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This work presents a comprehensive model for a modular system that integrates green hydrogen and ammonia production with renewable energy generation. The chemical module comprises a high-temperature water electrolyzer for hydrogen production and an ammonia synthesis reactor. When solving the models over time, the system exhibits complex yet predictable dynamics, with the chemical module having a much faster response than other components. Under typical weather conditions, the renewable energy module generates over 50 kW for most of the day, partially meeting the chemical module’s energy demands. Nonlinear model predictive control (NMPC) is employed to manage the operation of the chemical module in response to variable renewable energy availability. The proposed NMPC framework determines the optimal supplemental energy required from the conventional energy grid to sustain the process. When renewable energy availability is high, the controller minimizes grid energy usage, maintaining the chemical module near its desired operating conditions with minimal reliance on external sources. Conversely, during low renewable energy availability periods, the controller increases grid energy acquisition to ensure stable system operation, demonstrating a greater dependence on external energy supplies. 

The development of coke-resistant catalysts for dry reforming of methane (DRM) is critical for sustainable syngas production. To suppress coking, this study investigates the use of Ti3C2Tx and Nb2CTx MXenes as support for Ni catalysts in DRM and benchmarked their performance with conventional catalysts (Ni/γ-Al2O3, Ni/MgAl2O4, and Ni/SiO2). The MXenes were etched using NH4HF2, and a 10 wt% Ni loading on the supports was achieved via wet impregnation synthesis. Ni/Nb2CTx showed the highest H2 consumption (10.4 mmolH2/gcat). DRM was conducted at 700 °C using a feed ratio of CH4/CO2 of 1:1 and a high space velocity (90,000 ml/gcat h). Unlike the other catalysts, Ni/Nb2CTx pre-reduced at 500 °C exhibited a low normalized coking rate (4.41 µgcoke/mmolCH4), a high overall reaction rate (104 ± 13 mmol/gNi.min), and the highest turnover frequency at 16.7 s−1. The apparent CO2 reaction rate at these conditions was similar to the CH4 rate, suggesting that the low coking rate was due to the efficient utilization of dissociated oxygen. Molecular dynamics (MD) simulations performed on NbC(111) and TiC(111) surfaces at 700 °C and atmospheric pressure reveal that the efficient utilization was mediated by rapid oxygen spillover. The average oxygen velocity from the simulations was slightly higher on NbC (0.0969 Å/fs) than on TiC (0.0961 Å/fs). Both MXene supports are transformed to stable oxycarbides during DRM, and Nb2CTx was stable for 50 h TOS. This investigation not only highlights the potential of Ni/Nb2CTx as a coke- and sintering-resistant catalyst but also demonstrates the role of MXenes supports in the DRM process. 

The synthesis of carbon nanotubes has attracted considerable interest due to their unique physical and structural properties. Despite notable experimental advancements, particularly in chemical vapor deposition (CVD) techniques, a significant gap remains in developing comprehensive mechanistic models that correlate nanotube growth dynamics with gas-phase composition. The CVD involves a complex interplay of multiscale phenomena, including hydrocarbon transport within the reactor and surface reactions on catalyst nanoparticles, collectively contributing to nucleation and growth. This paper introduces a computational modeling framework that integrates these phenomena by leveraging density functional theory energy data, microkinetic modeling, and computational fluid dynamics. The proposed approach addresses the challenges inherent in this multiscale-multiphysics problem, providing insights into nanotube growth as a function of gas composition and transport, temperature, and catalyst properties. The simulation results show strong agreement with experimental trends, highlighting the significance of gas-phase reactions in a mixed hydrocarbon feedstock and the effects of catalyst deactivation.