Conference Agenda

Refuse-derived fuel (RDF) is produced from municipal solid waste (MSW) by removing inorganics and biodegradables. RDF has a significantly higher heating value compared to MSW and can be used as a raw material in energy and material recovery processes such as incineration, gasification, and pyrolysis. Although several studies have focused on thermochemical conversion in recent years, due to the complex nature of the combustion, gasification, and pyrolysis processes, no studies have been published comparing all the techno-economic aspects of these processes comprehensively.

In this work, a comprehensive techno-economic evaluation of the three thermochemical conversion processes is presented. For processing 10 t/h of RDF, computer simulation models for incineration, gasification, and pyrolysis plants were developed using Aspen Plus. Flue gas cleaning and combined heat and power generation were included in the case of incineration, while syngas cleaning, heat and prioritized power generation were modeled for gasification. For the pyrolysis process, upgrading of char and oil yields was included. Material and energy integration for each thermal conversion plant was performed. In all cases, the treatment of outgoing gaseous streams was set to meet the required limits for contaminants. The input data for the models were obtained through experimental measurements. All three processes were evaluated and compared from technical, environmental, economic, and safety perspectives.

All three processes demonstrated the ability to be used for energy recovery. Pyrolysis showed the greatest potential for material recovery, specifically char and oil. Syngas production via gasification, when intended for purposes other than power and heat generation, requires additional syngas purification. Incineration had the lowest investment costs but the greatest environmental impact due to emissions and greenhouse gas generation. Gasification was the most complex process, with the highest investment costs, but offered higher energy efficiency and lower emissions compared to incineration. Assuming a market exists for upgraded pyrolysis products, pyrolysis appears to be the most profitable thermal conversion process, however, the highest amount of wastewater was produced pyrolysis products upgrading. Based on the calculated Dow Fire and Explosion Index (F&EI), gasification was identified as the most hazardous process, followed by pyrolysis and incineration. The toxicity index (TI) for all three processes was found to be similar, making all three processes hazardous.

In summary, based on this extensive techno-economic analysis, the ranking of processes from an economic standpoint is: pyrolysis, incineration, gasification. From an environmental perspective, the ranking is: gasification, pyrolysis, incineration. In terms of safety, the order is: incineration, pyrolysis, gasification.

Power-to-Ammonia (PtA) technology offers a sustainable and efficient solution by integrating renewable energy sources with carbon-neutral fuel production. This approach allows energy to be stored in the form of ammonia, which can serve as a fuel, energy carrier, or key ingredient in fertilisers [1], [2]. However, the synthesis process presents complex multiscale challenges involving flow, heat, and mass transfer, particularly due to the highly exothermic nature of the reaction. Advanced modelling techniques, such as Computational Fluid Dynamics (CFD), are essential to address these challenges, enabling optimization of reactor performance, catalyst utilization, and overall energy efficiency [3].

Several studies have focused on CFD simulation of the PBR reactor in steady state. Gu et al. [4] explored decentralized ammonia synthesis for hydrogen storage and transport using a CFD model, focusing on a small-scale Haber-Bosch reactor with Ruthenium-based catalysts optimized for mild conditions. The results showed that temperature is the most influential factor affecting ammonia production and pressure primarily affects the chemical equilibrium. Furthermore, the study identified an optimal gas hourly space velocity (GHSV) of for efficient ammonia synthesis. Nikzad et al. [5] compared the performance of three different reactor configurations to find the optimal design to enhance nitrogen conversion and reduce pressure drops using 2D CFD simulations to analyze and compare the mass, energy and momentum conservation in each reactor type. Tyrański et al.[6] investigated the ammonia synthesis process in an axial-radial bed reactor using CFD focusing on understanding the influence of catalyst bed parameters, such as particle size and geometry modifications, on the efficiency of the reactor. The simulations demonstrated that smaller catalyst particles (1-2 mm) provided a higher ammonia formation rate, while larger particles showed a slower reaction rate spread throughout the bed.

However, in PtA systems, the intermittent nature of renewable energy sources, such as hydrogen production via electrolysis introduces additional complexities in reactor performance. Boundary conditions and external disturbances (variations in hydrogen flow or temperature fluctuations) can significantly impact key process variables, including reaction rates, heat transfer, and overall system stability. Therefore, understanding the reactor's transient response under such dynamic conditions is crucial. This study focuses on developing a 2D transient CFD model for ammonia synthesis within a fixed-bed reactor to analyze the effects of boundary conditions and external perturbations on reactor performance. By evaluating these transient states, the research aims to provide insights for reactor optimization and improvements on the scalability and economic viability of PtM technology under fluctuating energy supply conditions.

When planning wind farm projects, it is crucial to quantify and assess the wind resource of the candidate site. This assessment is typically conducted using the wind energy density, which requires the probability distribution of wind speed f_v(v) and the power curve of a wind turbine P_WT(v). Furthermore, based on f_v(v), P_WT(v) and using the change of variable theorem, it is possible to obtain the probability density function of the power of a wind turbine, f_PWT(P_WT), which provides additional insight into the energy that can be produced.

However, to optimize the management of a wind farm, it is necessary to know the probability density function of the power generated by the entire farm, f_PWF(P_WF). This function makes it possible to estimate the variability and availability of the power generated by the farm, facilitating production planning in a given period, and ensuring the integration of wind energy into the electricity grid, since by being able to determine the probability of obtaining a given power, optimal decisions can be made taking this probability into account.

One way to obtain f_PWF(P_WF) is to follow the same procedure used before. This requires a simple analytical expression of the wind farm power curve, P_WF(v), that allows the use of the change of variable theorem. For example, the power produced by a wind farm can be taken to be the power of one wind turbine times the number of turbines, P_WF(v)=n_turb·P_WT(v). However, this approach has the disadvantage that it does not consider a significant source of power loss such as the wake effect (turbines shading each other) due to wind direction. A first alternative to incorporate the wake effect would be to use a wind density function and a wind farm power curve dependent on wind speed and direction q, f_(V,Θ)(v,θ), P_WF(v,θ), however, obtaining these expressions and applying the change of variable theorem to distribution functions of several variables is very complicated. For this reason, some authors have tried to keep the P_WF(v)=n_turb·P_WT(v) approximation and add a wake coefficient term (Feijóo & Villanueva, 2017), simplifying some wake model such as Jensen's, but this approximation is only valid for wind farms with n´m rectangular geometry. This paper proposes that, from a sample of historical wind speed and direction data from a wind farm location, a sample of wind farm power output data is generated, using the Katic wake model (Katic et al., 1987) to calculate the effective velocity incident on each wind turbine. Then, the power generated by each wind turbine P_WT(v) is calculated and the total power of the wind farm is obtained as the sum of the individual powers. Finally, the wind farm power probability density function f_PWF(P_WF) is obtained using kernel estimators.

References

Feijóo, A., & Villanueva, D. (2017). Contributions to wind farm power estimation considering wind direction-dependent wake effects. Wind Energy, 20(2), 221–231.

Katic, I., Højstrup, J., & Jensen, N. O. (1987). A Simple Model for Cluster Efficiency. EWEC’86 Proceedings, 1, 407–410.

Policy makers around the world are moving towards designing systems to foster a sustainable future by emphasising environmental conservation, notably through reduction of carbon footprints within the system. The three systems, water, energy and food, are intertwined since the effect of any of these systems can affect others. The biomass energy-water-food nexus system as a whole is a subject of considerable scholarly inquiry, pursued for diverse purposes including allocation of resources, energy, food and water security management and formulation of sustainable policy strategy.

Management of this system confronts some uncertainties in terms of parameters, which causes a diverse range of outputs for decision making by policymakers. This work proposes a mathematical model incorporating uncertain parameters in the biomass energy-water-food nexus system. The model is the used for carrying out model-based optimisation for allocating optimal resources, reducing carbon footprint, increasing economic potential and managing resources sustainably in the whole system.

The superstructure of the whole system includes power plants, effluent treatment plants, agricultural field, livestock sectors, deep wells, rainwater harvesting systems and solar energy generation units. The water sources include river water, underground water from aquifers, treated water from effluent treatment plants, water supplied from power plant and water from rainwater harvesting systems placed in agricultural sectors, livestock sectors, domestic sectors and in effluent treatment plants. The energy system includes power plants utilising biomass and natural gas. Moreover, solar systems, installed in all the four sectors, supply the required energy to the system. While energy and water are utilized to produce food in agriculture, biomass from food waste is used to generate energy in the system. Uncertain values of the parameters corresponding to the rainwater precipitation coefficient and solar energy radiation flux in the system are considered.

The novel aspects of this work include formulating and solving the problem as a mixed-integer linear program and addressing the presence of uncertain parameters through a stochastic mathematical programming approach. Additionally, expected values of generated scenarios of uncertain parameters are used to solve the overall optimisation model. The solution of the optimisation problem offers policy makers profound insight into resource allocation across diverse contexts. Taking maximising economic benefit as an objective function, this work compares the results of the deterministic model with the results computed through incorporating uncertainty in the parameters. The results indicate that incorporation of uncertainty gives rise to diminished profitability than the deterministic model, while the amount of GHG emission is reduced. On the other hand, when taking minimizing GHG emission as an objective function, a much greater loss in the profitability from the stochastic model is obtained compared to deterministic model. Apart from economic benefit and GHG amount calculations, the optimisation of the system also provides different structural decisions, such as the number of installations of effluent treatment plants, rainwater harvesting systems and solar panels in the system. The effect of uncertain parameters on economic and environmental objective functions and structural decisions will be discussed.

This study presents highly accurate thermodynamic and kinetic predictions for ammonia synthesis on commercial iron-based catalysts, based on a century of experimental equilibrium and kinetic data. To address challenges in conventional Equations of State (EOS) that exhibit large deviations at high pressures due to ammonia's polarity, a modified Soave-Redlich-Kwong (SRK) EOS with an additional polarity correction factor is employed and consequently fitted to equilibrium data. This modification allows the use of a simple Arrhenius equation to predict equilibrium data effectively. A kinetic model is developed using a Langmuir-Hinshelwood approach, considering N* and H* species on the catalyst surface. The model is fitted to 11 datasets and incorporates a Relative Catalytic Activity factor for each catalyst. The model accurately describes all trends across all iron-based catalysts under a wide range of conditions, supporting and de-risking the global trend towards reducing operational pressure and temperature in ammonia production for energy savings.

Many efforts are underway to mitigate greenhouse gas emissions due to rising concerns about global warming. Among these gases, carbon dioxide (CO₂) is one of the most significant, particularly as it is released in large amounts from power plants and chemical industries.

Researchers have studied post-combustion CO₂ capture through chemical absorption in the conventional column for years. However, the large size of the conventional column has been recognized as a hindrance to CO₂ capture, which is associated with significantly high capital and operating costs and high energy requirements. To tackle these issues, a rotating packed bed (RPB) has been proposed, where the rotational force enhances mass transfer by increasing the liquid-gas interfacial area, which significantly reduces the size of the bed (Agarwal et al., 2010).

Recently, there have been efforts to improve the RPB absorbers. For example, Wu et al. demonstrated a novel multiple-liquid inlet rotating packed bed (MLI-RPB), which showed higher liquid mass transfer compared to conventional RPBs (Wu et al., 2018). Oko et al. show that the liquid phase temperature could rise significantly, which can be mitigated by installing intercoolers for the RPB (Oko et al., 2018). Although both studies proposed new RPB structures, neither analyzed CO₂ capture efficiency with these designs. In this study, we also suggest a multi-feeding strategy in RPB that combines the concept of intercooling and multi-inlet and analyze the CO₂ capture efficiency using mathematical modeling and simulation.

The model for the multi-feeding RPB with monoethanolamine (MEA) was developed using balance equations coupled with the thermodynamic model (electrolyte-non-random two-liquid (eNRTL) model) and the two-film theory for the liquid-gas mass transfer. The developed RPB model was validated for single feeding conditions using experimental data in the literature (Jassim, 2002 and Kolawole, 2019)

Our simulation results show that introducing a low-temperature MEA solution in the middle of the absorber improves capture efficiency by 1.3–2.5% compared to the conventional lab-scale RPBs. As the size of the absorber increases, the efficiency of the multi-feeding RPB is expected to improve even further. This is due to the short residence time in the lab-scale RPB absorber. The temperature rise could be higher with a stronger MEA solution as a solvent and industrial-scale RPB absorber (Oko et al., 2018), which implies that the use of the multi-feeding RPB (MF-RPB) might be more beneficial in such situations. Further simulations will be carried out by varying operating variables, such as liquid-to-gas ratios, intercooling location and the ratio of side-feed to main-feed.

<Reference >

1. Agarwal, L., Pavani, V., Rao, D. P., & Kaistha, N. (2010) Industrial and Engineering Chemistry Research, 49(20), 10046–10058.
2. Wu, W., Luo, Y., Chu, G. W., Liu, Y., Zou, H. K., & Chen, J. F. (2018). Industrial and Engineering Chemistry Research, 57(6), 2031–2040.
3. Oko, E., Ramshaw, C., & Wang, M. (2018). Applied Energy, 223, 302
4. Jassim, M. S. (2002).
5. Kolawole, T. O. (2019).