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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 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.