Integrated modelling for improving the design and operation of steam power plants

Integrated modelling for

improving the design and

operation of steam power

plants

with a focus on increasing their efficiency

Integrated modelling for

improving the design and

operation of steam power

plants

With a focus on increasing their efficiency

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus Prof. dr. ir. J.T. Fokkema, voorzitter van het College voor Promoties,

in het openbaar te verdedigen op donderdag 28 februari 2008 om 15.00 uur

door

Johannes Gerhard VAN PUTTEN

werktuigkundig ingenieur

en de toegevoegd promotor: Dr. ir. P. Colonna

Samenstelling promotiecommissie:

Rector Magnificus voorzitter

Prof. dr. -ing. H. Spliethoff Technische Universiteit Delft, promotor

Dr. ir. P. Colonna Technische Universiteit Delft,

toegevoegd promotor Prof. dr. D.J.E.M. Roekaerts Technische Universiteit Delft Prof. ir. O.H. Bosgra Technische Universiteit Eindhoven Prof. dr. ir. A.A. van Steenhoven Technische Universiteit Eindhoven Prof. dr. ir. T.H. van der Meer Universiteit Twente

Prof. C. Cort´es University of Zaragoza

This research has been partly funded by the European Commission under the Research Programme of the Research Fund for Coal and Steel, Contract Nr. RFC-CR-03007.

ISBN978-90-9022784-9

Copyright c 2008 by J.G. van Putten1

All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission of the author.

”It is our choices...that show what we truly are, far more than our abilities.”

–Albus Dumbledore– Harry Potter and the Chamber of Secrets

Summary

Integrated modelling for improving the design and operation of

steam power plants

Power producing companies have faced a new situation following the de-velopment of unregulated electricity markets. Strong competition forces them to take power production costs into account more carefully in order to maintain or increase profits. Additionally, to promote the efforts to reduce CO₂emissions, legislation and taxation drives operators to increase the use of coal and renew-able energy sources more efficiently and over a wider range. This results in an increasing interest in the use of solid-biomass-based fuels in pulverized coal-fired power plants. Therefore, fuel flexibility due to the combustion of varying relative shares of coal and biomass poses new challenges for plant operators. The understanding of deposition formation and its behavior and consequences has become a key issue in optimizing plant operation and in securing plant per-formance and availability.

The investigation presented in this dissertation was carried out as part of the EU-ADMONI project. The overall objective of the work presented here is the development of advanced monitoring models for solid fuel fired steam power plants. To obtain such advanced monitoring methods, three kinds of models are to be coupled with each other. They are: Process Monitoring Models, Dynamic

Process Models and 3D Boiler Models. This coupling consists of the exchange

of different boundary conditions (BCs) with each other. Although the develop-ment of an overall monitoring methodology has been outside the scope of this work, this dissertation contains descriptions and examples of the three sepa-rate modelling techniques needed to obtain the overall monitoring methodol-ogy which can be used for improving the operation of steam power plants. A short description of the three modelling techniques applied and their results are given below.

ei-all steam cycle efficiency of the power plant at hand, using the process data pro-vided by the Data Acquisition System (DAS) of the plant. The novelty of the mod-els presented in this work is the investigation of the possible use of exergy anal-ysis within the existing monitoring models to detect a possible decay of plant performance due to arising operational problems.

A validation of the models against experimental process data has been per-formed, and the predicted trends mimic the experimental data very well during steady operation. However, the difference between model predictions and ex-perimental data can reach considerable values. Under abnormal operation, the model is not capable of predicting the boiler behavior correctly. Furthermore, a proof of concept has been presented for using exergy and energy analysis to study the performance of solid fuel fired steam power plants. The possibility of incorporating these analyses in diagnostic tools has been shown. However, applying exergy analysis to study diagnostics during power plant operation, re-mains an open question.

Dynamic process models of the steam cycle have been developed to provide insight in the physics and plant behavior when (co)-firing varying biomass and coal blends. The results of these models can be very important for the design of the control system of plants which are co-firing these kinds of mixtures. Phys-ically based models have been formulated, since no historical data is available in the design phase, and thus black box models were not an option. A method-ology for the dynamic modelling of energy conversion systems has been pro-posed, and the modelling of a simple single pressure, small, steam power cycle, has been done as a ”proof-of-concept”. Since the issue is receiving more and more attention, it is foreseen that the methodology described here can also very well be used to study power plants with improved performance and response.

The correctness of the main concepts and of their implementation into com-puter code has been proven by the simulation results of a lab-scale steam cy-cle setup for which dynamic measurements are available. Hereafter, a feasibility study describing the development, implementation and validation of the dy-namic model of a small biomass-fired power plant has been demonstrated. This model has been validated by performing several exemplary simulations starting from on-design and off-design stable operation. A quantitative validation has been presented by comparison with an a-causal dynamic modelling paradigm for energy conversion systems. Finally, a successful qualitative dynamic valida-tion is presented by showing the response of the system to few selected step-wise changes of the input variables.

An accurate methodology for the simulation of 3D pf combustion has been developed to accurately predict the flow and temperature fields inside the boiler. To be able to accurately predict these phenomena, detailed combustion models are necessary. Various combustion models for solid fuel combustion have been proposed in literature, but an extensive validation of these models is

hardly given. Therefore, several combustion models have been validated against experimental data and the most accurate model has been used for full scale 3D boiler simulations.

Results have shown that the EDC combustion model mimics the experiments the best. Furthermore, the prediction of CO formation is not cor-rect for all investigated combustion models. A grid size dependency study has shown that it is very hard to obtain a grid independent solution in the near flame region. However, a very good agreement with the experimental data is observed in the other parts of the furnace. Hereafter, the EDC model, has been used to simulate pf combustion of a 350 MWe coal fired power station. The simulations of this power station have been performed using two commercial CFD codes, and results for temperature and velocity fields have shown to be very similar for both software packages. Therefore, the applied modelling methodology for pf combustion seems to be autonomous to which CFD code it is adopted. For the predictions of pollutant formation, however, this is not the case.

Hans van Putten

Samenvatting

Ge¨ıntegreerde modellering ter verbetering van het ontwerp en

de werking van stoomkrachtcentrales

Energie producerende bdrijven moeten een nieuwe situatie, volgend uit de ontwikkeling van de niet gereguleerde elektriciteits markt, onder ogen zien. Hevige concurrentie dwingt ze om nauwkeuriger rekening te houden met en-ergie productie kosten, teneinde eventuele winst in stand te houden of te ver-groten. Bovendien stuwen wetgeving en belastingen exploitanten in de richt-ing van effici¨enter gebruik en een breder bereik van kolen en duurzame energie bronnen, om de inspanningen te bevorderen die CO₂uitstoot beperken. Dit re-sulteert in een toegenomen belangstelling voor het gebruik van vaste biomassa brandstoffen in kolen gestookte energiecentrales. De daarvoor benodigde brandstof flexibiliteit door de verbranding van een varirend aandeel kolen en biomassa zorgt voor nieuwe uitdagingen voor procesoperators in energiecen-trales. Het begrijpen van depositie vorming en het gedrag en de gevolgen hier-van zijn een essentieel onderdeel geworden bij het optimaliseren hier-van de proces werking en bij het veilig stellen van de performance en inzetbaarheid van de centrale.

Het onderzoek wat in dit proefschrift gepresenteerd wordt is uitgevoerd als onderdeel van het EU-ADMONI project. De overkoepelende doelstelling van het hier gepresenteerde werk is de ontwikkeling van geavanceerde monitoring modellen voor vaste brandstof gevuurde stoomkrachtcentrales. Om zulke gea-vanceerde monitoring methodes te verkrijgen is het nodig drie verschillende soorten modellen met elkaar te combineren. Deze zijn: Proces Monitoring

Mo-dellen, Dynamische Proces Modellen en 3D Boiler Modellen. De koppeling tussen

de verschillende modellen bestaat uit het uitwisselen van verschillende rand-voorwaarden met elkaar. Alhoewel gebleken is dat de ontwikkeling van een overkoepelend monitoring systeem buiten de proporties van een enkel proef-schrift valt, bevat dit proefproef-schrift beschrijvingen en voorbeelden van elk van

centrales te verkrijgen. Een korte beschrijving van elk van de drie modelleer technieken die toegepast zijn, en hun resulaten, wordt hieronder gegeven.

Proces monitoring modellen zijn door de jaren heen ontwikkeld om warmte overdrachts weerstanden (deze zijn een indicator voor de hoeveelheid depositie vorming) van de warmtewisselende apparatuur van de stoom kringloop of het totale rendement van de stoomkrachtcentrale te voorspellen of te monitoren, door gebruik te maken van proces data verkregen van het Data Acquisitie Sys-teem (DAS) van de centrale. De noviteit van de modellen die in dit werk ge-presenteerd worden ligt in het onderzoeken van het mogelijke gebruik van ex-ergetische analyse binnen de al bestaande monitoring modellen, om een mo-gelijke afname van prestaties van de centrale door operationele problemen te ontdekken.

Een validatie van de modellen ten opzichte van experimenteel verkregen proces data is uitgevoerd, en de voorspelde trends volgen de experimentele data erg goed gedurende constante werking van de centrale. Het absolute verschil tussen model voorspellingen en experimentele data kan echter wel aanzienlijke waarden aannemen. Het model is niet in staat het boiler gedrag correct te voor-spellen gedurende onregelmatige werking van de centrale. Daarnaast is er een ”proof-of-concept” bewijs gepresenteerd voor het gebruik van exergetische en energetische analyse voor het bestuderen van de performance van met vaste brandstof gevuurde stoomkrachtcentrales. De mogelijkheid van het gebruik van deze analyses in diagnostische tools is aangetoond. De toepasbaarheid van ex-ergetische analyse voor het bestuderen van performance wanneer de energie centrale in operatie is, blijft echter een open vraag.

Dynamische proces modellen zijn ontwikkeld om inzicht te verkrijgen in de fysica en het gedrag van de centrale als varirende biomassa en kolen mengsels (mee)gestookt worden. De resultaten van deze modellen kunnen van grote waarde zijn bij het ontwerpen van het controle systeeem van centrales waar dit soort mengsels (mee)gestookt worden. Op fysica gebaseerde modellen zijn geformuleerd, aangezien historische data over het algemeen niet aanwezig is in de ontwerpfase, en een ”black box” formulering dus nooit een optie is geweest. Een methodiek voor het dynamisch modelleren van energie conversie systemen is voorgesteld, en de modellering van een eenvoudige kleine stoomkringloop met een enkel drukniveau is gepresenteerd als een ”proof-of-concept” bewijs. Aangezien de kwestie steeds meer aandacht begint te krijgen, wordt het voorzien dat de methodiek die hier beschreven is in de toekomst ook erg goed toepasbaar zal zijn voor het bestuderen van verbeterde performance en respon-sies van energiecentrales .

De correctheid van de belangrijkste begrippen en van hun implementatie in computer code is aangetoond door validatie van de simulatie resultaten voor een lab-schaal stoom kringloop opstelling waarvoor dynamische meetdata beschikbaar zijn. Hierna is er een haalbaarheidsstudie uitgevoerd, die de

ontwikkeling, implementatie en validatie van een dynamisch model voor een kleinschalige biomassa gevuurde energiecentrale beschrijft. Dit model is gevalideerd door verscheidene voorbeeld simulaties uit te voeren, startende vanuit ”design” en ”off-design” stabiele werkpunten van de kringloop. Een kwantitatieve validatie is gepresenteerd door middel van vergelijking met re-sultaten verkregen met een a-causaal dynamisch modelleer paradigma voor en-ergie

conversie systemen. Tot slot is er een succesvolle kwalitatieve validatie gepre-senteerd door de responsie van het systeem op stapsgewijze veranderingen van input variablen te beschouwen.

Een nauwkeurige methodiek voor het simuleren van 3D pf verbranding is ontwikkeld om het stromings- en temperatuursveld in de ketel van de centrale nauwkeurig te kunnen beschrijven. Om deze verschijnsels nauwkeurig te kun-nen voorspellen zijn gedetailleerde verbrandingsmodellen nodig. Verscheidene verbrandings modellen voor vaste stof verbranding zijn voorgedragen in de lit-eratuur, maar een uitgebreide validatie van deze modellen wordt nauwelijks gegeven. Daarom zijn enige van deze verbrandingsmodellen hier gevalideerd met behulp van experimentele data en het meest nauwkeurige model is daarna gebruikt voor de simulatie van een 3D boiler op volledige schaal.

De resultaten van de simulaties hebben laten zien dat het EDC verbrand-ingsmodel de experimentele data het beste benadert. Daarnaast blijkt dat de voorspelling van CO formatie incorrect is voor alle onderzochte verbrandingsmodellen. Een gridonafhankelijkheidsstudie heeft aangetoond dat het erg lastig is om een gridonafhankelijke oplossing te vinden in de nabijheid van de vlam. In de andere delen van de ketel is echter een erg goede overeenkomst tussen de experimentele en de voorspelde data waar te nemen Hierna is het EDC model gebruikt om de verbranding van een 350 MWe kool gestookte energie centrale te simuleren. De simulaties voor deze centrale zijn uitgevoerd met twee commerciele CFD codes, en de resultaten voor de stromings- en temperatuursvelden laten grote overeenkomsten zien voor beide software pakketten. De toegepaste methode voor het modelleren van pf ver-branding lijkt derhalve algemeen toepasbaar voor elk willekeurige CFD pakket. Voor het voorspellen van de vorming van (vervuilende) uitlaatgassen gaat dit echter niet op.

Hans van Putten Delft, 28 Februari 2008.

Table of Contents

Summary i

Samenvatting v

1 Introduction 1

1.1 Global Energy Trends . . . 1

1.2 Energy Outlook of The Netherlands . . . 4

1.3 Energy Conversion by Combustion of Solid Fuels . . . 5

1.4 The Efficiency of PF Fired Steam Power Plants . . . 7

1.4.1 Trends in Steam Power Efficiency . . . 8

1.4.2 Operational Problems . . . 9

1.4.3 Improving Plant Design, Operation, and Control . . . 10

1.5 Motivation and Scope of the Thesis . . . 12

1.6 Thesis Outline . . . 16

I

Improving power plant performance and operation using

online thermodynamic cycle monitoring

19

Nomenclature 23 2 Introduction 25 2.1 Background . . . 25

2.2 Applications of Steady State Cycle Simulation and Analysis . . . . 30

2.2.1 Design and Optimization Studies . . . 30

2.2.2 Online Plant Monitoring and Simulation . . . 32

2.2.3 Design vs. Off-design Calculations . . . 35

3.1.1 Working Principle of CycleTempo . . . 42

3.1.2 Case Studies and Model Validation . . . 47

3.2 Monitoring of Thermal Resistances . . . 55

3.2.1 Description of the Model . . . 55

3.2.2 Results and Discussion . . . 57

3.3 Monitoring of Steam Cycle Efficiency . . . 63

3.3.1 Description of the Model . . . 63

3.3.2 Results and Discussion . . . 66

Conclusions and Recommendations 73

II

Modelling the unsteady characteristics of small scale steam

power cycles

77

Nomenclature 81 4 Introduction 83 4.1 Background . . . 83

4.2 Dynamic Modelling of Small Capacity Steam Powered Plants . . . 84

4.3 Available Modelling Paradigms . . . 86

4.3.1 Distributed vs. Lumped Parameter Models . . . 86

4.3.2 Solving of DAEs and the Index Problem . . . 87

4.3.3 Modular vs. simultaneous approaches . . . 90

4.3.4 Causal vs. Non-causal Models . . . 91

4.3.5 Bilateral Coupling . . . 91

4.3.6 Available modelling languages . . . 95

4.3.7 Chosen approach . . . 96

4.4 Modelling Approach . . . 97

4.5 Dynamic Modelling throughout this work . . . 100

5 Dynamic Modelling of Small Scale Steam Power Plants 103 5.1 Validation: Comparison with a Lab-Scale Steam Cycle . . . 103

5.1.1 Laboratory Plant Layout . . . 103

5.1.2 SimECS Plant Model . . . 104

5.1.3 Experimental Validation . . . 105

5.2 Modelling of a Small Biomass Fired Steam Power Plant . . . 111

5.2.1 SimECS Model . . . 114

5.2.2 Modelica Model . . . 114

5.2.3 Specification of time-variables imposed to the system and model parameters . . . 117

5.2.4 Steady State Validation . . . 118

5.2.6 Qualitative Dynamic Validation . . . 121

Conclusions and Recommendations 129

III

Computational Fluid Dynamics modelling of pulverized

fuel (pf ) combustion

133

Nomenclature 137 6 Introduction 141 6.1 Background . . . 141 6.2 Solid Fuels . . . 142 6.2.1 Coal . . . 143 6.2.2 Biomass . . . 144

6.3 Solid Fuel Combustion Mechanism . . . 148

6.4 Modelling of Pulverized Fuel Combustion . . . 152

6.4.1 Devolatilization Modelling . . . 152

6.4.2 Gaseous Combustion Modelling . . . 156

6.4.3 Char Combustion Modelling . . . 160

6.5 CFD Modelling throughout this work . . . 167

7 Numerical Simulation of Pulverized Coal Combustion 169 7.1 Governing Equations and available Models . . . 169

7.1.1 Conservation Laws . . . 170

7.1.2 Turbulence Modelling . . . 172

7.1.3 Turbulence-Chemistry Interaction . . . 173

7.1.4 Discrete Particle Phase Modelling . . . 176

7.1.5 Radiation Modelling . . . 178

7.2 Validation of Coal Combustion Models against Benchmark Exper-iments . . . 181

7.2.1 Description of the Geometry . . . 181

7.2.2 Operating and Boundary Conditions . . . 182

7.2.3 Used Models . . . 182

7.2.4 Results and Discussion . . . 186

7.3 Modelling of a Large Scale Pulverized Coal Fired Power Plant . . . 193

7.3.1 Description of the Geometry . . . 194

7.3.2 Operating and Boundary Conditions . . . 196

7.3.3 Results and Discussion . . . 196

A Input Parameters For The Monitoring Models 213

B An Example Of Interconnecting Modules 217

C Model Parameters 221

C.1 Laboratory Plant . . . 221 C.2 Biomass Fired Steam Power Plant . . . 222 D Steady State Validation of the Biomass Power Plant Model 225

E Grid-size Dependency Study for the Lab Scale Burner 231

F Further Validation of the Lab Scale Furnace Model 235

G Species contours for the 350 MW pf fired power plant 243

Bibliography 247

Publications 271

Curriculum Vitae 273

Chapter

1

Introduction

1.1

Global Energy Trends

Climate change, as a result of rising greenhouse gas emissions, threatens the stability of the world’s climate, economy and population. More than two thirds of the world’s CO₂emissions come from the way energy is produced and used. The causes and consequences of climate change are global, and while national governments can (and should) take action, the ultimate solution must be a col-lective global effort. Based on current trends, the global emissions are expected to reach the double of pre-industrial levels before 2050, having severe impacts on our climate and the global economy. Investigations on the issue show that in the long-term the cost of inaction would be far higher than the cost of tackling climate change now [128; 129].

At the same time energy demand worldwide continues to increase, partic-ularly in the United States and emerging economies, such as China and India. On the basis of present policies, global energy demand will be more than 50% higher in 2030 than today, with energy related greenhouse gas emissions around 55% higher [129]. Even if more potential for increasing low carbon sources of energy is realized, it is clear that coal, oil and gas will play a significant part in meeting the world’s energy needs for the foreseeable future, and it is necessary to reduce their emissions. Also, with many countries increasingly reliant on im-ported energy, the risks arising from the concentration of fossil fuel reserves in fewer and further away places, some of them in less stable parts of the world, need to be managed.

Global primary energy demand in the reference scenario [129] of the

Inter-national Energy Agency (IEA) is projected to increase by 52% from 2003 to 2030,

reaching 16.3 billion tonnes of oil equivalent (toe). This increase in demand is 1.6% per year on an average basis, compared to an average of 2.1% per year over

the period 1971-2003. Fossil fuels will continue to meet the overwhelming bulk of the worlds energy needs as is shown in Fig 1.1. Oil, natural gas and coal ac-count for 83% of the increase in world primary demand over 2003-2030. The share of nuclear power decreases from 6.4% to 4.7%, while the share of renew-able energy sources including traditional biomass is projected to increase from 13% to 14%. The share of non-biomass, non-hydro renewables increases from 0.5% in 2003 to 1.7% in 2030.

0

1 000

2 000

3 000

4 000

5 000

6 000

1970

1980

1990

2000

2010

2020

2030

Mto

e

Oil

Coal

Gas

Nuclear

Hydro

Other renewables

Figure 1.1: World primary energy demand by fuel in the reference scenario. [129]

Oil remains the single largest fuel in the global primary energy mix in the

ref-erence scenario as is shown in Table1.1. However, its share will decrease marginally, from 35% in 2003 to 34% in 2030. The oil demand is projected to grow by 1.4% per year.

The primary demand for natural gas will grow by 2.1%, meaning that gas will overtake coal by around 2020 as the worlds second-largest primary energy source. Gas consumption will increase by three-quarters between 2003 and 2030, reaching 4 789 billion cubic meters. The share of gas in world energy de-mand will rise from 21% in 2003 to 24% in 2030 mostly at the expense of coal and nuclear energy. Power generation will account for most of the increase in gas demand over the projection period because, in many parts of the world, gas will be the preferred fuel in new power stations for economic and environmen-tal reasons. A small but increasing share of gas demand will come from gas-to-liquids plants and from the production of hydrogen for fuel cells.

1.1 Global Energy Trends

Table 1.1: World primary energy demand in the reference scenario (Mtoe). [129]

2003-1971 2003 2010 2020 2030 2030∗ Coal 1439 2582 2860 3301 3724 1.4% Oil 2446 3785 4431 5036 5546 1.4% Gas 895 2244 2660 3338 3942 2.1% Nuclear 29 687 779 778 767 0.4% Hydro 104 227 278 323 368 1.8%

Biomass and waste 683 1143 1273 1454 1653 1.4%

Other renewables 4 54 107 172 272 6.2%

Total 5600 10723 12389 14402 16271 1.6%

∗_{Average annual growth rate.}

(Mt) in 2003 to almost 7 300 Mt in 2030, an average annual rate of increase of 1.4%. Its share in world primary demand will still decrease a little, from 24% in 2003 to 23% in 2030. Power generation will remain the main driver of world coal demand.

The share of nuclear power in global primary energy demand will decline over the projection period. Few new reactors are expected to be built and several will be decommissioned. Nuclear power struggles to compete with other tech-nologies and many countries have restrictions on new construction or policies to phase out nuclear power. As a result, nuclear production is projected to peak around 2015 and then decline gradually. Its share of world primary demand will remain flat, at about 6%, through 2010 and then decrease to less than 5% by 2030. However, these projections remain very uncertain. Shifts in government policies and public attitudes towards nuclear power could mean that this en-ergy source plays a much more important role than projected in the outlook of the IEA.

The role of biomass and waste, much of which is used in traditional ways in developing countries, will decline slightly during the projection period. Their share of world primary energy demand will fall from 11% in 2003 to 10% in 2030, as they are replaced by modern commercial fuels. In absolute terms, the con-sumption of traditional biomass in developing countries will continue to grow. The use of biomass and waste will increase in power generation.

Other renewables, a group that includes geothermal, solar, wind, tidal and

wave energy is expected to grow faster than any other energy source, at an aver-age rate of 6.2% per year. However, they will still contribute marginally to meet-ing global energy demand in 2030. Their share in primary demand will grow from 0.5% in 2003 to 1.7% in 2030. Most of the increase in the use of renewables will be in the power sector.

More than two-thirds of the increase in world primary energy demand be-tween 2003 and 2030 in the reference scenario will come from the developing countries. Their demand growth will be more rapid than in the industrialized and transition economies, because their economies and populations will grow more quickly. Industrialization, urbanization and a shift in energy use from tra-ditional non-commercial biomass to commercial fuels will also boost demand. The developing countries’ share of global demand is expected to increase for all primary energy sources, except biomass. The developing regions’ share of world coal consumption is also projected to increase sharply, mainly because of booming demand in China and India. Coal demand will increase more rapidly in those two countries, which have large, low-cost resources, than anywhere else in the world. By 2030, China and India will account for 48% of total world coal demand, up from 40% in 2003. Oil demand in China is projected to increase almost 2.5 times over the projection period, to 13.1 mb/d in 2030. Natural gas demand will grow strongly globally and the share of gas in the primary fuel mix will increase in every region. The fastest rates of growth are expected to occur in China and India, where gas consumption is currently relatively low.

1.2

Energy Outlook of The Netherlands

According to the 2004 review of the IEA [128], in the year 2002, the total pri-mary energy supply (TPES) in The Netherlands was 77.9 Mtoe, which was an increase of 17% compared to the 1990 level. In 2002, natural gas accounted for 46% of the total supply, followed by oil (38.2%), coal (10.8%), combustible re-newables and wastes (1.7%), nuclear (1.3%) and non-combustible rere-newables (0.1%) as is shown in Fig. 1.2; 1.8% of the TPES comes from imported electricity. The most important domestic energy source is natural gas, accounting for 91% of domestic energy production. The total final consumption of energy (TFC) was 60 Mtoe in 2002, an increase of 17% compared to the 1990 level. In 2002, oil accounted for 42% of the TFC, natural gas for 38.1%, electricity for 14.3%, heat for 4.1%, coal for 1.1% and renewables and wastes for 0.4%. Between 1990 and 2002, the shares of gas and coal in the TFC decreased, while the share of oil, electricity and heat increased.

The goals of the Dutch government as stated in the ”Derde Energienota” [178] comprise continuous energy savings, energy efficiency improvements and a fur-ther development of renewable energy from 1% in 1995 to 10% in 2020. This tar-get is almost a fivefold of the 53 PJ in 2003, i.e., 270 PJ in 2020. From this tartar-get, 40% (120 PJ) is planned to be realized using energy from waste and biomass [147]. As can be seen from Table 1.2 a large part of this biomass is scheduled to be co-fired with coal in existing coal co-fired power stations. It is thus clear that the con-version of these solid fuels will play a major role in the realization of the target. Currently a total 4000 MWe of coal/co-fired units are installed in the Nether-lands. This is about 24% of the total installed capacity of 17000 MWe. As stated

1.3 Energy Conversion by Combustion of Solid Fuels Mto e 0 10 20 30 40 50 60 70 80 90 Gas Coal Other* Nuclear Oil 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Total Primary Energy Supply, 1973 to 2020

* includes solar, wind, combustible renewables and wastes and electricity and heat trade.

Figure 1.2: Total primary energy supply by fuel in The Netherlands. [128]

above, it is very important that coal/co-firing keeps its share in the Dutch elec-tricity supply. Technical developments considering emissions reduction is such that coal/co-firing is a sound technology also from a local emissions point of view. However, the relatively high CO₂ emission remains a point of attention. Currently, policies still aim at technological improvements to further reduce the emissions and increase efficiencies. In the most common technology, pulver-ized fuel combustion, impressive progress has already been made the past few years concerning the reduction of SO₂and NOxemissions [178]. Furthermore,

increased efficiencies go hand in hand with a positive evolution on the domain of CO₂emission. Moreover, the co-firing of biomass can also add to further re-strict the discharge of CO₂. Further investigation to improve efficiencies and reduce emissions of coal/co-fired power plants therefore is still very relevant.

1.3

Energy Conversion by Combustion of Solid

Fu-els

Currently the most widely adopted technologies to thermochemically con-vert the energy content of solid fuels are pyrolysis, gasification and

combus-Table 1.2: Renewable energy production (PJ) from domestic sources in The Nether-lands [53] 1990 1995 2000 2005 Source Hydropower 0.8 0.8 1.2 0.7 Wind energy 0.5 2.8 6.9 17.3 Photovoltaic 0.0 0.0 0.1 0.3

Thermal solar energy 0.1 0.2 0.4 0.7

Heat pumps - 0.1 0.4 1.2 Heat/cold storage 0.0 0.0 0.3 0.9 Waste incineration 6.1 6.1 11.4 11.9 Co-firing biomass - 0.0 1.9 29.4 Other 11.2 13.1 15.4 16.7 Energy form Electricity production 6.4 10.8 22.2 59.5 Electricity saving 0.0 0.0 0.3 0.7 Heat production 10.8 10.5 13.5 17.5 Gas production 1.4 1.9 1.9 1.6

Total renewable energy 18.6 23.1 37.9 79.3

Total energy use in The Netherlands 2702 2964 3065 3314

Share of renewable energy (%) 0.7 0.8 1.2 2.4

tion. Pyrolysis refers to the devolatilization (thermal decomposition) of a solid fuel into gaseous compounds and solid char in an inert atmosphere. It is also always the first step of gasification and combustion processes and is driven purely by the heat supplied to a fuel particle from the surroundings. Gasifica-tion is defined as the thermal degradaGasifica-tion of a solid fuel in the presence of an externally supplied oxidizing agent [259]. Oxygen, steam and CO₂can act as ox-idizing agents. Combustion is characterized by the complete oxidation of fuel molecules. The flue gases of an ideal combustion process contain only fully oxi-dized molecules such as CO₂and H₂O. In practice, combustion is seldom com-plete and small amounts of partially oxidized products such as CO and hydro-carbons are found in the flue gases and unburned carbon is present in the ash. Combustion is the prevailing technique for the thermal utilization of gaseous, liquid and solid fuels.

Since the focus in this work is on investigating steam power plants combust-ing solid fuels, this is the mechanism discussed in more detail here. Three main combustion concepts are currently employed in power plants [115]:

Grate Combustion During grate combustion the solid fuel is incinerated on a grate. There are fixed, moving, travelling, rotating and vibrating grates [259].

1.4 The Efficiency of PF Fired Steam Power Plants

Fuels with high moisture and ash content and a varying particle size, such as municipal solid wastes, wood residues and some industrial wastes, are primarily combusted on a grate. Grate combustion requires only minor fuel handling. On the other hand, the flue gas cleaning systems are exten-sive and capital intenexten-sive, particularly for municipal solid waste inciner-ators. Another drawback is the relatively low energy conversion efficiency of around 20-22% of the existing units, which is mainly due to the smaller scale of these facilities.

Fluidized Bed Combustion Fluidized beds suspend solid fuels on upward-blowing jets of air during the combustion process. The result is a turbulent mixing of gas and solids. The tumbling action, much like a bubbling fluid, provides for more effective chemical reactions and heat transfer. The bed consists of inert material (usually sand). Depending on the air fluidization velocity and the particle size of the bed material, one can distinguish between a bubbling (BFB) and a circulating fluidized bed (CFB) type. In a BFB furnace, a mixture of bed material and fuel is located and fixed at the bottom of the furnace. On the other hand, the bed of a CFB furnace is continuously circulating and the bed material is separated from the flue gas using cyclones before it returns to the bed. Fluidized bed combustion is a flexible technology suitable for many fuel types.

Pulverized Fuel (PF) Combustion PF combustion, which is known as dust or entrained combustion, uses a finely ground fuel which is pneumatically injected into a furnace. Pulverized combustion of coal is the technique that has enabled large-scale electricity production for industries and util-ities in the world. Large unit sizes up to 1000 MW_elare nowadays oper-ational. Fuel requirements for PF combustion are more severe than those for grate or fluidized bed combustion. When firing coal, it has to be ground to fine dust with 90-98% of particles smaller than 100µm [84]. In order to ensure stable combustion, the volatile yield and the heating value of the fuel must be sufficient. PF-fired power stations currently have an energy conversion efficiency of about 40%. However, 55% should be reachable when applying supercritical steam conditions. Pulverized coal (PC) com-bustion is the most common technology in the world to generate power, and as said before, it therefore substantially contributes to the CO₂ emis-sions. Three options for reducing these emissions are currently available for PC-fired utilities: improving the efficiency, capturing and storing the released CO₂, or co-firing renewable fuels [168].

1.4

The Efficiency of PF Fired Steam Power Plants

The energy released during the combustion of pulverized fuel contains en-ergy that can be converted in work up to a certain extent (’exen-ergy’). The

remain-ing part cannot physically be converted into work, and is known as ’anergy’. The heat of combustion is transferred with over 90% efficiency to water in the boiler of a power station to produce steam. The higher the pressure and temperature of steam, the higher its exergy, i.e. the part of the energy in the steam that is able to perform work. The total efficiency of conversion is governed by the laws of physics, and depends on the ratios of steam pressure and temperature at the input and output of the turbine. Low temperatures at the output, through cool-ing in the condenser, lead to higher efficiencies. This effect is further enlarged through direct cooling, for example using sea-water.

1.4.1

Trends in Steam Power Efficiency

Generation of electricity using coal started at the end of the 19th century. The first power stations had an efficiency of around 1%, and needed 12.3 kg of coal for the generation of 1 kWh. Early development of power generation tech-nologies naturally focused on the improvement of their economic feasibility. Improved efficiencies have reduced the amount of coal used, and the cost per kWh. Equally important, it has diminished emissions per kWh of CO₂, SOxand

NOx.

Through increasing experience, in combination with research and develop-ment, efficiency started to rise quickly. A multitude of new insights and techni-cal developments allowed improvement of almost any component of the power plant. Examples are continuous improvement of combustion technology and the endurance characteristics of materials.

Only in this way it has been possible to achieve an increase in steam param-eters, and hence a higher exergy content of the steam. In addition, reheating of the steam also has been a significant milestone in the steady increase of effi-ciency. Furthermore, size of power plants increased, with corresponding larger combustion chambers, and longer combustion paths, allowing a more effec-tive combustion. Water preheating, the use of waste heat and steam from the turbine, and a further reduction of heat losses (for example through the walls of the boiler), further significantly increased the efficiency. The most effective measure through the years, though, remained a steady increase of steam param-eters. In the early 1900s, 13 bar and 275◦C had already been achieved, enabling 5% efficiency. In the early twenties, steam parameters of 36 bar and 450◦C (20% efficiency) became possible. Through further developments, new power plants in the fifties operated at 150-180 bar and 450◦C, reaching 30% efficiency. Spe-cific use of coal at that time amounted to 728 gr per kWh. However, the aver-age efficiency of all power stations was still a moderate 17%. In the following decades, the efficiency of using coal further increased. The increase was slowed down by the need to use cooling towers and the addition of energy consuming equipment on-site for the desulphurization and reduction of nitrogen oxide in the exhaust gasses (having a 2-4% impact on efficiency). During the eighties, values as high as 43% were nevertheless achieved (260 bar, 540◦C). The average

1.4 The Efficiency of PF Fired Steam Power Plants

efficiency of all power stations had further increased to about 38%. The specific coal usage had been reduced by a factor 38 over a period of 80 years. Since then, the average efficiency has further increased slightly, through closure of older power stations. A coal-fired power station constructed with the newest technol-ogy in Denmark, near the coast, which started operation in the second half of the nineties, achieved the world’s best efficiency of 47% through direct cooling with seawater in 1998.

The current average efficiency of coal-fired power stations in the world is a moderate 31%. Many regions in the world have therefore significant potential to increase efficiency, and reduce emissions. Currently, further improvements focus on the improvement of steam parameters to 700◦C and 350 bar, result-ing in a 55% efficiency for a combined cycle plant. Pressurized pulverized coal combustion (PPCC) combined cycle power plants are even expected to reach an efficiency of 60%.

1.4.2

Operational Problems

Investigations concerning operational problems of pf fired power plants mainly concern ignition, burnout, and/or deposition, processes. These processes can involve risks of increased plant outages, possible interference with the operation of the burners, the furnace, the boiler convective section, and the environmental control equipment. Ash deposition, also known as slag-ging and/or fouling (see Fig. 1.3), has been estimated to be one of the most im-portant sources of losses of availability and energy efficiency in thermal power stations (especially biomass fired) [68; 69; 137; 244; 254] and is therefore high-lighted more thoroughly here.

Unexpected fouling of heat transfer surfaces has always been one of the main operational concerns in coal-fired utility boilers. The problem stems from a lack of knowledge of the evolution of coal mineral constituents, and the ensu-ing design practices in furnace sizensu-ing, backpass arrangement, tube pitches, etc., which proved to be inadequate in many instances [254]. It even worsened when the necessity of switching or blending coals arose due to economical and/or environmental restrictions. In recent times, co-firing of coal with biomass has brought along new ingredients in the fuel ash chemistry, some of them specially deleterious with respect to deposition phenomena [215], as well as more feed-stock variability [32].

Eliminating or alleviating these difficulties has therefore drawn a great deal of attention. Aside from the huge variety of concepts and techniques in boiler design or redesign, characterization of coal minerals and modelling of ash de-position, an intense field of research has been the continuous monitoring and control of the problem by means of special measuring instruments and on-line calculations.

Figure 1.3: Slagging and fouling phenomena in a pf fired boiler. [251]

1.4.3

Improving Plant Design, Operation, and Control

Currently, plant operators have to monitor hundreds of measurements and possible alarms to detect any fault situation; the operator of the plant must be able interpret each measurement that is received by the control system, and determine which is the condition of the equipment in order to make a proper control action. The complexity of the decisions that the operator is required to make is continually increasing due to stricter economic and environmental reg-ulations, together with the severity of the consequences of an error in judge-ment. In addition, the ability to respond quickly can often be the decisive factor in the prevention of the developing malfunction. Advanced monitoring tools could greatly alleviate the decision making process of operators.

The requirements for a continuous monitoring and control tool for ash de-position problems are very demanding. In the first place, only a thermal param-eter can be measured, be it heat flux, heat absorption, gas temperature or what-ever, having used standard plant instrumentation or dedicated probes. There-fore, some modelling is needed to translate it into some kind fouling index, a relative measure of the insulating effect of the deposits. This calculation is ac-complished by means of heat transfer models, by analyzing the historic record of measurements or by a combination of both techniques. For instance, given the steam and gas flows and temperatures through a superheater, the overall heat transfer coefficient can be calculated. Subsequently, a continuous record of these coefficients can serve to deduce the maximum attainable levels at each operating scenario, or heat transfer formulae are applied to calculate an abso-lute fouling resistance. Meeting this goal with reasonable repeatability is

some-1.4 The Efficiency of PF Fired Steam Power Plants

what difficult, due to the uncertainty in plant data and the usually great vari-ability induced by the feedstock.

The modelling problem exceeds by large the current abilities. An accurate physical model of ash evolution requires advanced characterization of the coal minerals, which is seldom available, due to its cost and the possibility of a sig-nificant change in the coal characteristics. In any case, some of the models de-scribing the physical phenomena are still far from a satisfactory development, such as those for sticking efficiency and bounding stress [68; 244].

Furthermore, even if a reasonable model is developed and empirically ad-justed, it is per se only useful to predict the behavior of a model surface. Com-puting the detail of deposits’ evolution over the geometry of a full-scale boiler demands a detailed knowledge of gas velocity and temperature fields. In other words, ash evolution models are to be coupled within computational fluid dy-namics codes. Besides the usefulness of these models to monitor and predict the deposits’ evolution throughout the boiler, the results are also very well suited to be used for optimization studies of the design of the boiler; an optimization of the design is expected to reduce these kinds of operational problems signifi-cantly.

Finally, when considering the future of pf fired power plants, in order to exploit the time-dependent sale opportunities on the free electrical market, a much more dynamic operation of the plants is expected, and multi-fuel oper-ation capabilities are required in order to comply with environmental regula-tions and incentives. These tasks are to be carried out using very complex and tightly integrated plants. On one hand, this complexity represents a huge poten-tial for highly flexible and optimized operation; on the other hand, mastering this complexity requires appropriate methodologies and tools, from the early design stages, through detailed engineering design, up to the day-by-day opera-tion. Together with more traditional, steady-state-based design methodologies, dynamic models, dynamic simulation tools, and advanced control system de-sign can play a key role in the dede-sign and successful operation of this kind of power plants.

In general, the availability of plant wide dynamic simulation tools brings a deeper physical understanding of the closely interrelated phenomena occur-ring in the process, with immediate benefits for people involved both in the design and future operation of the plant. Furthermore, the availability of this kind of models enables the demonstration of the potential advantages which can be obtained by the deployment of advanced control systems, long before the actual control system hardware is put in place; this kind of study can have a very low cost, compared to the overall cost of the installed control system. Sig-nificant advantages can then be obtained in terms of faster system response, increased plant-wide reliability during large transients, reduced stress levels in critical components, and improved efficiency during transient operation. Re-cent advances in the field, in particular non-linear model-predictive control and hybrid system control, can prove to be extremely attractive for these purposes.

This kind of analysis, made during the early design stages, helps making better and more informed decision about the actual control hardware and software which eventually is to be installed in the plant [62].

1.5

Motivation and Scope of the Thesis

Power producing companies have faced a new situation following the de-velopment of unregulated electricity markets. Strong competition forces them to take power production costs into account more carefully in order to maintain or increase profits. Additionally, to promote the efforts to reduceCO₂emissions, legislation and taxation, the operators are driven to increase the use of coal and renewable energy sources more efficiently and over a wider range. This results in an increasing interest in the use of solid -biomass- based fuels in pulverized coal-fired power plants. Therefore, fuel flexibility due to the combustion of vary-ing relative shares of coal and biomass poses new challenges for plant operators. The understanding of deposition formation and its behavior and consequences has become a key issue in optimizing plant operation and in securing plant per-formance and availability.

The investigation presented in this thesis has been initiated by the EU-ADMONI [266] project1_{. The primary objective of the project is to develop}

a new advanced steam boiler monitoring methodology that supports co-firing in coal-fired utilities, based on the analysis of fuel, fuel-ash and furnace condi-tions. Using such advanced methodologies leads to a better understanding of the processes taking place within the boiler and can help improving the per-formance and design of the boiler. The schematic presentation of all the ele-ments involved in the development of such a methodology is given in Fig. 1.4. The schematic clearly illustrates the complexity of the problem at hand and also puts forward the requirement of knowledge in different scientific disciplines. Since including all of the elements inside the methodology is far outside the scope of a single dissertation, the overall objective of this work is the develop-ment of advanced monitoring models for solid fuel fired power plants. The el-ements of the complete methodology which are to be included in the develop-ment of these models are highlighted in Fig. 1.4.

The main focus is on describing and understanding the operational prob-lems in solid fuel fired power plants. This is accomplished by the development of three different kinds of models. They are: Process Monitoring Models,

Dy-namic Process Models and 3D Boiler Models, and are shown in Fig 1.5. Process

monitoring models are useful to keep an eye on important process parameters such as efficiency or heat transfer characteristics. Dynamic process models are useful for improving the design (model predictive and hybrid) of the control system and, in future, the design of prompter power plants, which is a new

1_{Funded by the European Commission under the Research Programme of the Research Fund for}

1.5 Motivation and Scope of the Thesis ADVANCED MONITORING METHODOLOGY Process Data Ash Behavior Deposition Buildup Characteristics of the Fuel Blend Combustion Conditions Furnace Design

Heat Transfer Modelling Power Plant Process

Simulation Ash Utilization Corrosion Plant Operation Procedures Process Improvements

Flue Gas Emissions Operating Costs Improvement of Boiler Efficiency Advanced Process Control Measurements and Data Acquisition Calculation Routines Online:

Fuel Blend Selection - Complementary & Alternative Fuels

Figure 1.4: Schematic representation of the elements contributing to the advanced moni-toring methodology. Bold and underlined printed elements are subject in this thesis.

idea driven by the new market and global situation [30; 62; 252], where an in-creasing number of distributed power stations is foreseen due to the volatility of the electrical market. The need of physically based dynamic models is evident, since no historical data is available in the design phase, and thus no black box model is possible. Finally, 3D boiler models, which are usually based on compu-tational fluid dynamics (CFD), can both be applied for monitoring and design purposes; based on results form 3D boiler simulations, the furnace design can be improved to reduce operational problems such as slagging and fouling.

Furthermore, the models described in this work are to be coupled to obtain the overall advanced monitoring model as proposed in the project. The cou-pling consists of exchanging the different boundary conditions (BC’s) of the var-ious models with each other. For example, the predictions of boiler efficiency and deposition formation of the 3D boiler models can be used as inputs for both the process monitoring models and dynamic process models, which in turn provide steam and/or wall temperatures as boundary conditions for the boiler models. Based on an initially guessed wall temperature, the CFD model

is converged to a prescribed level, and new wall temperatures are calculated. Based on these, a heat flux and thermodynamic state of the tubes is calculated using the 0D steam cycle models. These then give rise to new wall temperatures. The CFD model is recalculated, and the new results transferred to the steam cy-cle, and an iterative process is carried out, until the full heat transfer is coupled to the steam cycle. The combustion calculation can be carried out as a post-processing task, with a reduced computing time due to the prior knowledge of the velocity field. However, due to time limitations it is outside the scope of this work to fulfill all of these tasks. They are denoted as a recommendation for fu-ture work. This thesis contains descriptions and examples of the three separate modelling techniques which can be used for improving the operation of steam power plants.

Part I

Process Monitoring

Models

Part II

Dynamic Process

Models

Part III

3 Dimensional Boiler

Modelling

BC’s BC’s

ADVANCED STEAM POWER PLANT MODELS

Figure 1.5: Schematic overview of the advanced steam power plant model and the various side objectives within this thesis.

A short description of the three modelling techniques applied is given below. 1. Improvement of Plant Performance by using Process Monitoring

1.5 Motivation and Scope of the Thesis

This objective is focused on the development of models which can predict or monitor either heat transfer resistances of the heat exchanging equip-ment of the steam cycle (this is an indicator of the amount of deposit for-mation) or the overall steam cycle efficiency of the power plant at hand, using the process data provided by the Data Acquisition System (DAS) of the plant. These kinds of models have been presented in literature al-ready quite extensively [15–17; 68; 69; 73; 109; 117; 118; 184; 185; 243; 244; 254] and have proven to be quite useful in the development of, for exam-ple, soot blowing strategies for plant operators. The novelty of the models presented in this work is the investigation of the possible use of exergy analysis within the existing monitoring models to detect a possible decay of plant performance due to arising operational problems. Such a fault ranking system could refine the actions a plant operator could undertake to achieve maximum plant efficiency. Operators can use the monitoring models to predict or monitor disturbances in plant operation, which can help them recognizing such events in future.

2. Dynamic Process Models to study the Unsteady Characteristics of Steam Power Plants.

The objective of developing dynamic process models in this work is twofold. When co-firing varying biomass and coal blends, the unsteady characteristics of the fuel blend can result in fluctuating heat inputs which the plant’s control system has to comply with to ensure normal operation. Furthermore, it influences the plant’s response to normal load changes drastically. A dynamic process model of the steam cycle can then provide insight in the physics and plant behavior when firing such fuel blends. In this field of research also a vast amount of literature is available. [6; 9; 18; 26; 27; 56; 57; 64; 77; 79; 88; 96; 98; 101; 102; 104; 105; 120; 139; 140; 146; 148; 149; 157; 159; 162; 167; 169; 181–183; 198; 202; 226; 260; 261; 273] The results of these models are also very important for the design of the con-trol system of plants which are co-firing these kinds of mixtures. The other reason to develop dynamic process models, is to investigate if these mod-els can be applied to design power plants with improved performance and response. Changing of, for example, heat exchanger types inside a cycle may prove to result in prompter plant response, which in turn, may result in higher efficiencies. As a ”proof-of-concept”, it has been decided here to start with a relatively simple modelling problem, instead of the complete dynamic model of a large scale steam power plant with multiple pressure levels. However, it is clear that the issue is coming up.

3. Accurate 3D Combustion Models to improve Temperature and Velocity Predictions

The last objective of the work described in this thesis is the development of 3D boiler models which can accurately predict the flow and temperature fields inside the boiler. To be able to accurately predict these phenomena,

accurate combustion models are necessary. These models are for example necessary to compute the detail of deposits’ evolution over the geometry of a full-scale boiler [68; 212; 244]. Various combustion models for solid fuel combustion have been proposed in literature [5; 20–22; 24; 36; 44; 55; 87; 93; 119; 127; 131; 134; 136; 143; 154; 160; 163; 175; 197; 208; 214; 219; 222; 224; 263; 267; 274; 281; 285; 286; 293], but an extensive validation of these models is hardly given. The goal in this work is to validate sev-eral combustion models against experimental data and then use the most accurate model for full scale 3D boiler simulations. These boiler models could then provide as an output more accurate predictions for boiler ef-ficiency and deposit formation (if coupled to ash deposition formation post-processors).

1.6

Thesis Outline

This dissertation is divided into three parts (see Fig. 1.5) and seven chap-ters. The present chapter consists of a general introduction about solid fuel fired steam power plants, their history, and some of the problems arising during op-eration of these plants. For the convenience of the reader, each part of this thesis has its own nomenclature and bibliography .

The first part deals with the use of (online) thermodynamic cycle monitoring methods to study improvements of steam power plant performance and opera-tion. Chapter 2 discusses the background of thermodynamic analysis for steam power cycles. A closer look into existing monitoring software is presented, and different applications of thermodynamic analysis are discussed. Finally, first-and second law analyses first-and an example of their application to a simple reheat-regenerative Rankine Cycle is demonstrated. Chapter 3 first presents the back-ground of the software for thermodynamic analysis that has been used for the development of the monitoring models throughout this work. Hereafter, a boiler model to monitor thermal resistances within the boiler, and a steam cycle effi-ciency monitoring model are presented and results are discussed.

The second part of this work deals with the modelling of the unsteady char-acteristics of small scale steam power cycles. Chapter 4 discusses the background of dynamic modelling applied to energy conversion systems. Fur-thermore, different modelling paradigms and their advantages/disadvantages, theoretical aspects, and available software regarding dynamic modelling are treated. Finally, the modelling approach applied in this work is presented. In chapter 5, hereafter, the presented modelling approach is used in the develop-ment of two dynamic models of small steam power cycles. The results of these models are validated and discussed.

The third part of the dissertation treats the computational fluid dynamics modelling of pulverized fuel (pf ) combustion. Chapter 6 presents a background on solid fuel combustion and the various available models to simulate pf

com-1.6 Thesis Outline

bustion. Chapter 7 addresses the development of an accurate model for the sim-ulation of pf combustion. Hereafter, the model is validated against lab scale ex-periments and applied to a large scale coal fired furnace using two available commercial CFD software packages. Finally, the results are presented and dis-cussed.

At last, seven Appendices are placed at the end of this thesis, presenting ex-amples, model parameters, validations, extra results etc., for which there was not any space in the main body of the dissertation.

Part I

Improving power plant

performance and operation

using online thermodynamic

Nomenclature

Roman

A = (1) System matrix = (2) Area, [m2] C = Composition, [-] D = Diameter, [-] E = Exergy, [W] g = Gravitational constant, 9.81 [ms−2] h = Enthalpy, [Jkg−1] k = Thermal conductivity, [Wm−1K−1] LHV = Lower heating value, [Jkg−1]

˙m = Mass flow rate per unit time, [kgs−1]

Nu = Nusselt number, [-]

p = Pressure, [Pa]

P = Power, [W]

˙Q = Heat input, [W]

R = Heat transfer resistance, [KW−1] s = Entropy, [Jkg−1K−1]

S = Source term, [units vary]

T = Temperature, [K]

T0˙Sgen = Irreversibility, [W]

∆Tlm = Logarithmic mean temperature difference, [K]

U = Heat transfer coefficient, [Wm−2K−1] V = Fluid velocity, [ms−1] ˙ W = Work, [W] x = Mass fraction, [-] z = Difference in potential, [m]

Subscript

II = Second law cv = Control volume D = Design fuel = Fuel FG = Flue gas h = Hydraulic i = In j = Species j k = Species k n = Iteration n net = Net o = Out slag = Slag ST = Steam wall = Wall

Greek

α = Power law coefficient, 0.6-0.8 [-]  = Break-off criterion, [-]

η = Efficiency

θ = Methalpy, [Jkg−1]

ψ = Stream availability, [Jkg−1] ∆ = Difference,x₂− x₁

Chapter

2

Introduction

2.1

Background

Steam power plants are widely used throughout the world for electricity gen-eration. The technology currently employed in these plants causes significant negative environmental impacts, since many of these utilities are over 25 years old and aging [209]. Furthermore, the general energy supply and environmental situation requires an improved utilization of energy sources. To utilize the plant more efficiently, efforts are often expended to improve its efficiency and perfor-mance through either modifications and retrofits to the design of the plant or to develop new, advanced analysis, simulation or monitoring tools to optimize, monitor or simulate the thermodynamic processes inside the system.

The (on-line) analysis methods currently employed in steam power plants are only capable to monitor and analyze limited aspects of plant operation [17]. The interpretation of the results for the overall systems have to be evaluated by the plant operator. Since nowadays the operation of power plants has to be opti-mal considering fuel costs and environmental regulations, the supervision and control systems of large-scale industrial plants are becoming more and more complex [15; 72]. This increasing complexity makes the interpretation of the plant operators more difficult and error-prone since a possible occurrence of a disturbance has to be detected by the evaluation of an increasing number of process variables. Furthermore, more optimal soot blowing strategies for reduc-ing the negative effects of slaggreduc-ing and foulreduc-ing phenomena can also only be obtained by incorporating more complex and physically based models. Propor-tionally to the growing complexity of decision-making for operators, the sever-ity of the consequences of an error in judgement is increasing. In addition, the ability to respond quickly can be the decisive factor in the prevention of a de-veloping malfunction.

To obtain the strictly guaranteed plant performance demanded by plant own-ers, the interpretation of results by operators needs to be eased. This requires thermodynamic calculations of high accuracy. As a result, the expenditure for thermodynamic analysis for design, optimization and monitoring has grown tremendously, since the application of computer-aided methods allows for these kinds of studies which otherwise would be too time consuming. The two avail-able tools for performing thermodynamic analysis of power generating equip-ment currently widely used are energy analysis (also known as first-law analysis) and exergy analysis (also known as second-law analysis). The main distinguish-ing feature between the two is that an energy analysis is based on a comparison with the unrealistic situation of converting all the energy put into a process, whereas a second law analysis compares with the maximum efficiency possible for that specific process. For a more thorough description of the two concepts, the reader is referred to literature on the thermodynamic analysis of power cy-cles [50; 187]. Table 2.1 compares energy and exergy efficiencies for some

exem-Table 2.1: Energy and exergy efficiencies of exemplary thermal processes [71].

Utility Energy Efficiency Exergy Efficiency

Large-scale electricity generation or traction 0.90-0.95 0.30

Industrial steam production 0.85 0.25

Fluidized bed drying 0.15-0.30 0.06-0.10

Heat pump drying 0.88 0.05

Transport (diesel powered) 0.40 0.10

Transport (gasoline powered) 0.25 0.10

Space heating or cooling 0.50-0.80 0.05

Domestic water heating 0.50-0.70 0.05

Lightbulb 0.05 0.05

plary thermal processes. In addition to an energy analysis, an exergy analysis helps to identify components where high inefficiencies occur and more impor-tantly it allows for the comparison of losses that are different in nature: for ex-ample, fluid dynamic losses in a turbine, heat transfer losses in a boiler or com-bustion losses in the furnace. Figure 2.1 It is well-known that exergy analysis can be used to determine location, type and magnitude of losses, and thus, can play an important role in developing strategies and providing guidelines for more effective use of energy in existing power plants [133]. Many studies including both energy and exergy analysis have been published. In most of these stud-ies, the focus is on optimizing the thermodynamic cycle by minimizing the irre-versibilities. Examples can be found in the novel design of co- and tri-generation plants [28; 170; 210], in studying the influence on reheat temperatures and pres-sures on cycle performance [72; 110; 211] or in improving the efficiency of exist-ing steam power plants [209].