Scholarlink

<p>This study introduces a multiscale model for ammonia synthesis catalysts that integrates intrinsic kinetics, intraparticle diffusion, and deactivation mechanisms, specifically water vapor self-poisoning and long-term aging. Extending the Temkin-Pyzhev kinetic framework, the model incorporates a size-dependent self-poisoning coefficient (<i>γ(dp)</i>), a time-dependent aging factor (<i>af(t)</i>), and a Thiele modulus-based effectiveness factor (<i>η</i>). Calibrated with experimental data, it accurately predicts nitrogen consumption rates (<i>r</i><sub><i>N2</i></sub>) for catalyst particle sizes ranging from 0.6 to 9.0&#xa0;mm and operational lifetimes of 2 to 5&#xa0;years, with errors as low as 0.8% for larger particles. Unlike traditional models, this approach quantifies reduction-induced deactivation, which significantly impacts larger particles by markedly reducing activity. Implemented in MATLAB, the model provides a predictive tool for optimizing catalyst design and reactor performance under industrial conditions. By linking microkinetic, transport, and deactivation phenomena, this work enhances the efficiency and longevity of ammonia synthesis processes, both traditional and novel.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multiscale catalyst model for ammonia synthesis: coupling kinetics, diffusion and deactivation

  • 期刊论文

  • 发表日期：

    2025年8月20日

查看全文
  • Nenad Zecevic

摘要

This study introduces a multiscale model for ammonia synthesis catalysts that integrates intrinsic kinetics, intraparticle diffusion, and deactivation mechanisms, specifically water vapor self-poisoning and long-term aging. Extending the Temkin-Pyzhev kinetic framework, the model incorporates a size-dependent self-poisoning coefficient (γ(dp)), a time-dependent aging factor (af(t)), and a Thiele modulus-based effectiveness factor (η). Calibrated with experimental data, it accurately predicts nitrogen consumption rates (rN2) for catalyst particle sizes ranging from 0.6 to 9.0 mm and operational lifetimes of 2 to 5 years, with errors as low as 0.8% for larger particles. Unlike traditional models, this approach quantifies reduction-induced deactivation, which significantly impacts larger particles by markedly reducing activity. Implemented in MATLAB, the model provides a predictive tool for optimizing catalyst design and reactor performance under industrial conditions. By linking microkinetic, transport, and deactivation phenomena, this work enhances the efficiency and longevity of ammonia synthesis processes, both traditional and novel.