In the hydrocracking process, it is of great significance to timely measure the product attributes for real‐time process control and optimization. However, they are often very difficult to measure online due to technical and economical limitations. To this end, soft sensor is introduced to predict product attributes through easy‐to‐measure process variables, with the advantages of low cost, fast response, and ease of maintenance. In this paper, a two‐layer ensemble learning framework is developed for soft sensing of three diesel attributes in an industrial hydrocracking process. In this modeling framework, the process variables are first divided into subspace blocks according to process topological structure to capture the local behaviors of different production cells. Then, to overcome the weak generalization ability of a single calibration model with specific hypothesis, different regression learners are constructed on each variable subblock to increase the model diversity. At last, individual models are fused to improve the prediction performance and generalization ability of soft sensor models. The effectiveness and flexibility of the proposed ensemble learning method is validated on a real industrial hydrocracking process.

中文翻译：

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工业加氢裂化过程中柴油属性数据驱动软传感器的两层集成学习框架

在加氢裂化过程中，及时测量产品属性对于实时过程控制和优化具有重要意义。然而，由于技术和经济限制，它们通常很难在线测量。为此，引入软传感器通过易于测量的过程变量来预测产品属性，具有成本低、响应快、易于维护等优点。在本文中，开发了一种两层集成学习框架，用于工业加氢裂化过程中三种柴油属性的软检测。在该建模框架中，首先根据过程拓扑结构将过程变量划分为子空间块，以捕获不同生产单元的局部行为。然后，为了克服具有特定假设的单一校准模型泛化能力弱的问题，在每个变量子块上构建不同的回归学习器，以增加模型的多样性。最后，融合各个模型以提高软传感器模型的预测性能和泛化能力。所提出的集成学习方法的有效性和灵活性在真实的工业加氢裂化过程中得到了验证。