Green ammonia is a crucial strategy for reducing carbon emissions and promoting sustainable development. However, in industrial applications, the production load of the ammonia synthesis section must be adjusted to accommodate fluctuations in renewable energy generation and hydrogen production. Consequently, the green ammonia synthesis process operates under multiple conditions with varying production loads. The operations of this multimode process introduce new challenges for process safety. Traditional fault diagnosis methods experience significant performance degradation when production conditions change. In new conditions, only a small number of normal samples can be obtained, and no fault samples are available. To address this issue, a novel transfer learning method named DA-CycleGAN is proposed for the multimode green ammonia synthesis process. This method combines a two-dimensional generation model based on CycleGAN (Cycle-Consistent Generative Adversarial Networks) with domain adaptation to enhance model performance in cross-domain tasks. The feasibility of the proposed method was initially validated using the benchmark Tennessee-Eastman process for fault diagnosis. Subsequently, a case study of the green ammonia synthesis process demonstrated that it significantly enhances performance in multimode processes, ensuring process safety and reducing losses for industrial applications.

中文翻译：

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基于 DA-CycleGAN 的多模式绿氨合成过程跨域故障诊断


绿氨是减少碳排放和促进可持续发展的重要策略。然而，在工业应用中，必须调整氨合成段的生产负荷，以适应可再生能源发电和制氢的波动。因此，绿氨合成工艺在多种条件下运行，具有不同的生产负荷。这种多模式工艺的操作对工艺安全提出了新的挑战。当生产条件发生变化时，传统的故障诊断方法的性能会显著下降。在新条件下，只能获得少量正常样品，没有可用的故障样品。为了解决这个问题，提出了一种名为 DA-CycleGAN 的新型迁移学习方法，用于多模式绿氨合成过程。该方法将基于 CycleGAN（Cycle-Consistent Generative Adversarial Networks）的二维生成模型与域适应相结合，以提高跨域任务中的模型性能。所提出的方法的可行性最初使用基准 Tennessee-Eastman 故障诊断过程进行了验证。随后，对绿氨合成工艺的案例研究表明，它显著提高了多模式工艺的性能，确保了工艺安全并减少了工业应用的损失。