Abstract: Systems and methods (300) for offline/online performance monitoring of batch processes (BPs) involving obtaining archived data (AD) obtained during runs of BP and including information defining a batch quality attribute for each run. The method also involves forming clusters by classifying AD for the runs into classes based on the batch quality attribute(s) and building a first multivariate statistical model (MSM) using AD. The method can further involve building a wavelet analysis based feature matrix (FM) using AD, forming a first projection (1200) by projecting FM onto a first MSM, building a second MSM (1300) using information obtained from the first projection, and computing centroids (C902, . . . , C918) and boundary profiles for the clusters (902, . . . , 918). The method can involve performing an online/offline performance monitoring (700/800) using an integrated version of the first and second MSM, a classification algorithm, centroids, and boundary profiles.

Abstract: A method (300, 400, 500, 1200) for offline/online monitoring of batch processes. The method involves (312) decomposing a time domain of a batch process run (BPR) into several blocks and (334) building multivariate statistical models (MSMs) for each of them using archived data for a batch process (ABPD). ABPD comprises stored data obtained during BPRs. The method also involves (506, 1204) retrieving recently stored data (RSD) for a recent fully performed BPR run (FPRNEW) or current BPR run. The method further involves (520, 1210) building a feature vector matrix (FVM) using RSD. FVM contains feature vectors representing statistical measures of wavelet coefficients determined for variables (v0, . . . , vJ). A projection (1100, 1150, 1190) is formed by projecting feature vectors onto at least one MSM or a combined multivariate statistical model (CMSM). CMSM is a weighted average of at least two MSMs.

Abstract: Systems and methods (300) for offline/online performance monitoring of batch processes (BPs) involving obtaining archived data (AD) obtained during runs of BP and including information defining a batch quality attribute for each run. The method also involves forming clusters by classifying AD for the runs into classes based on the batch quality attribute(s) and building a first multivariate statistical model (MSM) using AD. The method can further involve building a wavelet analysis based feature matrix (FM) using AD, forming a first projection (1200) by projecting FM onto a first MSM, building a second MSM (1300) using information obtained from the first projection, and computing centroids (C902, . . . , C918) and boundary profiles for the clusters (902, . . . , 918). The method can involve performing an online/offline performance monitoring (700/800) using an integrated version of the first and second MSM, a classification algorithm, centroids, and boundary profiles.

Abstract: A method (300, 400, 500, 1200) for offline/online monitoring of batch processes. The method involves (312) decomposing a time domain of a batch process run (BPR) into several blocks and (334) building multivariate statistical models (MSMs) for each of them using archived data for a batch process (ABPD). ABPD comprises stored data obtained during BPRs. The method also involves (506, 1204) retrieving recently stored data (RSD) for a recent fully performed BPR run (FPRNEW) or current BPR run. The method further involves (520, 1210) building a feature vector matrix (FVM) using RSD. FVM contains feature vectors representing statistical measures of wavelet coefficients determined for variables (v0, . . . , vJ). A projection (1100, 1150, 1190) is formed by projecting feature vectors onto at least one MSM or a combined multivariate statistical model (CMSM). CMSM is a weighted average of at least two MSMs.