| Recursive Nonlinear Estimation in Constrained Reactive Distillation Systems | ||
| Ravi Kumar Mandela, Chemical engineering department, Clarkson university, 8 clarkson ave, PO BOX 5707, Potsdam, NY 13699, Raghunathan Rengaswamy, Chemical Engineering, Clarkson University, Box 5705, Clarkson University, Potsdam, NY 13699-5705, Lakshmi Sridhar, Chemical Engineering, University of Puerto Rico, Mayaguez campus, P.O. Box 9046, Mayaguez, PR 00681 and Shankar Narasimhan, Department of Chemical Engineering, Indian Institute of Technology, Chennai, India Reactive Distillation (RD) with its growing applications has received considerable attention over the past several years. State estimation in reactive distillation is important as it is difficult to install sensors to measure the internal compositions for use in control. Good estimation techniques can enhance the reliability of the data used for process monitoring and feedback control and has a major impact on process efficiency and product quality. The inferential measurements that the estimation techniques produce can be successfully incorporated in feedback control of RD systems. In this paper, we will discuss the development of an optimization based constrained estimator for inferential measurement of the compositions in a RD system. The constraints are from the summation of mole fractions in all the trays of the RD column. There are several estimation procedures available in the literature. For a linearized model, the Kalman filter (KF) gives optimal estimates in the presence of measurement and state uncertainties. In the Extended Kalman Filter (EKF), a nonlinear model is used to develop a state estimator model. The advantages of KF and EKF lie in their recursive nature and the predictor-corrector form. However, these estimators cannot incorporate constraints. In our previous work [1], we demonstrated the incorporation of constraints within the recursive EKF form through the use of optimization techniques. The use of this approach in the estimation of compositions in RD columns will be discussed. We will also discuss the use of unscented transformation for estimation in RD systems. The unscented transformation addresses the nonlinearity in the propagation of the covariance matrices. We will use the approach presented in [2] for this estimation problem. The improvements in the accuracy of the estimates through this technique will be discussed. References [1] P. Vachhani, R. Rengaswamy, V. Gangwal, S. Narasimhan, Recursive estimation in constrained nonlinear dynamical systems, AICHE Journal, Vol. 51, pp. 946-959, 2005. [2] P. Vachhani, S. Narasimhan, R. Rengaswamy, Robust and reliable estimation via unscented recursive nonlinear dynamic data reconciliation, Journal of Process Control, Vol. 16, pp. 1075-1086, 2006.
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