BATCH PROCESS MONITORING FOR CONSISTENT PRODUCTION

A number of limitations have inhibited the success of batch process mo nitoring:- the finite and variable duration of a batch, the presence o f significant non-linearities, the lack of on-line sensors for measuri ng quality variables, the absence of steady-state operation, the diffi culty of developing accurate mechanistic models and process measuremen ts that are autocorrelated in time as well as being correlated with on e another. Recent approaches to the monitoring of batch behaviour have been based on extensions of the statistical projection methods of Pri ncipal Components Analysis (PCA) and Projection to Latent Structures ( PLS)- multiway PCA and multiway PLS. These techniques form the bases o f the multivariate statistical process control charts for batch proces s monitoring. The control limits for detecting when a process is movin g out of control for multivariate SPC charts are based upon Hotelling' s T-2 statistic. A new approach which allows the nominal data to dicta te the form and shape of the bound, the M(2) statistic, is reviewed. F inally, an application of multivariate SPC and the impact the differen t confidence bounds have on process operation is highlighted by applic ation to a batch methyl methacrylate polymerisation reactor.