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2024
Journal Article
Title
Reaction Kinetics for Ammonia Synthesis using Ruthenium and Iron based Catalysts under Low Temperature and Pressure Conditions
Abstract
Ammonia (NH3) production using green hydrogen and its emerging application as carbon-free energy carrier or fuel is predicted to play an important role for the global energy transition. Yet, the inherently fluctuating production of hydrogen from renewable energy and the corresponding new boundary conditions for NH3 synthesis require efficient and intensified processes. A key strategy for the intensification of the NH3 synthesis is the shift of the synthesis conditions to lower temperature and pressure compared to the conventional Haber-Bosch process. In this work, the reaction kinetics of ruthenium- and iron-based catalysts are determined experimentally at pressures between 10 to 80 bar and at temperatures from 350 to 450 °C. Using axially resolved temperature and concentration measurement, detailed experimental data were obtained in the kinetic regime and utilized to develop kinetic models for both catalysts. Therefore, an ideal plug-flow model for a fixed bed reactor, considering the axial temperature profile, is used to estimate the kinetic parameters. The developed kinetic models are based on the extension of the Temkin equation, which is adapted for both catalysts. Remaining deviation between simulated and experimental data is reduced to a root-mean-square error for the molar fraction of NH3 of below 0.6%. The proposed extension of the Temkin equation allowed the reduction of this deviation by 20-30% compared to the conventional Temkin expression, which underlines the relevance of the novel kinetic expressions. Based on the validated kinetic models, concepts for process intensification and modularization of the NH3 synthesis can be developed towards industrial realization.
Author(s)
Nanba, Tetsuya
National Institute of Advanced Industrial Science and Technology, AIST, Renewable Energy Research Center
Open Access
Rights
CC BY 4.0: Creative Commons Attribution
Language
English