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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,...
Just‐in‐time learning (JITL) technique has been widely used for adaptive soft sensing of nonlinear processes. It builds online local model with the most relevant samples from historical dataset whenever a query sample comes. Hence, the prediction performance greatly depends on the similarity measurement for relevant sample selection. Different similarity measurements have been developed for sample...
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