Dynamic composition estimation for a ternary batch distillation

A novel Kalman estimator has been proposed to provide the estimates of dynamic composition in a ternary batch distillation process operated in an optimal-reflux policy. The estimator is formulated based on a sequence of reduced-order process models representing a whole batch behavior. Therefore, the full-order models are first developed around different pseudo-steady-state operating conditions along batch optimal profiles. Then they reduce their orders to achieve all state observability and controllability by a balanced truncation method. In the estimator scheme, the reduced models as well as relevant covariance matrices of process noise are pre-scheduled and switched according to any desired periods. Four important issues have been studied including selection of a sensor frequency, effects of an integrating step size, a state initialization and a measurement noise. The performances of the reduced estimator have been investigated and compared with those of a conventional nonlinear estimator. Simulation results have demonstrated that the performances of the novel linear estimator are reasonably good and almost identical to the nonlinear estimator in all cases, though the linear estimator performs rather sensitively to the effect of high measurement noise. Nevertheless, it has been found to be applicable to implement in real plants with much lower computation effort, easier state initialization and unrequired a priori knowledge of thermodynamics.