mesbahi E., Assadi M., Torisson T., Lindquist T., A Unique Correction Technique for Evaporative Gas Turbine (EvGT) Parameters, submitted to ASME TURBOEXPO 2001, New Orleans, USA.
55