Process monitoring and diagnosis by multiblock PLS methods

TLDR

Monitoring of large continuous processes with PCA and PLS has been extended to naturally blocked subsections. Multiblock PLS methods create monitoring charts for each subsection and the whole process, enable detailed diagnostics by interrogating PCA/PLS models, and were demonstrated on a simulated multisection tubular reactor for low‑density polyethylene production. These methods detect faults earlier, identify the affected subsection, and reveal the key process variables responsible for deviations, facilitating easier diagnosis.

Abstract

Abstract Schemes for monitoring the operating performance of large continuous processes using multivariate statistical projection methods such as principal component analysis (PCA) and projection to latent structures (PLS) are extended to situations where the processes can be naturally blocked into subsections. The multiblock projection methods allow one to establish monitoring charts for the individual process subsections as well as for the entire process. When a special event or fault occurs in a subsection of the process, these multiblock methods can generally detect the event earlier and reveal the subsection within which the event has occurred. More detailed diagnostic methods based on interrogating the underlying PCA/PLS models are also developed. These methods show those process variables which are the main contributors to any deviations that have occurred, thereby allowing one to diagnose the cause of the event more easily. These ideas are demonstrated using detailed simulation studies on a multisection tubular reactor for the production of low‐density polyethylene.