(59d) Distributed and Sustainable Hydrogen Economy Via Renewable-Integrated and Intensified Process Design and Optimization | AIChE

(59d) Distributed and Sustainable Hydrogen Economy Via Renewable-Integrated and Intensified Process Design and Optimization


Zantye, M. S. - Presenter, Texas A&M University
Arora, A., Texas A&M University
Hasan, F., Texas A&M University
Hydrogen is an energy carrier and versatile feedstock with a wide range of applications in refining and petrochemicals, automotive and aerospace, power generation, and transportation sectors. There are several ongoing efforts to reduce the cost of manufacturing hydrogen and incorporate it in traditional chemical industries (e.g., steel, refining, ammonia, etc.) while ensuring process sustainability and reducing environmental footprint. Currently, steam methane reforming (SMR) is the predominant route for hydrogen production [1]. However, the major challenges include high energy intensity, low methane conversions and large environmental footprint. To counter these challenges, our objective is to develop a sustainable and efficient chemical process pathway for producing blue hydrogen from natural gas feedstock while simultaneously compressing emitted carbon for sequestration purposes. To this end, we develop a sustainable process that leverages a combination of dynamic process intensification (DPI) and renewable sources (solar and wind) to power the energy demands of the hydrogen production process and carbon capture. Specifically, the intensified hydrogen production process is based on sorption enhanced steam methane reforming (SE-SMR), which utilizes an admixture of Ni-based SMR catalyst and hydrotalcite adsorbent for in-situ carbon capture [2]. In comparison to conventional SMR process, the intensified SE-SMR process has several advantages including higher reaction conversion and product purity, lower energy demands (due to lower operating temperature) and easier product separation. However, the optimal design and operation of SE-SMR systems is especially challenging as they are typically represented by complex and detailed nonlinear algebraic and partial differential equation (NAPDE)-based first-principles models. In addition, the temporal variation in renewable availability and the SE-SMR process dynamics significantly increase the complexity of the overall operation.

To handle these complexities, we develop a computational framework for optimal design and scheduling of the intensified hydrogen production module while handling the process variabilities and intermittency in renewable availability. The framework is based on a large-scale mixed integer linear programming (MILP) model that minimizes hydrogen production cost while incorporating (i) SE-SMR process design and operation constraints, (ii) power flow from grid and renewables to process equipment, and (iii) process economics. For performing high-fidelity simulations of SE-SMR processes, a generalized reaction-adsorption modeling and simulation (GRAMS) platform is utilized [3]. To maintain the MILP nature of the problem, the complex nonlinearities in SE-SMR process dynamics and cost correlations have been adequately represented by artificial neural network (ANN)-based regression models. A nationwide analysis has been performed on more than 1000 locations in the United States to evaluate the potential of the developed intensified hydrogen production technology for localized manufacturing for hydrogen refueling stations. The results indicate that with future projected cost estimates of solar and wind energy, blue hydrogen can be manufactured to meet the key cost benchmark of $2.5 per kg for small-scale applications using the proposed hydrogen production pathway.


[1] M. Wieliczko and N. Stetson, “Hydrogen technologies for energy storage: A perspective,” MRS Energy & Sustainability, vol. 7, 2020.

[2] A. Arora, I. Bajaj, S. S. Iyer, and M. M. F. Hasan, “Optimal Synthesis of Periodic Sorption Enhanced Reaction Processes with Application to Hydrogen Production,” Comput. Chem. Eng., vol. 115, pp. 89–111, 2018.

[3] A. Arora, S. S. Iyer, and M. M. F. Hasan, “GRAMS: A General Framework Describing Adsorption, Reaction and Sorption-Enhanced Reaction Processes,” Chem. Eng. Sci., vol. 192, pp. 335–358, 2018.