Multistate analytics for continuous processes

Batch process monitoring methods, such as multiway PCA and multiblock multiway PLS, make use of process variable time profiles to normalize and define most likelihood trajectories for statistical process control. Nevertheless, a continuous process analytics counterpart has not been developed, nor addressed in the literature. This paper presents a novel methodology that defines “state variables” to determine the multiple operating points around which a continuous process operates. In this manner, the operating region is divided into multiple regions (states) and shifts in operating conditions are captured by such state variables. Transition trajectories between states are calculated to determine the most likely path from one state to another. This methodology is referred as multistate analytics and can be implemented in the context of empirical monitoring methods, named multistate PLS and multistate PCA. A case study using data from carbon dioxide removal process shows that multistate analytics is beneficial for statistical monitoring of continuous processes.

► Novel methodology to handle multiple operating points for continuous processes.
► Process operating region is divided into multiple sub-regions or states.
► Transition trajectories are defined between states.
► Application in the context of monitoring methods, called multistate PLS and PCA.
► Methodology is successfully demonstrated in a carbon dioxide removal process.