Design of State Estimators for the Inferential Control of an Industrial Distillation Column

In the control of distillation columns, on-line composition measurements offer challenges. In this study, in order to predict the product compositions in an industrial multi-component distillation column from available on-line temperature measurements, two state estimators, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS), are developed and tested by using an unsteady-state column simulator. A model predictive controller (MPC) is used with the developed estimators individually for the dual composition control of the column. The performances of the developed inferential control system utilizing the estimators are found to be satisfactory considering both set-point tracking and disturbance rejection cases.