Multicycle and multimode are important features in fixed-bed reactors due to a manifold of reasons such as catalyst regeneration and equipment updates. Unfortunately, samples are not sufficient to establish an accurate model because of the frequent changes in the operating conditions. Moreover, a large amount of data from the historical cycle cannot be used directly due to different operating conditions. The online modeling of these processes faces significant challenges, such as lack of samples, nonlinearity, and multimode characteristics. To overcome this problem, an adaptive JIT-TL-SFA modeling approach is proposed by merging transfer learning (TL) and slow feature analysis (SFA) into just-in-time (JIT) learning. A novel time-space similarity measure criterion, which considers temporal relevance and spatial relevance to improve the performance of JIT, is proposed in this work. The strategy is implemented and tested on an acetylene hydrogenation process, and the results are presented and analyzed.

中文翻译：

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基于转移学习和即时学习的多周期多模态固定床反应器自适应建模

由于催化剂再生和设备更新等多种原因，多循环和多模式是固定床反应器的重要特征。不幸的是，由于操作条件的频繁变化，样本不足以建立准确的模型。此外，由于不同的操作条件，无法直接使用历史循环中的大量数据。这些过程的在线建模面临重大挑战，例如缺少样本，非线性和多模特征。为了克服这个问题，提出了一种自适应JIT-TL-SFA建模方法，该方法将转移学习（TL）和慢特征分析（SFA）合并为即时（JIT）学习。一种新颖的时空相似性度量标准 在这项工作中，提出了考虑时间相关性和空间相关性以提高JIT性能的方法。该策略是在乙炔加氢工艺上实施和测试的，并给出并分析了结果。