An improved correlation-based just-in-time modeling method using dynamic partial least squares and adaptive local domain partition

摘要

This paper proposes an improved correlation-based just-in-time modeling method, referring to as the ICoJIT, for improving the prediction accuracy and real-time performance of the conventional correlation-based just-in-time (CoJIT) modeling method. To achieve this objective, a novel adaptive local domain partition method has been developed based on the moving window technique and the fitting precision, which takes into account the input and output information simultaneously and has potentially the capabilities of obtaining the optimal local domain partition adaptively and capturing new process states by adding new local domains. Utilizing dynamic partial least squares and adaptive local domain partition method, multiple local domains and corresponding local models can be obtained during the offline operation stage. So online computation burden is reduced compared to CoJIT modeling method. In addition, the proposed ICoJIT modeling method can efficiently deal with nonlinearity and time-varying behavior of processes as well as the CoJIT modeling method. The effectiveness of the proposed method is demonstrated through a real industrial process dataset in sulfur recovery unit process.