Micro CHP: Summary of Projects on AE-T100energiring.no/Content/Upload/AE-T100 Energiring.pdfanalysis. Sensors of the T100 internal system are marked in blue, those of the Citect system

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Micro CHP:

Summary of Projects on AE-T100

Asia Energiring

5th Fl., No. 56, Motehayyer Alley, Sohrevardi-Shomali St. Teheran, 1559814314 IRAN +98 21-88528739 +98 912-3908700 [email protected] www.energiring.com

Euro Energiring

2nd Fl., Øvre Strandgate 113 Stavanger, 4005 NORWAY +47 45224698 +47 90361081 [email protected] www.energiring.no

http://www.energiring.com/

http://www.energiring.no/

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Table of Contents

I. Table of Contents .................................................................................................................................... 2

II. Summary ................................................................................................................................................... 3

III. Project 1: Microturbine Energy Systems; The OMES Project ........................................................ 4

IV. Project 2: Risavika Gas Center; A Micro CHP Plant ....................................................................... 6

V. Project 3: Bio-CHP-Monitor; An Intelligent Biogas Fueled M-CHP .......................................... 12

VI. Project 4: Advanced Capture Technology; An Application to M-CHP System ........................ 17

VII. List of Selected Publications ................................................................................................................ 20

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Summary

The expert group at Energiring has been involved in a number of gas turbine based research and

development programs covering micro-, industrial-, and heavy duty gas turbines. This report summarizes

experimental and theoretical micro gas turbine projects carried out involving experts from the Energiring.

It is most probable that distributed power generation units will be operated by people without specific gas

turbine knowledge. Therefore a great deal of effort has been dedicated to development of data driven

intelligent monitoring tools to provide the end users with expert systems, looking after their units. Deviation

from expected operational data patterns trigger alarms that can be reported by the plant owner/operator or

directly reported via telecommunication to the service provider to remedy problems and avoid costly

damage to the unit. A combination of validated thermodynamic models and tools as well as data driven

models have been developed and used to study innovative cycles, techno-economic optimization and

maintenance planning.

T100 installation: a redesigned T100 arrangement for advanced studies at Risavika Gas Center, Stavanger, Norway

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Project 1: Microturbine Energy Systems; The

OMES Project

In 2001 the OMES (Optimized Microturbine Energy System) project was started - a European demonstration

project for the demonstration of the turbine technology at small scale CHP. The OMES Project* had partly been

financed through the EU 5th Frame Working Programme. Participants in the project were Gasum (Finland),

Vattenfall/SGC and the microturbine manufacturer Turbec (Sweden), Statoil (Norway), and DONG and Energi

E2 (Denmark). The installations, spread over six countries (Finland, Sweden, Norway, Denmark, Germany and

Ireland), were a mix of industrial, commercial and domestic installa-tions. The installations covered a number of

different applications and fuels:

Traditional small scale CHP (schools, business centers, etc.)

Flexible steam generation

CO2 fertilization in greenhouses

Cooling

Cluster installation of microturbine CHP units

Natural gas, biogas and methanol

Data on energy efficiency, availability, emission, O/M costs etc. were recorded and reported over the

operation period from 2002 to 2004 (more than 100,000 running hours). Table below depicts success criteria

besides the observations during the project:

Success Criteria for the OMES project Remarks

Power efficiency ≥ 30% during full load operation (ref. LCV)

Obtained for the newest versions installed

o Highly depended on the version of the

product (Turbec T100 -now AE-T100-

microtubine was a very new product

when the OMES project started)

o All results at net conditions including

work to raise gas pressure were

accounted for.

o At part load a considerable drop in

efficiency was remarked.

Overall efficiency ≥ 80% (ref. LCV) Not achieved. Observed interval for overall efficiency 60-78%, primarily depending of return temperature of water in the heating system

Availability ≥ 90% Achieved for most installations O/M Costs < 10 Euro/MWhe. Observed results: 13-15 €/MWh

Unit Cost < 800 Euro/kWe

Observed results: 800-860 /kW

o Extra costs for a methanol tank, heating

accumulator, absorption chiller, steam

mode, noise silencer etc.

o Less costs if installers and advisory

engineers become accustomed to this

new technology.

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o A reduction in hardware price from the

turbine manufacturer -here AE- seems

possible when higher volume

production is established. Emission levels < 15 ppm NOx at 15% O2 Achieved at most sites

* Optimized microturbine energy systems, EC ENERGY Con-tract NNE5/20128/1999, 2001.

Market assessment:

Regarding the market potential evaluation, this project covered investigation of CHP based on MGTs in the

power range between 20 and 200 kW for utilizing in the EU countries.

The largest market potential for MGTs is CHP-installations in hotels, schools, hospitals, office buildings,

apartment houses, sports centers, swimming baths, super markets and shopping centers (combined heat, power

and cooling –CHPC– for satisfying both heating and cooling demands), greenhouses (CHP and CO2-

fertilization), industrial laundries, sewage treatment plants, small and medium sized enterprises (SME)’s with a

certain profile of heat demand or some special process integrated industrial applications. Among these, some

special applications (such as CO2-fertilization in greenhouses), areas with no or poor supply of electricity and/or

lack of district heating infrastructure, and where the electricity grid needs reinforcement are very potential

markets. Areas with a long heating season and dense population are also potential markets.

A 3000-hour of full-load operation of MGT per year is considered as a minimum in order to pay back within

reasonable time. However, the present levels in specific cost for installation and cost related to overhaul and

maintenance for this rather new technology have to be reduced and/or the gap between cost of electricity and

gas has to be increased, to make it economically attractive substituting existing energy systems with CHP based

on microturbine units. In addition, reduction in engine price as well as installation cost is necessary.

The market potential in EU was estimated roughly based on above considerations and limitations. The total

technically market potential in EU-15 for CHP based on MGTs in the commercial, industrial and residential

sectors had been estimated to almost 950 thousands units. The average unit size is estimated to 60 kWe

amounting to a total installed capacity of 57 GWe. In the industrial sector (not focusing CHP production), the

main market potential was expected to be integrated solutions like CHPC and direct drive applications. However,

such integrated applications need to be further developed, technically matured and produced in large numbers

before a commercial breakthrough can be expected.

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Project 2: Risavika Gas Center (RGC); A Micro

CHP Plant

The main objective of this activity was to install and utilize two Turbec T100 (AE-T100) engines in Risavika Gas

Center (RGC) in Rogaland, Norway, and to provide RGC with heat and electricity. This plant was also connected

to the electric and the district heating grid so that surplus or deficit in production could be exchanged with the

grids. In addition to covering electricity and heat demand at RGC, this rigs were also used in several research

projects, mainly focusing on intelligent monitoring using simulated biogas fuel backed up with natural gas. One

of the MGTs installed at RGC was modified to allow the integration of other components for test purposes and allow

for more detailed/additional measurements compared to the standard T100 system.

Measuring system:

The measurement systems of the T100 MGT consisted

of the three following systems**:

(1) Standard measurements by the integrated T100

measuring system as being part of the normal

T100 control, monitoring and protection system.

It measures some standard parameters for

control and surveillance of turbine’s operation

and for economic reasons is reduced to the

absolute minimum. This measurement system is

referred to as the “T100 internal system”.

(2) The RGC measurement system for the overall

site supervision. This is called “Citect or

SCADA” system.

(3) In addition, extra measurements that include

additional sensors were installed on the modified

T100 for extraction of more detailed data during

research projects. These measurements consist

of additional pressure and temperature sensors

and are referred to as “T100 external system”.

The T100 external system involves compressor’s

as well as combustor’s inlet and outlet

measurements as depicts in following Figures.

The diagram below shows the locations of the most important measurements providing data for detail thermodynamic

analysis. Sensors of the T100 internal system are marked in blue, those of the Citect system in orange and those of

the Turbec external system are in green.

** Theoretical and experimental investigation of T100 driven m-CHP, Final project report, International Research Institute of Stavanger

(IRIS), Dec 2014.

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The data acquisition system consists of the connections between measurement location, the measuring systems

(conversion of the signals of the sensors into the digital signals) and the computer for further data processing. As

shown in the Figure below, each probe was ending in a “measuring hand”. These hands contain the plugs to connect

the local senor and the data acquisition/conversion unit. They were, therefore, the interface for the thermocouple

plugs and the flex tubes to the pressure scanner.

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Next Figure shows a detailed view of the tubes and wires corresponding to pressure and temperature measurements,

respectively. The connecting wires and tubes were led to the two data acquisition devices, one for temperature and

one for pressure measurement. In case of transient measurements with high resolution it might be necessary to

shorten the distance between sensors and data acquisition unit (DAU). However experience showed that the response

time to rapid changes in pressure, e.g. during compressor surge, could be captured by this setup. The wires and tubes

transferring the measuring signals from the MGT to DAU and computer are channeled as shown in the following

Figure.

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The Agilent DA device is a commercially available device used for temperature measurements. It contains two

modules with 20 connectors each for temperature sensors. For the current installation only one module was used.

The Agilent data acquisition unit is connected via an RS232 bus to the computer for collecting data. The length of

the connecting cables needs to be kept as short as possible when transient data with a high resolution needs to

measure. The signals from both, pressure and temperature DAU devices, are connected in parallel to the computer

via LPT and COM ports, respectively.

The first version of Turbec T100 external system did not contain compressor inlet sensors. As these values are

essential for any thermodynamic analysis of the compressor and in consequence of the gas turbine, additional probe

with four temperature sensors and 5 pressure sensors was installed. These sensors were connected to the existing

channels at the DAU. All sensors, cables and channels on DA device as well as in LabVIEW software were tagged.

The tag names and positions of the new sensors were identified experimentally and documented (Figures below).

As stated at above sections, several pressure and temperature sensors for research purpose had been installed on

T100. Data collected via these sensors were referred to as the external measurement system. In order to find out if all

sensors are working properly a preliminary test was carried out at RGC. Before running the test connectivity between

all cables and sensors were controlled. The engine power was set to 50 kW and increased with step of 5 kW to 80 kW

during the test.

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Lastly, these three measurements systems were collecting operational data from T100 and interfacing systems and the

data is transmitted to the IRIS data server. Since the systems are collecting data with different frequencies it is

necessary to synchronize the data in same frequency and also collect those in one log file for further processing.

Data processing:

As stated before, data was collected via three different systems in the T100 at RGC. This leaded to different sampling

intervals of the data and the fact that the internal timer of different systems are not synchronized. As the long term

tests were conducted, the need for an automatic data collection procedure was necessary. Work on the data system

was performed as follows:

Replacement of the data acquisition system for the T100 external system via a LabVIEW based version.

Automatic collection of data from different systems at RGC and unified storage of data for the ease of further

processing.

Averaging of the measured data because of fluctuating over the time and transient operation of the turbine.

Synchronization of the collected data and alignment of different sampling rates (T100 internal system: once per 1.5 minutes; T100 external system: once in less than a second; building management system; once per second).

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Plant behavior evaluation:

In order to define necessary measurements/sensors as well as to define a test program, the operating behavior of the

T100 in connection to the local and district heating system was studied. The following basic finding/analysis resulted

from test runs at various loads and the evaluation of the detailed hardware installation: a) Influence and location of

hot water temperature measurement; b) Temperature demand settings of the two systems, as the settings are of

significant importance for the control of the system; c) Because temperature settings in the hot water system in the

T100 and site system the pumps were operating at 100% speed, any change in heat input needed to be compensated

for by reduced hot water inlet temperature. Figures below show operational behavior of the T100 in connection with

the site control system at various loads.

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Project 3: Bio-CHP-Monitor; An Intelligent

Biogas Fueled Micro CHP

The Bio-CHP-Monitor project had been financially supported by Research Council of Norway (RCN) in 2009. The

main objective of this project was to develop knowledge and competence enabling efficient and economic utilization

of biomass resources for energy purposes. The focus was specifically on optimization of various processes from

biogas to usable energy products. This has done by using intelligent monitoring applied to small scale, distributed

combined heat and power (CHP) plants. Use of biogas in three different energy conversion technologies including

internal combustion engines, micro gas turbines and fuel cells have been studied experimentally and theoretically

using the facilities installed and equipped in our previous project at Risavika Gas Centre (RGC). The T100 was used

for MGT driven CHP technology which would be focused here. During this project both use of natural gas and

biogas were tested, measured data were collected and associated models from monitoring and diagnostics point of

view were developed. In addition, stability investigations have been performed to evaluate the bandwidth in variation

of the lower heating value of the fuel. One of the MGT installed at RGC was modified to allow the integration of

other components for test purposes and allow for more detailed/additional measurements compared to the standard

T100 system. Operational data were collected using 3 different systems as explained in earlier project description

(Project 2).

In addition to experimental activities, mathematical modeling were performed for biogas-based systems using

existing/developed thermodynamic models for NG-based technologies. Moreover, these models have been validated

against test results. In addition, computational fluid dynamic (CFD) simulations have been also performed to improve

the accuracy of the results. Apart from theoretical studies, data driven modeling for performance monitoring of each

technology have been carried out using the artificial neural networks (ANNs). Operational data from the available

test rigs as well as additional data from the validated models developed in the heat and mass balance programs have

been used for training purpose of the developed ANN models. Relevant methods and tools for remote monitoring

and control as well as condition based maintenance of this specific energy conversion technology was also studied.

Biogas utilization:

As the characteristics of biogas differ from those of natural gas, it is not possible to directly burn biogas in the

combustion chamber originally designated for natural gas. The heating value of biogas is much lower than that of

natural gas; hence, a higher fuel flow rate is required to maintain the same heat input. Therefore, MGTs need to be

modified before burning pure biogas.

Alternatively, by mixing biogas with natural gas, the characteristics of the fuel mixture, such as low heating value

(LHV) and flame temperature increase, reaching values close to those of natural gas. Thus, stable operation of the

combustor might be maintained without any engine modifications. As a result, the environmental advantage of

burning an amount of renewable fuel to reduce greenhouse gas emissions can be achieved, still using existing MGTs.

This approach also allows the use of natural gas as a ‘‘fallback’’ solution in the case of e.g. shortage of biogas and or

the eventual variation of the biogas composition due to changes in digestion process parameters, resulting in improved

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availability of the MGT. In addition, the use of a mixture of natural gas and biogas is economically viable for small-

scale plants, since the costly upgrade of biogas to the natural gas quality and engine modifications can be avoided,

while at the same time, the natural gas consumption is reduced. In this way, different tests and studies were performed

trying to map an operational window by increasing the share of biogas from zero to the maximum possible level at

various load levels, suggesting proper fuel mixtures for satisfactory performance of the engine.

During experimental tests, natural gas and exhaust gas samples were taken for gas composition analysis. The mixing

station was outside the T100 to simulate different fuel compositions, ranging from only NG to a mixture of natural

gas and biogas by mixing natural gas and CO2:

T100 MGT modeling and simulation; data-driven modeling vs. mathematical modeling:

For the micro gas turbine technology both mathematical and data-driven methods have been used as both methods

are able to produce accurate models. Mathematical models are derived from principles of physics and

thermodynamics. However, they are usually complex and their accuracy relies strictly on the availability of

components’ characteristic maps. The numerical solution of such models might be rather demanding regarding

computational time and when implemented in such a way they are not suitable for real-time monitoring applications.

However, they are still very valuable when an in-depth thermodynamic analysis is the main concern. On the other

hand, the data-driven modeling approach is able to provide a reliable model based solely on the measured data.

Though various data-driven modeling methods are available, selection of artificial neural networks as main approach

for this project was based on our previous experience and knowledge. Once ANN are trained, they are simple and

consist only of straight-forward equations which are programmable in any computer language for automatic

operation. The ANNs do not require an iterative solution to predict outputs, they are fast in response, can be used

for online applications, and also shown to be a suitable method for the modeling of nonlinear and multidimensional

energy systems when an accurate prediction of performance.

To do that, specific knowledge of the system is necessary for the selection of model parameters. In addition to this

knowledge, the modified T100 MGT system with additional instrumentation allowed use of parameters that are not

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measured in the standard version of the micro gas turbine. The additional data provided the possibility to investigate

the impact of extra parameters on the model performance by implementing systematic sensitivity analyses. Based on

these analyses, the significance of various parameters on the prediction accuracy of the ANN model could be

established, resulting in an optimized sensor setup selection for ANN modeling of the MGT. table below shows input

and output parameters of the ANN model for the T100.

Input parameters Output parameters

Range Min. value Max value

𝒕𝒂𝒎𝒃 (°C) -1.23 10.01 𝑃𝑜𝑤𝑒𝑟 (kW)

𝒑𝒂𝒎𝒃 (kPa) 98.64 103.51 𝑡6𝑜 (°C)

𝑹𝑯 (%) 13.9 95.1 𝑡11 (°C)

𝑷𝒔𝒆𝒕 (kW) 50 70 𝑡5𝑜 (°C)

𝒕𝟓𝒊 (°C) 0.55 8.73 𝑝5𝑜 (bar)

𝒑𝟓𝒊 (bar) 0.0 0.02 𝑡𝑜𝑖𝑙 (°C)

𝒅𝒑𝒇𝒊𝒏𝒆 (Pa) 82 170

𝒕𝟑𝒊 (°C) 549.8 595.9

𝒑𝟑𝒊 (bar) 2.22 2.76

For condition monitoring applications, the model should be able to predict the normal or healthy operational

condition of the system independent of any degradation or deterioration inside the system. Therefore, input

parameters were preferably chosen on the basis that their variations are not influenced by the system and they change

independently. Accordingly, the ambient conditions represented by air temperature, air pressure and air relative

humidity were selected as the input parameters. The MGT operation is controlled by the power set, selected by the

user. Apart from these parameters, other input parameters were chosen to investigate the impact of additional

instrumentation and engine modifications on model accuracy in order to discover the optimal instrumentation for

accurate performance prediction.

Once the input and output parameters were selected, the data were filtered from false sensor signals and outliers. The

method used for filtration in this work was manual observation by plotting all input and output data of the ‘‘healthy

engine’’ versus time to detect eventual outliers. Data filtering can significantly influence the model’s accuracy. Also,

the training data set has to be large enough to cover all operational conditions of the MGT. The operational condition

of the engine changes with the ambient conditions at each power demand.

The commercial software NeuroSolutions was employed for the development of the ANN model in which multi-

layer perceptron neural network with one hidden layer was evolved during the training process using the back-

propagation algorithm. A systematic sensitivity analysis was also carried out in four steps to shed light on the impact

of the initially selected parameters on the prediction accuracy of the model and to sort out the optimum input

parameters for accurate prediction of engine performance. This indeed contributes to illuminating an optimized

sensor setup selection for ANN modeling of the MGT. The settings used for ANN training and input/output

parameters of the model for Biogas-fueled T100 are summarized in Tables below, respectively.

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Parameter Value/remark

Network structure MLP

Number of hidden layers 1

Training algorithm Back-propagation/batch

method

Hidden and output neurons

activation function tanh

Error function Mean square error (MSE)

Division of training data

Training/cross

validation/testing =

0.60/0.15/0.25

Training data 3167

Epochs 20,000

Prevention of over-fitting 500

Number of weights

initialization 3

Hidden neuron range 8-18 with step size of two

Input parameters (range) Output parameters

Power set (50-100 kW) Power output

Compressor inlet temperature (7.12-13.8 °C) Turbine inlet temperature

Compressor inlet pressure (-0.002 to +0.002

bar) Turbine outlet temperature

Biogas content of fuel (0-39%) Compressor outlet temperature

Compressor outlet pressure

Burner inlet temperature

Burner inlet pressure

CO2 emission

As part of this project, a steady state thermodynamic model of the T100 MGT was also developed and validated

against real-life data obtained from the test rig. The modeling was carried out using commercially available software,

IPSEpro, which is a heat and mass balance software tool. More details can be found in our publications [2-8]. Figure

below shows the graphical user interface of the MGT.

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The concluding ANN model was found to be a reliable baseline model, which is able to predict the normal

performance of this micro gas turbine with high accuracy, making the model useful for online monitoring applications

at both system and component level. All the simulations results can be found in [2-8].

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Project 4: Advanced Capture Technology; An

Application to M-CHP System

The expected increase in distributed power generation, and the necessity for a reduction in greenhouse gas emissions

requires an evaluation of carbon capture application at small-scale combined heat and power plants. In this regard,

the T100 MGT has been selected for further investigation as a baseline. The results of previously-mentioned project

(Project 3) including developed models, methods and tools have been further utilized via collaboration with Gas

Future Advanced Capture Technology Systems (Gas-FACTS) project. The Gas-FACTS project is a consortium of

different UK universities, and University of Stavanger from Norway and Carnegie Mellon University from the USA

are international partners. This project aims at evaluating different innovative gas turbine cycles for post-combustion

CO2 capture systems. Knowledge built during implementation of bio-CHP project is an important asset to be further

utilized by the Gas-FACTS project [1].

The UK Carbon Capture and Storage Research Centre’s (UKCCSRC) Pilot-Scale Advanced Carbon-Capture

Technology (PACT) National Core Facilities has two natural gas-fueled micro-turbines, both of which are AE-T100

PH designs. The Series 1 turbine was used for these tests. Each turbine produces 100 kW of electrical power, and

since they contain a combined heat element, they also generate up to 165 kW of thermal power, in the form of hot

water at 70-90°C. The electrical efficiency is around 30%, but the use of heat recovery components (a recuperator

and heat exchanger) increases the overall efficiency to ~77%. The key components of the turbine, including those

for heat recovery and the additional instrumentation (TC – thermocouples; PT – pressure transducers; FR flowrate

meters) are outlined in Figure below.

Firstly, a number of MGT operating parameters were internally monitored. Secondly, as detailed in Figure above, a

significant amount of additional instrumentation has been integrated into the turbine system to ensure full systems

monitoring and more a comprehensive characterization of the MGT cycle. Data-logging for these was achieved with

LabVIEW, also set to a logging frequency of 1 Hz. Thirdly, the emissions analysis assessed the levels of various gas-

phase emissions in the flue gases from the gas turbine; two methods were utilized, both taking samples from the flue

gas duct. A GasMet FTIR DX4000 analyzer and associated conditioning system characterized the majority of the

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species in the flue gas. This was used to determine the levels of primarily CO2, CO and various unburned hydrocarbon

species (CH4, C2H6, C2H4, C3H8, C6H14 and total hydrocarbons).

In this project, a thermodynamic model validated against data obtained from a test rig has been extended to enable

modeling of a CO2 capture unit. In addition, two innovative cycles, an exhaust gas recirculation (EGR) cycle and a

humid air turbine (HAT) cycle, have been investigated using the selected MGT model with a focus on improved

carbon capture efficiency. The thermodynamic performance indicators of all cycles, namely, the baseline MGT cycle,

the EGR cycle, and the HAT cycle, all with capture unit, were presented. The results showed a considerable

improvement in cycle efficiency for the HAT cycle (25.8%), compared to the baseline MGT (23.0%) and EGR

(22.5%) cycles. However, the surge margin is reduced markedly for the HAT cycle. It is shown that the effect of EGR

on the operation of the MGT is marginal. The effects of varying ambient air temperature on the performance of all

cycles as well as the effect of different recirculation percentages on the performance of the EGR cycle had also been

investigated. The results confirmed that the performance in the EGR cycle is less sensitive to the change in ambient

temperature, compared to the other cycles. Thermodynamic performance of the different cycles in terms of electrical

efficiency has been depicted in the chart below.

This study provides a valuable contribution to a deeper understanding of the technical limitations and opportunities

of distributed power generation, especially with the current expected increase in distributed power generation in

Europe. The models developed during this study will be used to simulate various scenarios prior to tests in

experimental rigs, to evaluate risks and provide a better understanding of the system behavior. The outcome of this

study can also illuminate necessary adjustments to large-scale natural gas fired plants when equipped with CCS. The

positive impact of the HAT and EGR cycles on the CO2 capture deployment will encourage the development and

demonstration of the GT-based CO2 capture technology for largescale plants. Such a development will also support

CCS for distributed power generation. However, investigation of the plants complexity with higher cooling

requirements and further detailed economic analyses as well as evaluation of the transient behavior and part-load

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operation of the proposed cycles, are needed for enhanced understanding of the techno-economic potential of these

plants.

More details of the experimental as well as simulation results can be seen in [1].

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List of Selected Publications

1. “Micro gas turbine configurations with carbon capture – Performance assessment using a

validated thermodynamic model”, Applied Thermal Engineering, 73 (2014) 172-184.

2. “Performance analysis of a biogas-fueled micro gas turbine using a validated thermodynamic

model”, Applied Thermal Engineering, 66 (2014) 181-190.

3. “Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning

mixtures of natural gas and biogas”, Journal of Applied Energy, 117 (2014) 30–41.

4. “Thermodynamic analysis of innovative micro gas turbine cycles”, Proceedings of the ASME

TURBO EXPO 2014, Germany.

5. “Intelligent biogas-fueled distributed energy conversion technologies: Overview of a pilot study

in Norway, Proceedings of the ASME 2014 Gas Turbine India Conference, India.

6. “Development of an optimized artificial neural network model for combined heat and power

micro gas turbines”, Journal of Applied Energy, 108 (2013) 137–148.

7. “Experience of using an optimized artificial neural network applied to a micro gas turbine

driven CHP”, Proceedings of the 2012 International Congress on Technical Diagnostics, Poland.

8. “Review of theoretical and experimental studies implemented on CHP Micro turbine using

natural gas and biogas fuels”, Proceedings of the 2011 International conference on applied

energy, Perugia, Italy.

Documents

Micro CHP: Summary of Projects on AE-T100energiring.no/Content/Upload/AE-T100 Energiring.pdfanalysis. Sensors of the T100 internal system are marked in blue, those of the Citect system