(564d) Predictive Modeling and Optimal Regulation of Modular Hydrogen-Ammonia Systems with Renewable Energy Integration for Resilient Chemical-Energy Conversion

The drive to implement more sustainable chemical systems in the 21st century has motivated and propelled the scientific community to investigate innovative, multi-functional processes [1]. Numerous studies have explored the feasibility and resilience of integrating chemical systems and renewable energy technologies, focusing on four potential economic entities: solar and wind power, hydrogen, and ammonia [2-5]. The former two are low-cost, intermittent, and accessible clean energy sources, while the latter two are commodities with crucial applications across various sectors of a functional economic chemical-energy matrix. Hydrogen is produced in large quantities by catalytic steam and dry reforming of hydrocarbons, a non-renewable process that generates significant atmospheric pollution in CO and CO₂. Recent technological advancements have made it possible to competitively produce hydrogen through greener processes, such as water electrolysis. Conversely, ammonia is industrially produced at high pressures by catalytic hydrogenation of atmospheric N₂ through the Haber-Bosch process, relying on air separation technologies, typically adsorption and hydrogen supply.

Other studies propose a new paradigm for chemical manufacturing, shifting from the few large-scale production complexes existing today towards more compact, locally active production systems [6,7]. The benefits of modularized, self-sufficient, and integrated chemical and renewable energy systems include maximizing economic metrics, physical intensification between processes, and reconciling sustainability with environmental concerns. Naturally, this new perspective involves significant technical challenges, such as inherent operational restrictions, intermittency of renewable energy supplies, highly integrated chemical and energy systems, and real-time automation and control. In this context, computational modeling investigations can provide meaningful insights into the operation of modular chemical-energy systems and the necessary predictive capabilities for informed decision-making and control.

While there is relevant work on modeling advanced chemical processes such as hydrogen and ammonia synthesis with different levels of physical intensification (chemical-energy polygeneration, material recycles, etc.), the majority of these studies have only minimally addressed real-time decision-making and control challenges, particularly when considering the direct involvement of renewable energy inputs [8-10]. This limitation is primarily due to complications in the mathematical formulation and numerical solution of the resulting highly integrated and nonlinear differential and algebraic equations. This work develops a phenomenological dynamic model for a modular renewable energy-driven system that simultaneously produces hydrogen and ammonia for resilient energy storage and fertilizer production in rural communities. Specifically, this study focuses on integrating the chemical production system with renewable energy provisions through concentrated solar-thermal and wind power technologies. The hydrogen-ammonia production module incorporates a dynamic axial-dispersion formulation that considers both kinetics and thermodynamic effects. Additionally, other units, such as compressors and heat exchangers, are integrated into the system model to harmonize the distinct operations of the water electrolyzer and ammonia reactor. The solar plant is modeled using transient mass and energy balances that account for variations in solar irradiance throughout the day. Furthermore, a correlation is utilized to directly relate blade height and wind velocity to the output power, facilitating the prediction of wind turbine dynamics [11]. Furthermore, the model efficiency and the integrated system’s stability and performance are investigated using model predictive control under constraints imposed by the availability of renewable energy. The controller determines the optimal amount of additional energy input to be acquired from external sources (such as the grid or non-renewables) to achieve the desired steady state.

References:

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[3] Matthew J. Palys, Hanchu Wang, Qi Zhang, and Prodromos Daoutidis. Renewable ammonia for sustainable energy and agriculture: vision and systems engineering opportunities. Current Opinion in Chemical Engineering 31 (2021).

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[11] Andrew Allman and Prodromos Daoutidis. Optimal scheduling for wind-powered ammonia generation: Effects of key design parameters. Chemical Engineering Research and Design, 131:5–15 (2018)