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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.

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The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model has been widely used to define rainfall thresholds for triggering shallow landslides. In this study, the rainfall intensity(I)-duration(D) thresholds for multiple slope units of an area in Pu’an County, Guizhou Province, China were defined based on TRIGRS. Given that TRIGRS is used to simulate the slope stability under the conditions of a given increasing sequence of I-D data, if the slope reaches instability at I = a, D = b, it will also become unstable in the case of I = a, D > b or I > a, D = b. To explore the effect of these I-D data with the same I or D values on the definition of I-D thresholds and the best method to exclude these data, two screening methods were used to exclude the I-D data that caused instability in the TRIGTS simulation. First, I-D data with the same I values when D values are greater than a certain limit value were excluded. Second, several D values were selected to exclude I-D data with the same I values for a slope unit. Then, an I value was selected to exclude I-D data with the same D values. After screening, two different I-D thresholds were defined. The comparison with the thresholds defined without screening shows that the I-D data with the same I or D values will reduce the accuracy of thresholds. Moreover, the second screening method can entirely exclude these data.