物理引导的条件扩散模型用于水煤气变换反应的稀有事件合成与诊断
Physics-Guided Conditional Diffusion Model for Rare Event Synthesis and Diagnosis for the Water-Gas Shift Reaction
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中文总结 AI 辅助
提出物理引导的条件扩散模型，结合反应规律生成水煤气变换反应稀有事件轨迹，增强数据平衡，并引入危险评分辅助深度学习诊断，优于数据驱动方法。
中文摘要 AI 辅助
随着世界向可持续能源方向发展，氢气（H2）因其高能量密度和零碳排放，可被视为化石燃料的环保替代品。水煤气变换（WGS）反应是一种广泛使用的工业制氢过程，通过将一氧化碳和水蒸气转化为氢气和二氧化碳。然而，诸如严重结垢、催化剂劣化和热失控等事件会阻碍反应动力学/过程安全性，并降低H2的产率。这些事件是罕见的，在异常条件下收集过程数据具有挑战性。在这项工作中，我们提出了一种物理引导的条件扩散模型，用于生成WGS反应的现实稀有事件轨迹。所提出的模型将条件去噪扩散概率模型（CDDPM）与反应的控制规律相结合，以生成物理一致的过程轨迹。条件特征使模型能够为通常超出训练范围的稀有事件域生成高质量的合成剖面。生成的稀有事件轨迹随后增强原始数据集，以实现正常和异常条件之间的平衡分布。我们进一步提出了一种危险评分，基于操作轨迹评估操作条件的风险严重程度。使用增强数据集训练深度学习模型以诊断反应的健康状态。仿真结果表明，所提出的物理引导扩散模型在合成数据质量和稀有事件的诊断性能方面优于数据驱动模型。
英文摘要
As the world moves towards sustainable energy sources, hydrogen (H2) can be treated as an eco-friendly alternative to fossil fuels due to its high energy density and zero carbon emissions. The water-gas shift (WGS) reaction is a widely used industrial process for hydrogen production by converting carbon monoxide and steam into hydrogen and carbon dioxide. However, occurrences like severe fouling, catalyst deterioration, and thermal runaway can hamper the reaction kinetics/process safety and decrease the yield of H2. These incidents are rare, and gathering process data under such abnormal conditions is challenging. In this work, we propose a physics-guided conditional diffusion model to generate realistic rare-event trajectories for the WGS reaction. The proposed model integrates a conditional denoising diffusion probabilistic model (CDDPM) with governing laws of the reaction to generate physically consistent process trajectories. The conditioning features allow the model to produce high-quality synthetic profiles for rare-event domains that are typically beyond the training regimes. The generated rare-event trajectories then augment the raw dataset for a balanced distribution between normal and abnormal conditions. We further propose a hazard score to assess the risk severity of the operating condition based on the operating trajectory. Deep learning models are trained with the augmented dataset to diagnose the health status of the reaction. Simulation results show that the proposed physics-guided diffusion model outperforms data-driven models in terms of the quality of synthetic data and diagnosis performance for rare events.
发表机构
- Texas Tech University（德克萨斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
