Inferential control system of distillation compositions using dynamic partial least squares regression

In order to control product compositions in a multicomponent distillation column, the distillate and bottom compositions are estimated from on-line measured process variables. In this paper, inferential models for estimating product compositions are con- structed using dynamic Partial Least Squares (PLS) regression, on the basis of simulated time series data. It is found that the use of past measurements is e€ective for improving the accuracy of the estimation. The in¯uence of selection of measurements and sam- pling intervals on the performance is also investigated. From the detailed dynamic simulation results, it is found that the cascade control system based on the proposed dynamic PLS model works much better than the usual tray temperature control system.