Composition and Relative Volatility Estimation in Ethanol-Water Distillation Process through Quadratic Program Based Constrained Kalman Estimation Paradigm

Document Type : Research Article


1 Department of Electronics and Instrumentation Engineering, VNR VJIET, Hyderabad, INDIA

2 Department of Instrumentation Engineering, MIT, Anna University, Chennai, INDIA


Kalman filter is a classic iterative estimation technique widely used to estimate states and parameters of linear dynamic systems with white Gaussian measurement and process noises. However, if the measurement noises are predominant, resulting in a poor signal-to-noise ratio, the estimator fails to provide allowable error covariance and optimal state estimation. In such circumstances, to enhance the estimation accuracy, measurement constraints need to be incorporated into the estimation routine. Through this work, a Quadratic Program-based Constrained Kalman Estimation (QP-CKE) estimation sequence is proposed and developed to handle the additive measurement noise constraints. This is implemented by incorporating a deconvoluted quadratic program with a modified Kalman estimation paradigm to handle the constraint cost function. Composition estimation in a laboratory binary distillation process for ethanol-water mixture separation under steady-state operating conditions is used as a case study. Noise augmented Two Input Two Output (TITO) linearized dynamic model of the process is established by inferring Gaussian distributed tray temperature measurements and mixture vapor-liquid equilibrium data. The performance of this new estimator is tested for top and bottom composition estimation for step input excitation for reflux rate and reboiler power inputs under feed flow disturbances and the results are compared with that of conventional Kalman and Q adaptive Kalman estimators. The performance of the proposed estimator proves to be competent with reasonable computational speed and improved estimation accuracy. Also, relative volatility and vapor-liquid equilibrium trends are derived from estimated tray composition data, and results are found in good relevance with that of the experimental data.


Main Subjects

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