of Flexible and Prompt Energy Systems
The CO
2
Capture Plant at the
of Flexible and Prompt Energy Systems
The CO
2
Capture Plant at the
Buggenum IGCC Power Station as a Test Case
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof. ir. K. C. A. M. Luyben, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op maandag 23 juni 2014 om 12.30 uur door
Carsten TRAPP
Diplom-Ingenieur f¨ur Luft- und Raumfahrttechnik
(Universit¨at Stuttgart) geboren te Radebeul, Duitsland
Samenstelling promotiecommissie:
Rector Magnificus Voorzitter
Prof. dr. ir. P. Colonna Technische Universiteit Delft, promotor
Prof. Dr.-Ing. J. Groß Universit¨at Stuttgart, Duitsland
Prof. Dr.-Ing. H. Spliethoff Technische Universit¨at M¨unchen, Duitsland
Prof. ir. J. Grievink Technische Universiteit Delft
Dr. D. Bhattacharyya West Virginia University, Verenigde Staten
Dr. F. Casella Politecnico di Milano, Itali¨e
Dr. K. Damen Vattenfall
Prof. ir. J. P. van Buijtenen Technische Universiteit Delft, reservelid
The work documented in this thesis has been performed within the CO2
Catch-up R&D programme aimed at demonstrating and optimising pre-combustion CO2 capture technology for the energy sector. This programme is executed in a con-sortium of Vattenfall, the Delft University of Technology and the Energy research Centre of the Netherlands. This project has been carried out with subsidy from the Ministry of Economic Affairs, EOS Unieke Kansen Regeling (Projectnumber: UKR05003). This research is also part of CATO-2, the Dutch national programme
on CO2capture, transport and storage.
ISBN 978-94-6259-222-3
Copyright c 2014 by Carsten Trapp
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, includ-ing photocopyinclud-ing, recordinclud-ing or by any information storage and retrieval system, without the prior permission of the author. An electronic version of this thesis is available at http://www.library.tudelft.nl
Published by Carsten Trapp, Delft
1 Introduction 1
1.1 Challenges in the energy sector . . . 2
1.2 CCS technologies . . . 4
1.2.1 CO2capture . . . 4
1.2.2 CO2transport . . . 9
1.2.3 CO2storage . . . 9
1.3 Pre-combustion capture for IGCC plants . . . 9
1.4 Research motivation . . . 11
1.5 Thesis outline . . . 13
Nomenclature . . . 15
References . . . 16
2 Steady-state modelling and validation of a pre-combustion CO2 capture pilot plant 19 2.1 Introduction . . . 20
2.2 Process description . . . 22
2.3 Model development . . . 23
2.3.1 Process models . . . 23
2.3.2 Thermodynamic models of the process fluids . . . 25
2.4 Validation methodology . . . 25
2.4.1 Validation against design data . . . 26
2.4.2 Experiments . . . 26
2.4.3 Data acquisition and analysis . . . 27
2.4.4 Data reconciliation and parameter estimation . . . 28
2.5 Results and discussion . . . 32
2.6 Conclusions . . . 39
Nomenclature . . . 41
References . . . 42
3 Design optimization for flexible operation of a pre-combustion CO2 capture plant embedding experimental knowledge 45 3.1 Introduction . . . 46
3.2 Methodology . . . 47
3.4 Pilot plant experiments . . . 55
3.5 Result analysis and discussion . . . 58
3.5.1 Global design decision . . . 58
3.5.2 Local design decision . . . 64
3.6 Conclusions . . . 68
Nomenclature . . . 70
References . . . 71
4 Efficiency improvement in pre-combustion CO2removal units with a waste-heat recovery ORC power plant 75 4.1 Introduction . . . 76
4.2 CO2 capture process configuration and waste heat recovery possi-bilities . . . 78
4.3 Waste-heat recovery ORC power plants and their configurations . . 80
4.4 Analysis and optimization methodology . . . 81
4.5 Results and discussion . . . 86
4.5.1 The base-case: ORC system composed of standard power modules . . . 86
4.5.2 Optimized subcritical ORC power plant . . . 88
4.5.3 Optimized supercritical ORC power plant . . . 94
4.6 Conclusions . . . 94
Nomenclature . . . 99
References . . . 101
5 Dynamic modelling and validation of a pre-combustion CO2 cap-ture plant for control design 105 5.1 Introduction . . . 106
5.2 Model development . . . 107
5.2.1 Modelling approach . . . 107
5.2.2 Thermophysical properties . . . 108
5.2.3 Development of component models . . . 108
5.3 Modelica-FluidProp interface . . . 110
5.3.1 Library architecture . . . 111
5.3.2 Lessons learned . . . 113
5.3.2.1 Choice of state variables . . . 113
5.3.2.2 Developing index-1 models . . . 115
5.3.2.3 Improvement of computational time . . . 117
5.3.3 Recommendations for a future interface . . . 120
5.4 Dynamic model validation . . . 120
5.4.1 Validation approach, experiments and results . . . 120
5.5 Process analysis . . . 128
6 Dynamic system model of the absorption section of pre-combustion CO2 capture plants 141 6.1 Introduction . . . 142 6.2 Model development . . . 143 6.2.1 Process description . . . 143 6.2.2 Modelling approach . . . 143
6.2.3 Dynamic absorber model . . . 144
6.2.4 Additional process models and control . . . 148
6.3 Dynamic validation . . . 148
6.3.1 Absorber model validation . . . 149
6.3.2 Absorption and solvent regeneration section model validation 158 6.4 Conclusions . . . 160
Nomenclature . . . 163
References . . . 165
7 Conclusions and Perspectives 167 7.1 Conclusions . . . 168 7.2 Perspectives . . . 172 References . . . 174 Summary 175 Samenvatting 179 Acknowledgements 183 Selected publications 187
Walter Ulbricht, Chairman of the State Council of the German Democratic Republic, press conference, East-Berlin, June 15, 1961
1
Introduction
The work documented in this thesis investigates the design of pre-combustion
CO2capture systems for integrated gasification combined cycle (IGCC) power
plants. The motivation of this research stems from the need for design compe-tence and validated design tools enabling future large-scale implementation of this technology in the power sector. First, recent developments in the energy sector are described concerning the need for reduction in emission intensity and the demand for more flexible and prompt operation of fossil-fuelled power plants. Then a general introduction to carbon capture and storage (CCS) technologies is given. The different capture processes and their technical chal-lenges are addressed, with an emphasis on the key chalchal-lenges in the design
of pre-combustion CO2 capture plants. The research motivation is discussed
in detail and scientific questions addressed in this thesis are formulated. The chapter concludes with a thesis outline.
1.1
Challenges in the energy sector
The rapid growth of the world population and the emergence of developing econ-omies might lead to the continuation or even to the increase of the use of fossil fuels in the coming decades in order to meet the growing world energy demand [1]. In particular, coal remains an attractive source of energy because it is still cheap, abundant, and easily accessible. However, coal is also the most carbon-intensive fossil fuel in comparison to other primary energy sources, and thus its consumption contributes to a large extent to environmental pollution and the increasing level of carbon dioxide in the atmosphere. It is expected that constraints on carbon emissions will be more strict in future, in order to mitigate the effects of global warming attributed to the high atmospheric concentration of greenhouse gases.
The generation of power and heat, with its major use of fossil fuels, was
iden-tified as the largest producer of CO2 emissions, being responsible for a share of
more than 40 % of the global CO2 emissions in 2010 [2]. Reduction of the
emis-sion intensity in the energy sector can be achieved by implementation of mitiga-tion measures, such as the improvement of power plant and end-use technology efficiency, the increase of the share of non-emitting sources, i.e., renewables and possibly nuclear fuel, the use of more biofuels and carbon capture and storage.
Not to mention, that the most crucial measure for CO2 abatement is electricity
saving.
The International Energy Agency (IEA) estimated that CCS technologies ap-plied to fossil-fuelled power plants is a potentially beneficial technology, because it
might contribute to the required reduction of CO2emissions for as much as 17 %
by year 2035 [3]. This is based on the so called 450 scenario, whereby the
long-term temperature increase is limited to 2◦C in comparison to the pre-industrial
level. CCS technologies might therefore play an important role in a foreseeable near-future energy scenario, in which a carbon-constrained transition period leads to electricity primarily supplied by renewable energy resources.
An increasing number of countries are adopting climate targets in order to promote the transition to a more sustainable electricity generation, resulting in significant changes in the energy sector. In 2009 the European Union (EU) set
the objective that 20 % of its final energy consumption1is provided by renewable
sources by the year 2020 [4]. Stimulated by this so-called Renewables Directive, wind and solar energy technologies have exhibited the largest growth rates among all the technologies for the conversion of renewable energy sources. In 2013 they accounted for almost 90 % of newly installed power capacity, as far as conversion of renewable energy is concerned. The share of wind power with respect to the total installed power capacity increased from 2.4 % in 2000 to 13 % in 2013, resulting in a gross power capacity of 117.3 GW. The share of installed photovoltaic (PV) power capacity was marginal in 2000, and increased to 9 % in 2013, which corresponds to a power capacity of 80 GW. As a result, wind power plants can cover about 1The final energy consumptions refers to the total energy consumed by the end user, such as private households, agriculture, industry and transport.
8 %of the EU electricity demand, while PV power plants about 3 % [5, 6]. It is expected that the growth of the wind and solar power installations will continue, though the growth rates might be more modest than in the last few years due to cutbacks in governmental subsidies for renewable energy.
The major drawback of renewable energy sources, such as wind and solar, is their naturally fluctuating availability, which can vary considerably, and even at different time-scales: hourly, daily or seasonally. This results in intermittent elec-tricity generation. Due to the rapidly increasing share of the elecelec-tricity converted from the wind or solar energy, balancing energy demand and supply becomes more challenging [7, 8]. One of the prospective technical measures against grid-unbalance is the use of so-called smart grid technology. A smart grid is a network that integrates the actions of generators and consumers by means of bi-directional communication in order to improve the efficiency and reliability of the electricity supply. For example, consumers can optimize their energy use as they receive accurate information on electricity prices, and, as a consequence, the number of power plants for peak demand can be reduced. Another solution in order to match generated and consumed power is the implementation of electricity storage like, for example, batteries, pumped-storage plants, or production of fuels.
Currently, energy storage capacity is insufficient in Europa, and smart grids are at an early stage of development. However, the increasingly intermittent char-acteristic of electricity generation needs to be matched to the inherently variable demand for energy. Thus conventional power plants must be made capable of sustaining a much higher level of flexible operation. It is also worth noting that wind and solar power plants are prioritized in the power generation scheduling of all European countries, and therefore the number of hours in which conventional power plants are operated at full load is decreasing rapidly, while their part-load operation is increasing. The reduction in the utilization rate of conventional power plants is currently also amplified by the excess of installed capacity, due to the lower energy consumption at times of economic crisis. In order for fossil-fuelled power plants to remain competitive in this rapidly transforming electricity mar-ket, improvements regarding plant flexibility are essential. These are in general the reduction of the minimum load limit and the enhancement of the dynamic performance of power plants, in particular the so-called ramp rate [9].
The rapid growth of renewable energies but also the liberalization of the Eu-ropean electricity market have thus significantly changed the power sector. Until recently, electricity supply relied on large base-load power plants, such as nuclear or fossil-fuel plants, and on dynamically operated gas turbines in order to cover peak demand. Nowadays however, the evolution of the electricity market demands for more prompt and flexible operation also of fossil-fuel power plants. As it is expected that constraints on carbon emissions will require fossil-fuel power plants
to be equipped with CO2 capture units, consequently these gas-processing plants
have to be able to follow the very dynamic operation of power plants, and at the
1.2
CCS technologies
CCS entails the capture of CO2 emissions from large, stationary point sources,
such as fossil-fuelled power plants, but also from industrial sites, like refineries, cement production and steel making plants, and the transport of the concentrated
and liquefied CO2to its permanent storage location in deep geological formations,
such as depleted oil and gas fields, saline aquifers or unmineable coal seams. Fig-ure 1.1 depicts the entire CCS process chain for fossil-fuelled power plants. In the following, the different technologies to perform carbon capture, transport and storage are introduced. In addition, the technical challenges are discussed, with primary focus on the power sector due to its role as the major contributor to the global emissions of CO2.
1.2.1
CO
2capture
Currently, three general processes for the removal of CO2 applied to fossil-fuelled
power plants are investigated [11]:
• Removal of CO2from synthetic gas prior its combustion — pre-combustion
capture,
• Separation of CO2 from flue gas after combustion — post-combustion
cap-ture,
• Fossil fuel combustion in nearly pure oxygen to yield pure CO2as combustion
product — oxyfuel combustion.
Figure 1.2 gives an overview of the different capture systems, which are explained in more detail below. In general, different separation technologies can be adopted for these capture processes, namely absorption, adsorption, membranes, and cryo-genics. The choice of the most suitable technology depends among others on the condition of the gas stream to be treated (such as temperature, pressure, and
concentration of CO2) and the desired purity level of the CO2 product stream
[13]. Some of these technologies are already applied commercially for separation
of CO2in the chemical industry. Examples are: process gas treatment (chemical
absorption), natural gas sweetening (physical absorption or polymer membranes), and hydrogen purification (chemical absorption, adsorption) [14]. However, the
scale, process conditions and requirements that would apply to CO2 removal in
power plants are largely different from CO2capture technologies currently adopted
(higher flow rates, possibly low pressure, presence of different impurities). As a consequence available technologies are primarily not cost-effective [13]. Physical and chemical absorption are presently the most mature and viable technologies
for the removal of CO2 from the syngas or flue gas of fossil-fuelled power plants,
while other technologies require further development before commercial-scale im-plementation can occur.
Figure 1.1: Graphical representation of the entire CCS process chain applied to fossil-fuelled power plants [10].
In general, CO2 capture is the most energy intensive step of the entire CSS process, resulting in a power plant efficiency loss in the range of 6.4 − 11 %-points depending on the employed technology and the type of power plant [15]. Moreover,
CO2separation accounts for about 70 % of the costs related to the implementation
of CCS [16].
Pre-combustion capture
Pre-combustion CO2 capture is suitable for integrated gasification power plants,
i.e., combined cycle power stations whereby the gaseous fuel for the gas turbine is obtained by gasification of fossil fuels or biomass at elevated pressure to yield a synthetic gas (syngas) predominantly containing CO and H2. In case of the
addition of a pre-combustion CO2 capture process plant, steam is added to the
syngas to produce CO2and H2in catalytic shift reactors. Finally, CO2is separated
from the syngas typically by means of physical absorption, which is most effective
at high partial pressure. Thereafter, the CO2 recovered of the loaded solvent is
further compressed for sequestration. The resulting H2-rich gas is fed to the gas turbine of the combined cycle power plant. Figure 1.3 illustrates a simplified IGCC
power plant with a pre-combustion CO2capture unit. A more detailed discussion
of the entire process is given in Section 1.3.
The main processes of pre-combustion capture, such as the water-gas shift and
the CO2 absorption, have already been used in the chemical industry for a long
time, hence these technologies are well proven. However, the application of this technology to IGCC power plants implies a much larger scale, and continuous and dynamic operation, which introduces remarkable differences and challenges if
com-pared to the CO2capture process plants of the chemical industry. Disadvantages
of pre-combustion capture are related to the complexity of the fuel treatment pro-cess and issues related to the burning of hydrogen-rich syngas in the gas turbine [11].
Post-combustion capture
Figure 1.4 depicts a simplified scheme of a power plant with post-combustion cap-ture. Chemical absorption with aqueous amine solutions is the most mature CO2 separation technology for post-combustion capture commercially deployed for
pro-cess gas treatment. CO2is absorbed from the flue gas by the amine solvent, which
is thereafter regenerated at elevated temperature (about 120◦C), and continuously
recycled.
The distinct advantage of the post-combustion capture process is that it can be implemented as retrofit to existing power plants without significant modifications to the power generation system. The disadvantages of this technology are i) the high demand of energy required by the solvent regeneration, and ii) the relatively large size of the equipment due to the low partial pressure of CO2. Challenges are related to operation under varying plant conditions, and to the scale-up of the technology, which is required in order to treat the entire flue gases emitted
Figure 1.3: Simplified scheme of an IGCC power plant with integrated pre-combustion capture unit [17].
from a power plant, so that approximately 90 % capture rate can be achieved. Further improvements of post-combustion capture performance can be obtained by a higher degree of process integration with the power plant [18].
Oxyfuel combustion
Figure 1.5 visualizes a simplified configuration of a power plant based on oxyfuel combustion technology. Oxyfuel combustion is a process whereby the fossil fuel is combusted by means of nearly pure oxygen, yielding exhaust gases primarily
containing CO2and water vapour, which can easily be separated to produce CO2
with high purity. One of the technical difficulties inherent in the oxyfuel combus-tion process is the flue gas cleaning due to the higher concentracombus-tion of impurities,
such as SO2and NOx, in comparison to those in the flue gas of an air-coal
combus-tion plant. This makes this technology less suitable for combuscombus-tion of low quality fuels [19, 20]. The oxygen required for combustion is produced by means of air separation based on cryogenic distillation. This process can be considered as a mature technology, however it is very energy intensive. A part of the flue gases is recycled in order to lower the high flame temperatures related to combustion with pure oxygen. Current research is directed, for example, at the investigation of the oxyfuel combustion processes, boiler design and optimization of air separation in order to reduce the energy consumption [11, 21].
1.2.2
CO
2transport
After removal from the fuel or flue gas, the CO2 is liquefied by compression to
pressures between 110 and 150 bar, and transported to storage sites. Arguably, if CCS will become mainstream, the only way of transporting and distributing large
quantities of CO2 will be by means of pipelines. Possible transport options for
smaller quantities and short distances are by truck, rail or ship. CO2delivery by
various means already occurs since few decades. Examples can be found in the
food industry, and in the oil extraction sector, whereby compressed CO2is utilized
for enhanced oil recovery. The required technology and operational experience is therefore available. However, CCS deployment would require an extremely large
new network, including hubs in order to redistribute CO2 collected from various
power plants to individual storage sites. In this context, more specific health and safety regulations need to be considered.
1.2.3
CO
2storage
Storage involves the injection of pressurized CO2into geological formations deep
underground, where the CO2 is retained in the pore space of sedimentary rocks.
Suitable storage sites are oil and gas reservoirs, unmineable coal seams and saline formations (aquifers). Aquifers are estimated to provided the largest storage
vol-umes worldwide. The storage of CO2 in oil and gas reservoirs is utilized since
various decades for enhanced oil recovery. Current activities concern the
explo-ration of suitable storage sites, the demonstexplo-ration of CO2 storage in aquifers in
order to determine actual available capacity, and the long-term monitoring of
in-jected CO2in order to ensure that the gas cannot escape from the reservoir.
To summarize, most of the individual component technologies for capture, transport and storage are already proven in industry, sometimes in different con-figuration and/or with other purposes. However, the largest challenge for CCS deployment is the integration of these individual technologies and its implementa-tion at large-scale in the power sector [22]. This entails continuous and sustained technology development in order to reduce the power plant efficiency loss due to
CO2removal. Improvements in overall conversion efficiency can be obtained by
re-ducing the energy consumption of the CO2capture process, by making transport
and storage less energy demanding, and by better integrating all CO2
capture-related processes into the power conversion system.
1.3
Pre-combustion capture for IGCC plants
The integrated gasification combined cycle is a concept for complex energy con-version systems, which combines solid fuel gasification technology with a highly efficient power generation system, the combined cycle power plant. The combined cycle configuration comprises a gas turbine (GT) and a heat recovery steam
gener-Air Separation Unit (ASU) Coal Preparation Air Coal
Gasification Syngas Scrubber/
COS Hydrolysis Sulphur Removal Sulphur Recovery Water-gas shift CO2 Removal CO2 Compression Gas Turbine HRSG Steam Turbine Electricity Generation Raw fuel gas
Steam
Steam
(from gas coolers) Stack Gas
Steam Sulphur (By-product) CO2 to Storage Air N2 from ASU To ASU Hydrogen
Figure 1.6: Process flow diagram of an IGCC power station integrating a CO2 removal
plant [23].
ator (HRSG) powering a steam turbine (ST). Figure 1.6 shows a simplified scheme
of an IGCC power plant with integrated pre-combustion CO2capture unit.
The fuel, in most cases coal, first undergoes a preparation process depending on the employed gasification technology (e.g., milling and drying in case of dry-feed gasifiers), before it is fed together with oxygen, produced by an air separation
unit, to the gasifier. Under conditions of high temperature (1400 − 1600◦C) and
pressure (30 − 40 bar) a synthetic gas is produced predominately containing H2 and CO. Thermal energy is recovered from the syngas leaving the gasifier by using it in order to generate high-pressure steam which is fed to the ST. Thereafter, the syngas is cleaned by means of cyclones, filters and water scrubbing in order to remove the remaining fly ashes and HCl. The carbonyl sulphide (COS) present in the syngas is converted into H2S during the COS hydrolysis. The sulphur is thereafter removed in the H2S removal unit, and sent to the Claus plant whose final product is elementary sulphur.
After all these treatments, the syngas enters the pre-combustion CO2capture
unit. As described in Section 1.2, first the CO present in the syngas is converted
into CO2 and H2 by means of a staged water-gas shift process, which requires
adding steam to the syngas. Thereafter, the CO2 is removed by means of
ab-sorption and compressed for sequestration. The resulting H2-rich syngas is fed to the gas turbine of the combined cycle in order to produce power. The thermal energy of the gas turbine exhaust is recovered in the HRSG, and the obtained high-pressure steam is thereafter expanded in the ST. Many different plant config-urations are possible, depending on the type of gasification technology, the degree
of process integration and the choice of capture technology. The process descrip-tion given here concerns therefore only a general IGCC plant configuradescrip-tion with
CO2capture, but many variants are possible, if more details are considered.
To conclude, integrated gasification combined cycle power plants are a promis-ing technical solution if electricity production must integrate carbon capture,
be-cause CO2can effectively be removed at high partial pressures, and the plant net
energy efficiency is estimated to be higher than that of conventional pulverized coal (PC) steam power plants [15]. The U.S. National Energy Technology Laboratory
(NETL) predicted that IGCC power plants with 90 % CO2capture can reach net
plant efficiencies between 31.2 and 32.6 %, depending on the employed gasification technology. The efficiencies are based on the higher heating value (HHV). Notably,
the estimated efficiency of supercritical PC steam power plants with CO2capture
is 28.4 %. Moreover, gasification allows for i) lower emission levels of regulated pollutants resulting from effective syngas cleaning, ii) greater fuel flexibility (the fuel can be any kind of coal, and biomass, even in combination with coal), and iii) the integrated generation of different products, such as electricity, fuels and chemicals.
However, a number of challenges must be overcome in order bring this technol-ogy to commercial scale and spread its adoption. These are the high energy penalty
associated with CO2 capture, compression, transport and storage, the increase in
system complexity, the process availability, and the high capital investment. Moreover, the integration of the capture unit into the very complex gasification process and combined cycle power plant leads to outstanding technical problems as far as dynamic operation is concerned. As outlined in Section 1.1, transient performance of power plants is becoming extremely relevant, due to recent de-velopments in the electricity market, namely the liberalization (in the European countries), and the increase of the share of electricity obtained from renewable en-ergy sources. As a consequence, the IGCC power plant and the integrated capture unit have to be able to follow frequent and fast load changes in order to balance the intermittent nature of the conversion of wind and/or solar radiation.
Apart from the technological problems, the competitiveness and economic vi-ability of CCS technologies in the power sector will depend on future policies and regulations. Recent activities regarding exploration of storage sites demonstrated that public acceptability plays also a key role.
1.4
Research motivation
The technical obstacles related to large-scale implementation of pre-combustion
CO2 capture plants are addressed in an increasing amount of scientific literature
which documents performance analysis by means of steady-state modelling and simulation [23–28]. These studies compare different technologies and evaluate the impact of several operating parameters on the energy efficiency penalty due to the
process optimization maximizing efficiency or power output by means of model-based design is addressed in only a few studies [29, 30] and demands, in particular
for the CO2capture process, further research. The challenges related to transient
operation and control of CO2capture systems integrated with IGCC power plants
have been treated so far only by few researchers, due to the complexity of the required modelling and simulation work [31–33].
For the majority of the documented models used for process analysis, model validation could not be performed due to lack of experimental or industrial data. Data for validation were lacking in case of both steady-state and dynamic oper-ation. Validation of the models against measurements is essential to improve the accuracy of the simulation results leading to reliable and predictive design tools. Therefore, comprehensive experimental investigations accompanied by modelling activities to develop detailed and accurate steady-state as well as dynamic models for process design were required.
The work presented here was part of a larger research project involving the util-ity company Vattenfall, the Energy research Centre of the Netherlands (ECN) and the Delft University of Technology aimed at the development of pre-combustion
CO2 capture technology to be applied in a future commercial-scale IGCC power
plant. A unique, fully instrumented CO2 capture pilot plant was realized at the
Buggenum IGCC power station in the Netherlands in order to demonstrate the technology and investigate its performance [34].
The general objective of the work documented in this thesis is to generate
knowledge on pre-combustion CO2capture systems for IGCC power plants
utiliz-ing the pilot facility in order to provide design competence and validated design tools for future large-scale implementation of this technology in the power sector. This overall goal translates into original research questions which aim to identify
important design variables of the IGCC pre-combustion CO2capture system, such
as the most relevant process parameters, as well as environmental and operational
limits and/or targets, and their impact on the CO2removal efficiency penalty, and
on the optimal operating conditions.
This investigation is approached by means of model-based process simulation and design optimization targeting reduction in energy consumption, and through design and execution of experiments to evaluate the impact of different parameters on the process performance throughout the operating window of the pilot plant. Furthermore, this research project targeted the investigation of the capabilities of a capture system to follow prompt load variations, and the study of control strategies that enhance the responsiveness of the plant.
The most relevant research objectives are to improve and develop tools and methodologies which i) facilitate detailed steady-state performance analysis and sophisticated optimization of process design and operating conditions, and ii) en-able studies on process dynamics already during the early design phase in order to support the choice of equipment and control strategies aiming at the improvement of transient performance.
pre-combustion CO2capture plant at the Buggenum IGCC power station. However, the aim of this work is to generalize as much as possible aspects of the adopted novel system engineering techniques and tools, which would then be applicable to the design of a larger class of chemical and energy conversion systems. The title of this thesis has therefore been chosen so as to represent this more general objective, whereby the application to the specific case of the pre-combustion capture plant is highlighted by the subtitle.
1.5
Thesis outline
This thesis covers two aspects of the design of prompt and flexible energy systems,
demonstrated by analysis of a pre-combustion CO2 capture plant. The studies
related to steady-state modelling, simulation and optimization are discussed in Chapter 2 to 4, and the investigations targeting dynamic performance are sum-marized in Chapter 5 and 6.
Chapter 2 documents the steady-state modelling and simulation of the
pre-combustion CO2capture pilot plant built at the Buggenum IGCC power station
comprising a water-gas shift and an absorption and solvent regeneration process. Comprehensive model validation is demonstrated for the water-gas shift unit utiliz-ing 20 experimental data sets recorded at the pilot facility by applyutiliz-ing a procedure of simultaneous data reconciliation and parameter estimation including gross error detection based on the contaminated Normal estimator. The chapter concludes with an analysis of the reconciled measurements to evaluate the accuracy of the developed model and to identify biased measurements.
Chapter 3 presents the design optimization of a large-scale pre-combustion
CO2 capture plant following a two-phase approach suited to the use of process
simulator environments. In the first phase, global design decisions at plant level are evaluated, targeting the minimization of the energy consumption due to CO2 capture. These are the extent of CO conversion in the water-gas shift unit and the
percentage of CO2 capture in the removal unit. An optimization of both global
design variables is presented considering i) flexible operation in terms of overall carbon capture target, ii) deactivation of catalyst activity throughout the catalyst life, and iii) different operational limits of the steam/CO ratio in the water-gas shift unit. In the second design optimization phase, local design decisions at unit level are evaluated. Two studies are presented focusing on: 1) the design of the
solvent regeneration and CO2compression section, and 2) the impact of the
sol-vent temperature on energy efficiency penalty and equipment cost of the removal unit.
Chapter 4illustrates the recovery of low-grade thermal energy from the
turbo-generators. Differently from other conventional ORC power system applications, the thermal energy source in this case is a syngas-water mixture, which is cooled
from a temperature of approximately 140◦C, and partly condenses due to the heat
transfer to the ORC primary heat exchanger. The performance of the three cate-gories of systems, depending on working fluid and cycle configuration, i.e., systems based on (i) commercially available units, (ii) tailor-designed subcritical cycle, (iii) tailor-designed supercritical cycle, is analysed in terms of net power output, second law efficiency and component-based exergy efficiencies. In this study, particular attention is focused on the semi-empirical optimization approach, in order to avoid unnecessary computations, and general guidelines are provided.
Chapter 5discusses the development and implementation of dynamic models
of the pre-combustion CO2 capture process into an open source software library
by means of the object-oriented, equation-based Modelica language. Moreover, the content includes the description of the development of an interface prototype in order to enable the computation of fluid properties with accurate thermonamic models available within external property packages. Comprehensive dy-namic model validation is demonstrated at component, sub-system and system level by comparison against experimental measurements obtained from various
open- and closed loop transient tests at the CO2 capture pilot plant. Finally,
a simulation-based control design study is presented, whereby a control strategy involving feed-forward, feed-back and cascade control has been implemented and tested with the aim of improving the dynamic performance of the capture unit.
Chapter 6 provides a detailed treatment of the development of the dynamic
model of the absorption and solvent regeneration unit as part of the pre-combustion
CO2capture system, following the equilibrium-based approach for modelling of the
physical absorption process. The accuracy of the model predictions is evaluated by comparison against measurements obtained during two transient tests monitoring the system response to step changes in syngas and solvent mass flow rate.
Chapter 7 concludes this thesis by summarizing the main results and
Nomenclature
AcronymsCCS = Carbon capture and storage
ECN = Energy research Centre of the Netherlands
EU = European Union
GT = Gas turbine
HHV = Higher heating value
HRSG = Heat recovery steam generator
IEA = International Energy Agency
IGCC = Integrated gasification combined cycle
NETL = U.S. National Energy Technology Laboratory
ORC = Organic Rankine cycle
PC = Pulverized coal
PV = Photovoltaic
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John F. Kennedy, President of the United States, public speech, West-Berlin, Juni 26, 1963
2
Steady-state modelling and validation of a
pre-combustion CO
2
capture pilot plant
Model validation plays an important role during the development of reliable process models in order to demonstrate that the obtained model can predict the process performance with sufficient accuracy with respect to the modelling purpose. Comprehensive model validation requires process measurements from industrial or laboratory facilities. These measurements are subject to random and gross errors which must be eliminated from the validating data in order to successfully perform model validation. This chapter documents the
steady-state modelling and simulation of the pre-combustion CO2capture pilot plant
built at the Buggenum integrated gasification combined cycle (IGCC) power station, which comprises a water-gas shift section and an absorption and sol-vent regeneration section. Model validation is demonstrated for the water-gas shift (WGS) section utilizing 20 experimental data sets which were recorded at the pilot facility. A procedure of simultaneous data reconciliation and pa-rameter estimation including gross error detection was applied using the con-taminated Normal estimator. It can be concluded that the steady-state model of the water-gas shift section predicts the process performance throughout the entire operating range with sufficient accuracy. The model therefore serves as a reliable foundation for the development of commercial-scale models of
pre-combustion CO2 capture plants, which are essential for process analysis and
2.1
Introduction
Steady-state process models of energy conversion and chemical systems are essen-tial tools during the early process design phase up to commissioning and plant operation. They can be used for purposes of performance verification or analy-sis, and for process optimization considering design as well as optimal operating conditions. Quantitative validation of the process models, not only against design data but in particular against experimental measurements, plays an important role during the model development process in order to obtain reliable and accu-rate performance predictions.
The aim of the work documented here is to promote the development of
pre-combustion CO2removal technology for future large-scale IGCC power plants [1]
by means of modelling and simulation of a small-scale CO2 capture plant. This
pilot plant was realized at the Buggenum IGCC power station in the Netherlands by the utility company Vattenfall for technology demonstration and experimental studies. Measurements covering on- and off-design operation were used for the validation of the pilot plant models, using data reconciliation and parameter es-timation as discussed in this chapter. Ultimately, the validated models are used
as basis to develop models of the full-scale CO2 capture system in order to
sup-port process design. In the following, the choice of the validation methodology is motivated by considering some fundamental aspects of the use of experimental measurements.
Experimental data obtained from industrial processes or laboratory analyses are subject to different errors and possibly process variability. Measurements therefore will not satisfy the conservation laws and constraints which are used to mathematically describe the process. The errors can be classified as random errors – randomly distributed with expected average value of zero – caused by the inaccuracy of the measurement device, and gross errors – considered to be non-random – which can occur due to instrument malfunction, miscalibration or poor sampling among others. Gross errors can be further distinguished between outliers and biases, whereby the first describe abnormal measurement values caused, for instance, by malfunction, and the second define measurement values which are systematically higher or lower than the true process value [2].
Data reconciliation is the process of adjusting the measurements by minimiza-tion of the residual between the corrected and recorded process value in order to satisfy material and energy balance constraints. Reconciliation can only be per-formed if redundant measurements are available. The result of the reconciliation procedure is quality process data which can be used for performance analysis or model validation.
In the presence of gross errors, which do not follow the statistical distribution of the sampled data, the procedure of data reconciliation might lead to significantly biased estimates. Any performance analysis based on poorly reconciled data will provide non-reliable results. Gross errors therefore need to be identified during the reconciliation process, and either replaced by corrected measurements or the
measurement must be discarded from the data set.
Various approaches to gross error detection and elimination have been pro-posed, ranging from statistical measurement tests [3] performed in a sequential manner to simultaneous gross error detection and data reconciliation methods. Good reviews on the gradual advancement of data reconciliation procedures are authored by Crowe [4], Romagnoli and Sanchez [5] and Narasimhan and Jordache [6].
Among the statistical tests, the modified iterative measurement test, a method of serial elimination of the measurements that are most likely affected by large errors, performs best in terms of computational speed and efficiency in compar-ison to similar algorithms [7]. This test is based on the assumption of normal distribution of the measurements and makes use of the weighted least squares (WLS) as maximum likelihood estimator. The presence of gross errors violates this assumption, resulting in biased estimates, and therefore an iterative detection and elimination procedure is required, which comes at the cost of computational effort. Instead of using the weighted least squares estimator, Tjoa and Biegler [8] proposed the contaminated Normal as the maximum likelihood estimator, which is a bivariate distribution function accounting both for contributions of random and gross errors. They demonstrated a method of simultaneous gross error detection and data reconciliation. Based on robust statistics, additional estimators which are applicable to data reconciliation problems in presence of gross errors have been proposed, such as Lorenzian, Fair or Hampel estimator [9–12].
The corresponding studies demonstrated that robust estimators show a low sensitivity to gross errors present in measured data, and, as a consequence, robust estimators provide less biased estimates of reconciled process data. At the same time, the iterative sequential procedures for gross error detection and elimination, as required for statistical approaches of gross error detection, are avoided. Among the several classes of robust estimators, M-estimators, which are based on the maximum likelihood principle, are the most important ones for problems of data reconciliation.
Prata et al. [2] provides a comprehensive overview of studies on robust
es-timators applied to simulated as well as industrial data. ¨Ozyurt and Pike [13]
published one of the only comparative studies, in which six different methods de-rived from robust statistics were applied to literature and industrial process cases. It is concluded that robust estimators based on Hampel, Cauchy and Logistic dis-tribution achieve promising performance for simultaneous data reconciliation and gross error detection.
In case process models contain unknown parameters, an additional step of pa-rameter estimation, in which the bias-free reconciled estimates are used to fit the values of the model parameters, has to be applied. The two-step method is the common approach to solve the two non-linear programming problems (NLP) in a sequential manner. First, a simultaneous data reconciliation and gross error detection is performed generating a set of measurements with only random errors by gross error elimination or correction. Second, the measurement set with
ran-dom errors is used for simultaneous data reconciliation and parameter estimation (DRPE). Advanced methods formulate this problem as simultaneous gross error detection, reconciliation of measurements to obey conservation laws and estima-tion of parameters, by solving one single non-linear programming problem (one-step methods) [8, 12]. Chen et al. [14] performed a comparison between these methods concluding that with both approaches accurate parameter estimates and reconciled process values are obtained. The two-step method showed a better performance at the cost of 82 % higher computational time.
Instead of using NLP algorithms for DRPE problems, also non-deterministic methods have been suggested, such as particle swarm optimization [2] or genetic algorithms. For these methods the determination of the derivatives of the prob-lem is not required and the impprob-lementation is rather straightforward. Due to the global search character, non-convex optimization problems can be solved effec-tively, though the computational effort is typically higher due to a larger number of function evaluations.
To conclude, for the analysis of the measurement data and validation of the CO2 capture pilot plant model the joint data reconciliation, gross error detection and parameter estimation procedure using robust estimators is deemed most suitable. This chapter is organized as follows: Section 2.2 gives a brief process
descrip-tion of the CO2capture pilot plant, while Section 2.3 introduces the corresponding
process and fluid thermodynamic models. The validation methodology consisting of experimental tests design and implementation, data acquisition and analysis, data reconciliation and parameter estimation is outlined in Section 2.4. The re-sults of the validation are discussed in Section 2.5. The concluding remarks of Section 2.6 complete this chapter.
2.2
Process description
The process flow diagram of the CO2 capture pilot plant built at the site of
the Buggenum IGCC power station in the Netherlands is depicted in Figure 2.1. This plant is a simplified, smaller version of a foreseen large-scale capture plant, equipped with sensors and analysers allowing for extensive performance measure-ments [1].
The syngas from the gasifier, which contains about 55 − 60 mol % CO and
2 − 6 mol % CO2, enters the water-gas shift section of the CO2 capture plant at
process conditions of 21 bar and 40◦C and is mixed with process water in order
to obtain a pre-set steam/CO ratio. The syngas-water mixture is fully evaporated and superheated by means of electrical heaters. Carbon monoxide present in the syngas is converted into hydrogen and carbon dioxide via a three-stage, sweet, high-temperature water-gas shift process with interstage cooling. Table 2.1 gives an overview of the temperature and pressure conditions of the reactors. The partial bypass around the first reactor (gas quench) allows for lower steam consumption, hence substantial energy saving [15, 16]. The excess process water is recovered
Parameter Reactor 1 Reactor 2 Reactor 3
Inlet temperature [◦C] 340 340 340
Outlet temperature [◦C] 495 470 350a
Outlet pressure [bar] 18.5 18 17.2
aReactor 3 features a lower catalyst activity than expected,
probably caused by a slight over-reduction of the catalyst dur-ing commissiondur-ing or initial operation [17].
Table 2.1: Water-gas shift reactor conditions at reference state operation.
from the shifted syngas through condensation and recycled.
Then the shifted syngas, which contains about 35 − 40 mol % of CO2, enters
the CO2absorption and solvent regeneration section. Carbon dioxide is removed
from the syngas in a packed column by means of physical absorption utilizing the solvent dimethylether of polyethylene glycol (DEPEG) at process condition
of 40 − 45◦Cand 21.5 − 22.5 bar. The resulting H2-rich syngas is fed to the gas
turbine of the combined cycle power plant and the CO2 is recovered by
three-stage depressurization of the loaded solvent (flash pressures: 7.5 bar, 2.9 bar and 1.3 bar). The lean solvent is recycled to the absorber, while the CO2-rich product stream is compressed and, in the case of the pilot plant, mixed with the H2-rich
syngas. Typically, 80 − 85 % of the CO2present in the shifted syngas is removed.
A more detailed process description is given by Damen et al. [1].
The large-scale CO2 capture plant process is very similar to the described
process of the pilot plant, with the main difference being thermal energy recovery, or so-called heat integration within the WGS section: Electrical heaters and coolers are replaced by feed-effluent and feed-steam heat exchangers, whereby the steam is drawn from the heat recovery steam generator of the combined cycle power plant. In the absorption and solvent regeneration section, the gas recovered from the first
flash vessel (also called H2recovery vessel), which primarily contains co-absorbed
hydrogen, is recompressed and recycled to the absorber column. This way the
combustible H2 is not lost with the CO2product.
2.3
Model development
2.3.1
Process models
The model of the pre-combustion CO2 capture process is implemented into a
commercial software tool [18], which is widely used in academia and industry for the modelling of chemical processes. The system model is assembled from models available in its process component library. The main components are the three water-gas shift reactors and the absorber column, which are briefly described in the following.
Syngas 1st Shift Reactor 2nd Shift Reactor 3rd Shift Reactor Re ac tio n W at er Booster Compressor Condensate Pump CO2 Absorber
Flash 1 Flash 2 Flash 3
Solvent Pump CO2 Compressor Product CO2 H2-rich Syngas Process water Ma ke -u p W at er Rectifier Cooler Air-blown Cooler 2 Air-blown Cooler 1 Electric Heater 3 Electric Heater 1 W as te w at er Ga s Q ue nc h Electric Heater 2 Solvent Heater Solvent Cooler Separator Knock-out Drum Feed Splitting Vessel Rectifier Compressor Cooler
Absorption & solvent regeneration section
Water-gas shift section
Figure 2.1: Process flow diagram of the CO2 capture pilot plant built at the site of the
Buggenum IGCC power station.
based on minimization of the Gibbs free energy of the reacting species. The reac-tor model allows specifications for restricted equilibrium in case the system does not reach complete equilibrium [18]. The corresponding parameter, the tempera-ture approach to equilibrium, was set to 0 K. This assumption is addressed again during the discussion on data reconciliation and parameter estimation (see Sub-section 2.4.4). The moderately exothermic reaction is given by
CO(g)+ H2O(g)−→ CO2(g)+ H2(g), ∆Hr= −41.1 kJ/mol. (2.1)
The reactor inlet temperatures are maintained at a constant value, which is
340 ◦C at reference state operation. This is achieved in the system model by
choosing the outlet temperature of electrical heater 3 and air-blown cooler 1 as input (see Figure 2.1) and by providing the required temperature value. The temperature at the inlet of reactor 2 is maintained by a design specification which manipulates the flow rate of the gas quench.
The absorber column is represented by a rigorous, rate-based model for simu-lating all types of multi-stage, vapour-liquid fractionation operations under steady-state operating conditions [18]. Model parameters are related to the packing spec-ifications, which are summarized in Table 2.2 for the random and structured pack-ing tested at the pilot plant. The correlation of Billet and Schultes [19] was selected to compute the mass transfer coefficients and the interfacial area. The solvent flow rate is maintained by a design specification manipulating the flow rate of solvent make-up added to the system.
In the process model the following chemical components are accounted for: Ar, CO, CO2, COS, DEPEG, H2, H2O, H2S and N2. Other trace components such as
Parameter Random packing Structured packing
Packing type Raschig Raschig
Super-Ring 0.6 Super-Pack 250Y
Specific surface area [m2 m−3] 215 250
Void fraction [m3m−3] 0.98 0.98
Table 2.2: Absorber column packing specifications.
2.3.2
Thermodynamic models of the process fluids
The thermophysical properties of the two-phase multi-component syngas-water and syngas-DEPEG mixture are calculated with the perturbed chain - statistical associating fluid theory (PC-SAFT) equation of state (EoS) [20] due to its success in predicting vapour-liquid equilibria of complex fluids and mixtures for a broad range of conditions. Moreover, due to the strong physical background and the small number of pure-component parameters, the PC-SAFT EoS is robust, con-sistent and extrapolative [20] even if calibrated on the basis of a limited amount of experimental thermodynamic property data.
The solvent DEPEG is employed in industry under the commercial names of
SelexolTM or Genosorb 1753TM, whereby the latter one was tested at the pilot
plant. For simplicity the solvent, which is a blend of glymes, is represented as a pseudo pure fluid in the thermodynamic model. The required pure-component pa-rameters were obtained by fitting the EoS to published vapour pressure and liquid density data which is available for the lighter compounds of the blend [21]. The parameters of the heavier pseudo glyme were estimated by extrapolation following a method demonstrated by Nannan et al. In order to determine the binary inter-action parameters, the PC-SAFT EoS was applied to experimental vapour-liquid equilibrium data provided by the solvent vendor, resulting in good agreement for
binary mixtures between DEPEG and gases such as CO, CO2, H2, N2and water,
if for the latter mixture cross-association interactions are considered [21]. The accuracy of the resulting thermodynamic model has proved to be suitable for en-gineering purposes. With regard to the transport properties, the liquid and vapour viscosity are predicted with the default models suggested for use together with the PC-SAFT EoS, see Ref. [18].
2.4
Validation methodology
The study documented here was aimed at the validation of the WGS section model by comparison of simulation results with design data and by evaluation of model estimates obtained during data reconciliation and parameter estimation using ex-perimental data. The reconciled process measurements of the WGS section are further used for validation of a detailed reactor model which is based on kinet-ics. The validation of the absorption and solvent regeneration section was beyond
the scope of this study. The focus of the absorption section validation is on the tuning of the mass transfer parameters in the absorber column using composition measurements at the absorber top.
2.4.1
Validation against design data
First, the developed process model of the WGS section of the pilot plant was compared to plant design data, which are consistent and free of measurement er-rors. The comparison of the design simulation results with the model predictions of the main mass flows, temperatures and compositions shows satisfactory agree-ment with average deviations smaller than 2 %. Larger deviations were observed for some composition estimates, which might be related to the choice of the EoS which is used to compute the thermodynamic properties of the process fluids. The detailed results of the comparison are not presented for the sake of conciseness.
2.4.2
Experiments
For the purpose of quantitative model validation but also individual component performance analysis, various parametric tests have been designed and executed. In order to increase the reliability of the validated models and its parameters, the experimental data used for validation should cover the entire operational range of the process. Therefore, the most sensitive system variables have been varied between their lower and upper operational limit. The resulting four different test runs for the water-gas shift section cover changes in
1. the individual reactor inlet temperatures, 2. the syngas inlet composition,
3. the syngas inlet mass flow rate and
4. the steam to CO ratio (overall as well as reactor specific).
From the performed experiments 20 individual data sets were selected for model validation based on criteria discussed in Subsection 2.4.3, data acquisition and analysis. An overview of the data sets is given in Figure 2.2 in terms of measured values for temperatures, flows and compositions in the water-gas shift section.
The variations in reactor 1 and 2 inlet temperature are depicted in Figure 2.2(a) together with the variations in syngas and reaction water flow rate. The inlet
temperature of reactor 1 was varied from 315◦Cto 355◦C, while that of reactor
2 from 335◦Cto 355 ◦C. For the syngas inlet flow rate the experiments covered
the range from 840 to 1235 kg/h, and for the reaction water flow rate from 990 to 1480 kg/h. Some of the observed changes in the reaction water flow are related to performed variations in the overall steam/CO ratio (measured at the inlet of the WGS section) and applied changes in the reactor 2 steam/CO ratio (measured at the inlet of reactor 2), which are shown in Figure 2.2(c). Among all data sets,
0 2 4 6 8 10 12 14 16 18 20 300 310 320 330 340 350 360 370 380 390 Temperature [°C] Data set 0 2 4 6 8 10 12 14 16 18 200 200 400 600 800 1000 1200 1400 1600 0 200 400 600 800 1000 1200 1400 1600
Mass flow rate [kg/h]
Reactor 1 inlet temp. Reactor 2 inlet temp. Syngas inlet flow Reaction water flow
(a) 0 2 4 6 8 10 12 14 16 18 20 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 CO 2 , H 2 , N 2 [mole fraction] Data set 0.5 0.55 0.6 0.65 0.7 CO [mole fration] CO CO2 H2 N 2 (b) 0 2 4 6 8 10 12 14 16 18 20 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Molar steam/CO ratio [−]
Data set Reactor 1 inlet Reactor 2 inlet
Overall (inlet WGS section)
(c)
Figure 2.2: Overview of the data sets used for the validation of the water-gas shift sec-tion model. a) Variasec-tions in the inlet temperature of reactor 1 and 2, syngas and reaction water flow rate. b) Variations in the syngas inlet composition. c) Variations in the molar steam/CO ratio in front of reactor 1, reactor 2 and overall (inlet WGS section).
the overall molar steam/CO ratio varies from 1.07 to 1.55, whereas the individual molar steam/CO ratio at the inlet of reactor 1 ranges from 3.51 to 4.57 and that of reactor 2 from 1.96 to 3.45. Figure 2.2(b) visualizes the variations in the syngas
inlet composition. The CO mole fraction varies from 0.54 to 0.62, the CO2content
from 0.01 to 0.06 and the H2content from 0.27 to 0.33.
2.4.3
Data acquisition and analysis
The process measurements obtained from the distributed control system include temperature, pressure, flow rate, level and composition measurements as well as control variables such as valve position, heater duty and cooler fan-speed. Mea-surements are recorded when changes in variable values exceed a threshold which was set for most of the variables to 0.1 % of the individual measurement range.
The on-line storage, display and analysis of the recorded experimental data is man-aged by a commercial software [22]. Selected measurement data were transferred to suitable data processing tools for off-line analysis.
First, the periods of steady-state operation were determined from the raw ex-perimental data of the test runs via visual inspection of the main process variables, such as mass flow rates, compositions, temperatures and pressures. Constant val-ues of these variables signify steady operation of the pilot plant. A minimum period length of 3 hours was considered to ensure a sufficient number of recorded data points, which applies especially to the discrete composition measurements (nor-mal gas chromatograph (GC) analysis mode: one measurement every 15 minutes at each location). Outliers in composition measurements were removed from the steady-state period on a heuristic basis as these errors are rather easy to identify. For further data analysis the mean and the relative standard deviation of all variables were determined for each identified steady-state period and compared to other data sets. In case the relative standard deviations of the individual process variables were comparable in terms of their absolute value to the ones observed in other tests, the quality of the data was considered satisfactory for further analysis. Coriolis and vortex flow meters are used for mass flow measurements in the pilot plant. The coriolis meters measure mass flow rates which are directly recorded. The vortex meters measure volumetric flow rates which are converted into mass flow rates using stream dependent density conversion factors and then recorded. The data analysis is based on mass flow rates, and therefore coriolis measurements can be used straightforward, whereas recorded vortex measurements need to be corrected according to the actual density, which changes during operation based on variations in pressure, temperature and composition of the measured stream. The density calculations were performed with the PC-SAFT EoS.
GC measurement analysis indicated that the recorded wet compositions are unreliable due to steam condensation within the sampling and/or analysis system. Using dry gas composition however resulted in closing elementary balances over the reactors within generally 2 %. Dry gas compositions were therefore used throughout data analysis as measures to prevent steam condensations proved not successful in fully eliminating condensation effects.
2.4.4
Data reconciliation and parameter estimation
Problem definition
Commonly, multiple sets of independent data, whereby all measurements change with each data set, are used in order to obtain reliable parameter estimates (for the presented case: multiple data sets from independent test runs). In case the model parameters do not change throughout the measurement sets and assuming all measurements are subject to errors, then the individual data sets are coupled through the parameter estimates. The resulting simultaneous data reconciliation and parameters estimation problem is described as an errors-in-variables measured problem (EVM) and can be formulated in general terms as
