Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
T1: Modelling and Simulation - Session 4
Time:
Tuesday, 08/July/2025:
8:30am - 10:30am

Chair: Brahim Benyahia
Co-chair: Alexander Mitsos
Location: Zone 3 - Room E031

KU Leuven Ghent Technology Campus Gebroeders De Smetstraat 1, 9000 Gent

Show help for 'Increase or decrease the abstract text size'
Presentations
8:30am - 8:50am

Wind Turbines Power Coefficient estimation using manufacturer’s information and real data

Carlos Gutiérrez, Daniel Sarabia, Alejandro Merino

Universidad de Burgos, Spain

The dynamic modelling of wind turbines and their simulation (individually or grouped in the form of wind farms) is a very useful tool for studying their behaviour, developing and testing turbine and/or farm control strategies, optimal management strategies of the wind farm setpoints, etc. One of the key elements concerning physical models of wind turbines is the power coefficient Cp(λ, β) which acts as an efficiency in the extraction of power from the wind. In modern pitch-controlled turbines with variable rotor speed, this coefficient depends mainly on the wind speed v, the rotor angular velocity ωr and the pitch angle β of the blades. Unfortunately, this coefficient is often unknown a priori as it does not usually appear in the information provided by manufacturers.

Power coefficient is often modelled as an exponential curve (1), described in [1], where λ = ωr R/v is the tip speed ratio and R is the blade radius.

C ̂p (λ,β)=C1 (C2i -C3 β-C4 βC5-C6) exp⁡((-C7)/λi ) (1)

1/λi =(1/(λ+C8 β))-(C9/(β3+1))

This article describes a methodology for obtaining the power coefficient (1) of different wind turbine commercial models by using the power curve provided by the manufacturer, which shows the theoretical power Pj that the wind turbine is able to produce for each wind speed vj. For this purpose, an optimization problem (2) is solved to minimize the difference between theoretical power and power calculated through power coefficient, where Ck (k=1,…,9) are the coefficients of equation (1) to be estimated and βj is the pitch angle for each wind speed vj, being both, Ck and βj, decision variables.

min┬{ckj } ⁡∑j▒(Pj-P ̂(λjj ))2

s.t.: λj=(ωr,j R)/vj (2)

P ̂(λjj )=1/2 ρπR2 vj3 C ̂pjj )

This methodology has been tested with variable-speed wind turbines and pitch-controlled, showing good results.

In this way, the theoretical behaviour of different commercial models can be characterised and incorporated into detailed simulations. However, this methodology can also be used based on historical series of turbine operation data, which allows characterising the real behaviour of a turbine already installed, opening up other possibilities in the use of simulations, such as being able to determine in real time the producible power, determine whether the turbine is operating properly, model update in digital twins, etc.

For this purpose, data series from the Kelmarsh [2] and Penmanshiel [3] wind farms (both in the UK) have been used. There are stored the most important variables, such as generated power, rotational speed, pitch angle and wind speed, every 10 minutes from 2016 to 2021.

References

[1] Slootweg, J. G., de Haan, S. W. H., Polinder, H., & Kling, W. L. (2002). General Model for Representing Variable-Speed Wind Turbines in Power System Dynamics Simulations. IEEE Power Engineering Review, 22(11), 56–56. https://doi.org/10.1109/MPER.2002.4311816

[2] Kelmarsh wind farm data. https://doi.org/10.5281/zenodo.5841834

[3] Penmanshiel wind farm data. https://doi.org/10.5281/zenodo.5946808



8:50am - 9:10am

Techno-economic analysis of a novel small-scale blue hydrogen and nitrogen production system

Adrian Irhamna, George M. Bollas

University of Connecticut, USA

As global energy systems are geared toward cleaner sources, the demand for clean hydrogen is projected to surge, potentially reaching 125 – 585 million tons annually by 2050 and accounting for over 70% of global hydrogen demand (McKinsey & Company, 2023). While conventional steam methane reforming dominates the current hydrogen production infrastructure, the future lies in blue and green hydrogen technologies. Blue hydrogen, particularly in regions with low natural gas prices, is anticipated to be more cost-competitive than green hydrogen (Ueckerdt et al., 2024). Emerging applications in steel manufacturing, synthetic fuels, and heavy-duty transport are expected to drive demand for cleaner hydrogen production route. Additionally, distributed small-scale hydrogen production could accelerate the transition to a hydrogen economy by enabling on-site generation at locations such as hydrogen refueling stations (Navarro et al., 2015), addressing challenges in storage and transportation.

This paper presents an economic analysis of a blue hydrogen and nitrogen production system, using a novel intensified reformer previously proposed (Irhamna & Bollas, 2024c) with a hydrogen production efficiency of 80% when integrated with a shift reactor (Irhamna & Bollas, 2024b). The system’s capability to produce both high-purity hydrogen and nitrogen opens opportunities for small-scale blue hydrogen and distributed ammonia production (Burrows & Bollas, 2022). We analyze two production scales: 500 kg/day and 5000 kg/day, corresponding to small and large hydrogen fueling stations (Kurtz et al., 2020), respectively. We optimized a system that comprises three identical reforming fixed bed reactors, a heat recovery system, and shift reactors. The system was studied using a dynamic model, simulated and optimized in (Irhamna & Bollas, 2024a). The optimized system was then used to perform Techno-Economic Analysis (TEA), considering factors affecting both capital and operating expenses. TEA revealed that the hydrogen production cost for the 500 kg/day system is approximately 3.05 USD/kgH2, while the 5000 kg/day system is 2.68 USD/kgH2. These results are consistent with but higher than larger-scale blue hydrogen production systems (2.0-2.5 USD/kg) studied in prior work (Argyris et al., 2023; Spallina et al., 2019; Szima et al., 2019). We also conducted a sensitivity analysis exploring the impact of key factors such as oxygen carrier lifetime, oxygen carrier price, and natural gas price on blue hydrogen costs. This research contributes valuable insights into the economic viability of small-scale blue hydrogen production, potentially facilitating the broader adoption of hydrogen technologies in a cleaner energy future.



9:10am - 9:30am

A new computational method for the simulation of catalyst deactivation in fluidized bed reactors

Andrea Pappagallo2, Hugo Petremand2, Oliver Krocher2, Flavio Manenti1, EMANUELE MOIOLI1

1Politecnico di Milano, Italy; 2Paul Scherrer Institute, Switzerland

Modelling catalyst deactivation in fluidized bed reactors is up to now a challenging task. The main difficulties are related to the estimation of the movement of the particles over the reactor, resulting in the division of the particles in several classes with different flow patterns. This leads to the presence of different populations of particles with diverse deactivation profiles. In this work, we elucidate a new methodology developed to address this challenge. The methodology was developed starting from a pilot plant operating the fluidized bed CO2 methanation, from which axial concentration and flow pattern profiles were obtained. Additionally, a model for the solid phase movement was calibrated, determining the different movement patterns of the most represented particle classes. The reference deactivation used in this study is the decomposition of ethylene, which is often present in the feed streams to methanation reactors. On the base of these results, the deactivation profiles for the main classes of particles were calculated. According to the knowledge developed with this model, the system was optimized by modifying the flow pattern, so that the residence time in the deactivation zone could be reduced. The most promising solutions to decrease the catalyst deactivation were validated experimentally in the pilot plant.

A fluidized bed methanation reactor with a throughput of ca. 50 Nm3/h of reactive gases was used to measure concentration and bubble rise profiles. These data were used to validate a fluidized bed reactor model, developed on the base of the two phase assumption. This assumption considers the reactor as composed of a bubble phase (with plug flow characteristics) and a dense phase including the catalyst, where the reaction occurs (this phase has CSTR characteristics due to the particle motion). Based on the experimental results, we calibrated a model of the particle movement. The result is a complete description of the fluidization in terms of reactivity and flow patterns. In this model, we plugged a deactivation model developed considering the effect of the presence of ethylene in the methanation feed gas. To characterize the deactivation phenomenon, we performed experiments by adding ethylene as a co-feed to the methanation reaction. We performed experiments simulating various bed heights, to understand the influence in the change of composition in the reactor on the activation/deactivation pattern of the catalyst. Based on the experimental evidence, we derived a deactivation kinetic model obtained by regression. This was implemented in the global model to understand the deactivation pattern in the fluidized bed reactor and to optimize the catalyst lifetime by changing the flow pattern and/or adding steam to the feed gas.



9:30am - 9:50am

Kinetic modelling and optimisation of CO2 capture and utilisation to methane on dual function material

Meshkat Dolat1, Andrew D. Wright2, Mohammadamin Zarei1, Melis S. Duyar1,3, Michael Short1,3

1School of Chemistry and Chemical Engineering, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom; 2Department of Chemical Engineering, School of Engineering, The University of Manchester, UK; 3Institute for Sustainability, University of Surrey, Guildford, Surrey GU2 7XH, UK

Power-to-Gas (PtG) technology offers a sustainable solution for converting surplus renewable energy into synthetic natural gas (SNG) via the CO2 methanation (Sabatier) reaction. Particularly well-suited for post-combustion carbon capture applications, PtG facilitates efficient energy storage while promoting a carbon-neutral cycle when paired with renewable hydrogen. CO2 adsorption, however, remains a costly process, and the methanation reaction presents significant thermodynamic and kinetic challenges, requiring careful reactor design and effective management of heat and mass transfer. Dual-function material (DFM) technology (Duyar et al., 2015) has emerged as a promising alternative, combining CO2 adsorption and in situ hydrogenation, enabling direct conversion of CO2 from diluted streams without the need for energy-intensive purification steps typically required in cyclic adsorption/absorption processes. To scale this novel technology for industrial applications, comprehensive studies are needed to elucidate the kinetics of CO2 adsorption, purge, and hydrogenation, as well as their interdependencies with process conditions and material properties.

This study builds on kinetic modelling efforts by Bermejo-López et al., (2020) by applying a bespoke reaction rate expression for CO2 methanation using Ni-Ru/CeO2-Al2O3 catalysts developed at the University of Surrey. The model simulates the cyclic adsorption, purge, and hydrogenation processes within a dynamic plug flow reactor framework, employing finite difference methods (FDM) to discretise the coupled partial differential equations (PDEs) governing the transient nature of these processes. Python is used for simulation and parameter estimation, with the least-squares method applied for the latter.

The model explores various process conditions, such as different CO2 and hydrogen concentrations, as well as varying temperature, pressure, and cycle times, to optimise process conditions for industrial applications. This work provides a valuable framework for designing efficient PtG systems, offering insights into CO2 conversion mechanisms and identifying optimal conditions for CO2 utilisation using DFM technology.

Acknowledgments

We would like to acknowledge that this work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/Y005600/1].

References

Bermejo-López, A., Pereda-Ayo, B., González-Marcos, J. A., & González-Velasco, J. R. (2020). Modeling the CO2 capture and in situ conversion to CH4 on dual function Ru-Na2CO3/Al2O3 catalyst. Journal of CO2 Utilization, 42, 101351. https://doi.org/10.1016/J.JCOU.2020.101351

Duyar, M. S., Treviño, M. A. A., & Farrauto, R. J. (2015). Dual function materials for CO2 capture and conversion using renewable H2. Applied Catalysis B: Environmental, 168–169, 370–376. https://doi.org/10.1016/J.APCATB.2014.12.025



9:50am - 10:10am

Real-time carbon accounting and forecasting for reduced emissions in grid-connected processes

Rafael Castro-Amoedo1,2, Alessio Santecchia1, Manuel Oliveira1, François Maréchal3

1Emissium Labs, Portugal; 2Instituto Superior Técnico, Portugal; 3Industrial Process and Energy Systems Engineering (IPESE), École Polytechnique Fédérale de Lausanne, Sion, Switzerland

The lack of granular data in energy systems presents a significant challenge for effectively managing electricity consumption and reducing carbon emissions. As energy systems grow more complex, precise data becomes essential for operating and optimizing energy use. However, current emissions tracking methods often lack the temporal and spatial resolution required for efficient decision-making, leaving industries and energy managers without the insights to align operations with periods of low-carbon electricity generation.

In response to this challenge, we have developed a highly granular electricity emissions tracking system that integrates advanced machine learning algorithms and digital twin models of power grids. Our system forecasts emissions based on real-time grid conditions, enabling industries to schedule energy-intensive operations during periods of low carbon intensity. This innovative approach allows companies to balance operational needs with environmental sustainability, reducing their overall carbon footprint without compromising productivity.

Our methodology leverages the predictive capabilities of machine learning to enhance emissions forecasting, accounting for variables such as energy demand, weather patterns, and grid congestion. Digital twin models replicate the physical power grid, providing a virtual environment for simulating and optimizing energy flows. Preliminary results demonstrate that, in countries with a high penetration of renewable energy, this approach can lead to up to 40% reductions in carbon emissions by intelligently aligning electricity consumption with greener energy availability.

This research highlights the potential of advanced data analytics and grid simulations for transforming energy management practices. Our findings underline the pressing need for granular emissions tracking to empower industries and grid operators with actionable insights, driving the transition toward a sustainable, low-carbon economy.



10:10am - 10:30am

Liquid Organic Hydrogen Carriers: comparing alternatives through energy and exergy analysis

Elvira Spatolisano, Federica Restelli, Laura Annamaria Pellegrini

Politecnico di Milano, Italy

In the transition towards sustainable energy, green hydrogen has gained attention as a low-emission alternative. However, its transport is hindered by its low volumetric density. To address this challenge, various hydrogen carriers have been proposed as a more practical solution. These hydrogen-bearing compounds can be transported more easily at milder conditions and dehydrogenated upon arrival to release hydrogen. Among all the possible options, liquid organic hydrogen carriers (LOHCs) are considered promising due to their compatibility with existing infrastructure (Lin and Bagnato, 2024). Various LOHCs have been explored in the literature. While some compounds are more suitable for hydrogenation and dehydrogenation, certain criteria help in identifying optimal candidates. The ideal LOHC should have a low melting point and high boiling point to avoid solidification and volatility issues, respectively, a high hydrogen storage capacity, low dehydrogenation enthalpy, low toxicity and be cost-effective at the same time (Pellegrini et al., 2024).

Once the hydrogen carrier is selected, a detailed techno-economic assessment of the entire hydrogen transport value chain is essential to compare alternatives and demonstrate their feasibility. The H2 value chain typically includes: hydrogenation of the organic molecule exploiting green H2, produced where renewable energy sources are extensively available, transport of the hydrogenated compound up to its final destination and, upon arrival, dehydrogenation to release hydrogen.

In this framework, the aim of this work is to present a systematic methodology for comparing different hydrogen value chains. Toluene and dibenzyltoluene (DBT) are selected as representative carriers due to their promising characteristics. A harbor-to harbor scenario is discussed, to study long distance H2 transport. The hydrogenation and dehydrogenation stages were designed in Aspen Plus V11®. Based on process simulations, a detailed technical assessment is provided. Each stage of the value chain (i.e., hydrogenation, seaborne transport, dehydrogenation) is assessed through energy and exergy analysis. Energy consumptions are expressed in terms of equivalent H2, which, in the end, lowers the amount of hydrogen delivered at the utilization hub. In this way, the efficiency of each stage can be easily quantified. Weaknesses and drawbacks are pointed out, to pave the way for future process intensification.

References

Pellegrini, L.A., Spatolisano, E., Restelli, F., De Guido, G., de Angelis, A.R., Lainati, A., 2024. Green H2 Transport through LH2, NH3 and LOHC: Opportunities and Challenges. SpringerBriefs in Applied Sciences and Technology, Part F3263.

Lin, A., Bagnato, G., 2024. Revolutionising energy storage: The Latest Breakthrough in liquid organic hydrogen carriers. International Journal of Hydrogen Energy 63, 315-329.



 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: ESCAPE | 35
Conference Software: ConfTool Pro 2.6.154
© 2001–2025 by Dr. H. Weinreich, Hamburg, Germany