On-line batch process monitoring using hierarchical kernel partial least squares

In this paper, new monitoring approach, hierarchical kernel partial least squares (HKPLS), is proposed for the batch processes. The advantages of HKPLS are: (1) HKPLS gives more nonlinear information compared to hierarchical partial least squares (HPLS) and multi-way PLS (MPLS) and (2) a new batch process monitoring using HKPLS does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The proposed method is applied to the penicillin process and continuous annealing process and is compared with MPLS and HPLS monitoring results. Applications of the proposed approach indicate that HKPLS effectively capture the nonlinearities in the process variables and show superior fault detectability.

Research highlights
► Hierarchical kernel partial least squares is proposed for monitoring batch processes.
► HKPLS gives more nonlinear information compared to HPLS and MPLS.
► HKPLS does not need to estimate or fill in the unknown part of the process.
► HKPLS show superior fault detectability in process monitoring.