Conference Agenda

Simulation plays a crucial role in the design and optimization of gasifiers by providing a detailed understanding of the involved physical processes and complex chemical reactions without the need for extensive trial-and-error experiments. It can also serve as a valuable tool for identifying potential technical issues in experimental devices that operate below expected performance. Simulations can reveal discrepancies between theoretical predictions and actual performance, helping to identify inefficiencies or malfunctions in experimental setups. By comparing simulated outcomes with experimental data, researchers can systematically investigate the root causes of deviations, enabling targeted troubleshooting and refinement of the experimental design. This proactive approach reduces downtime, optimizes performance, and ensures that gasification systems operate closer to their intended efficiency and output.

This study presents a comprehensive simulation of biomass gasification using the open-source software DWSIM. The simulated results were compared with experimental data from a pilot-scale spouted bed reactor, featuring a square-based design with a 20 kWth capacity, using wood pellets as feedstock. The original reactor design [1] was modified to enhance its performance, and a complete experimental campaign was conducted to evaluate the effectiveness of these modifications.

The reactor's thermo-chemical conversion process was simulated using a kinetic approach in DWSIM, accounting for key parameters such as temperature profiles, equivalence ratios, and gas composition. Experimental results revealed that the reactor operated effectively at temperatures exceeding 800°C, maintaining stable conditions across a wide range of equivalence ratios. However, the distribution of products—particularly hydrogen (H2)—did not match expected results based on both literature and simulations. A joint analysis of experimental data and expected behavior from simulation helped identify the observed inefficiencies and optimize the reactor's performance.

[1] DOI 10.1002/cjce.23223

Conventional amine-based CO₂ capture is burdened by its high energy consumption. As a result, current research efforts notably focus on process intensification. In parallel with the development of alternative solvents, many process modifications have been discussed in the literature. These modifications introduce additional process units into the initial flowsheet to enhance absorption or integrate heat more effectively. Among these modifications, heat pumping is gaining attention as it relieves the reboiler of some energy demand by increasing the temperature of available waste heat at the cost of additional mechanical work. Several configurations are available in the literature and can be broadly divided into two categories. Closed-loop heat pumps, which work with an intermediate refrigerant, operate between different process streams. In contrast, open-loop heat pumps use one of the process streams as the working fluid.

While these modifications lead to energy savings, they increase cost and complexity, requiring costly mechanical work. Therefore, the integration of such process units should be carefully considered. Published results often report different performance indicators, such as reboiler duty, equivalent work, or efficiency loss, making systematic comparison difficult without conversion to a common metric.

With this in mind, this work proposes a methodology to evaluate systematically the inclusion of a heat pump effect within CO₂ capture. It relies on the theoretical framework of Pinch Analysis, a process engineering tool used to assess the minimum energy consumption of a process and the possible heat integration improvements.

The proposed methodology starts with an extensive description of the heat requirements of the system. The initial flowsheet is analysed through three successive lenses. At first, the stripping column is split into individual equilibrium stages and each stage is represented as a heat exchanger with mass exchange. This simplified model gives insight into the actual mass transfers and the corresponding energy needs at the various temperature levels. Next, the Column Grand Composite Curves (CGCC) are drawn to compare the previous description to the optimal energy distribution. Finally, conventional Pinch Analysis is used to identify heat integration opportunities between the energy consumer (that is the column) and the background process.

Once the energy consumption is well understood, heat pumping opportunities are identified thanks to the Grand Composite Curve (GCC). Both closed-loop and open-loop cycles are studied to compare their performance and integration potential. The GCC shape, which describes the heat availability at different temperature levels, is interpreted. Identified opportunities are modelled and compared. Closed-loop heat pumps are modelled by a linearised relationship between coefficient of performance (COP) and temperature lift while open-loop heat pumps are directly integrated into the flowsheet. Finally, temperature-enthalpy diagrams are used to evaluate the energy requirements before and after heat pump integration.

To demonstrate the methodology, it is applied to an example flowsheet that models an MEA-based CO₂ capture unit that separates CO₂ from the flue gases of a biomass cogeneration plant.

The urgent need to reduce global temperatures, minimize greenhouse gas emissions, and achieve energy independence has driven the search for sustainable energy solutions. Steam co-gasification of biomass and plastic waste presents a vital pathway for promoting renewable energy by offering a clean alternative for producing fuels, sustainable aviation fuels, and alcohols like methanol and ethanol. This process can significantly reduce greenhouse gas emissions and decrease reliance on fossil fuels. Modelling and simulation play a crucial role in understanding gasification behaviours, particularly in optimizing syngas yield. However, despite the importance of modelling for process optimization, there is a notable lack of comprehensive comparative studies on Aspen Plus modelling techniques for steam co-gasification in the literature.

Syngas, the primary product of biomass and plastic waste gasification, is essential for various energy applications, making the accurate prediction of its composition critical. This study addresses the gap by comparing three Aspen Plus modelling strategies—thermodynamic equilibrium modelling (TEM), restricted thermodynamic modelling (RTM), and kinetic modelling (KM)—to simulate the co-gasification of pine sawdust and polyethene using steam as the fluidizing medium. The primary aim of the research is to evaluate the effectiveness of these strategies in predicting syngas composition and to identify the most suitable approach for the co-gasification process under different operating conditions.

The methodology involves developing three separate models based on thermodynamic, restricted thermodynamic, and kinetic principles using Aspen Plus software, followed by a comparison of the predicted syngas compositions with experimental data from published literature [1]. In particular, for the restricted thermodynamic model (RTM), a detailed sensitivity analysis was conducted on 17 experimental syngas compositions to optimize reaction temperatures. The purpose of this sensitivity analysis was to improve the match between predicted and actual syngas composition values. These approach temperatures were subsequently applied to calculate new syngas compositions, which were compared against the experimental results to assess the accuracy of the models.

The RTM demonstrated the highest accuracy, achieving an average Root Mean Square Error (RMSE) of 0.0296 when compared to experimental syngas compositions. In contrast, the TEM showed the least accuracy, with an RMSE of 0.1234, highlighting its limitations in predicting the syngas composition for this process. The optimal solution derived from the RTM also exhibited improved accuracy, with an RMSE of 0.0880, while the KM performed moderately, with an RMSE of 0.0929. Notably, the optimal solution derived from the RTM presents a good alternative when detailed kinetic data is unavailable, while also offering the advantage of avoiding the computational expense associated with conducting a sensitivity analysis within the RTM framework. The findings of this study contribute to the ongoing efforts to optimize syngas prediction, thereby supporting the development of more efficient and sustainable bioenergy technologies. This research paves the way for a more comprehensive evaluation of predictive accuracy in co-gasification processes, particularly for mixed feedstocks like biomass and plastic waste.

[1] Pinto, F., et al. "Co-gasification study of biomass mixed with plastic wastes." Fuel 81.3 (2002): 291-297.

The distribution of operating parameters within PEM fuel cells under dynamic conditions is inherently nonuniform. Traditional designs, employing constant platinum loading and temperature, often encounter operational challenges due to inefficient heat and mass transfer. When humidified reactants are used, large amounts of liquid water accumulate in porous layers of the cell, making it difficult to achieve high current densities without purging. This purging process, however, leads to significant hydrogen fuel wastage. Conversely, if the reactants are not fully humidified and the cell operates at a constant temperature, the ionomer in the membrane and catalyst layers risks dehydration and starvation. To address these issues, three critical advancements were developed by our research group.

Firstly, to address these issues, a spatially variable temperature profile, or a variable temperature flow field, can be employed. This approach helps balance the production of water and the water vapor partial pressure, ensuring fully humidified conditions across the cell's active area without requiring external humidification. This concept was explored through computational fluid dynamics and experimental research, revealing that a variable temperature flow field can be established by using a liquid coolant with a low flow rate. As the coolant heats up from the cell's waste heat, the desired temperature profile is maintained, enhancing operational efficiency.

Further improvements were achieved by combining the variable temperature profile with a graded catalyst design. Experimental and numerical studies identified the optimal pairing of a variable temperature flow field and graded Pt loading in the cathode catalyst, yielding significantly higher performance. In fact, compared to isothermal operation with uniform catalyst loading, the optimal design demonstrated a 260% increase in current density at 0.6 V, with a 19% reduction in Pt utilization and a notably more uniform current density distribution.

To further enhance performance further, novel flow fields with secondary channels for water removal near the cathode outlet were developed using 3D metal printing. These designs enabled higher current densities with minimal adjustments. These designs leveraged inertial effects in the diffusion layers caused by high flow velocities, a novel concept for PEM fuel cells where flow is typically laminar.

Overall, these innovative approaches—combining variable temperature profiles, graded catalyst designs, and advanced flow fields—demonstrate the potential to significantly improve PEM fuel cell performance. These findings point toward the future development of tailored anisotropic heat and mass transfer systems for next-generation fuel cells, offering enhanced performance and durability.

This work presents a comprehensive multiscale model for Solid Oxide Fuel Cells (SOFCs), integrating microscale and macroscale simulations to analyze internal reforming and its impact on overall cell performance. Macroscopic models have been shown to accurately predict SOFC performance, but require extensive calibration with experimental results [1,2].

Our multiscale model combines a microscale and a macroscale SOFC description. The microscale model [3], [4] captures the intricate mass and charge transport phenomena at the pore scale of porous electrodes, resolving electrochemical reactions at the triple-phase boundaries and modeling chemical reactions at pore spaces. Simultaneously, the macroscale model provides a broader view of the entire cell's behaviour by solving the same transport equations on a much coarser computational mesh. The multiscale approach is particularly useful for addressing the challenges posed by concurrent chemical and electrochemical reactions at the anode, which complicate the modelling of internal reforming. To overcome these challenges, a novel approach is introduced, spatially separating the regions of chemical and electrochemical activity in the pore scale domain by taking the electrochemical active layer thickness into consideration.

The integrated multiscale model is applied to a full-scale internal reforming SOFC to explore how electrode morphology, particularly the use of gradient microstructures on the anode, influences cell performance. By varying the porosity across the anode—linearly from the fuel channel to the electrolyte—this study provides new insights into optimizing SOFC efficiency.

Optimizing the porosity distribution is crucial in internal reforming cells, where mass transport limitations play a key role in determining overall performance. The findings emphasize the importance of connecting micro- and macro-scale behaviours to enhance predictive accuracy and reduce the model's reliance on experimental calibration.

References

[1] K Tseronis, I Bonis, IK Kookos, C Theodoropoulos “Parametric and transient analysis of non-isothermal, planar solid oxide fuel cells” 2012, International journal of hydrogen energy Vol. 37, 530-547

[2] K. Tseronis, IS Fragkopoulos, I. Bonis, C. Theodoropoulos. “Detailed Multi‐dimensional Modeling of Direct Internal Reforming Solid Oxide Fuel Cells” 2016, Fuel Cells, Vol. 16, 294-312

[3] H. R. Abbasi, M. Babaei, A. Rabbani, and C. Theodoropoulos, ‘Multi-scale model of solid oxide fuel cell: enabling microscopic solvers to predict physical features over a macroscopic domain’, in Computer Aided Chemical Engineering, vol. 52, Elsevier, 2023, pp. 1039–1045.