Dynamic data reconciliation based on node imbalance autocovariance functions

• Published in: Computers & chemical engineering 2012-08, Vol.43, p.81-90
• Main Authors: Vasebi, Amir, Poulin, Éric, Hodouin, Daniel
• Format: Article
• Language: English
• Subjects: Adaptative systems; Applied sciences; Autocovariance function; Computer science; control theory; systems; Control theory. Systems; Data reconciliation; Exact sciences and technology; Industrial metrology. Testing; Mass balance; Mechanical engineering. Machine design; Modelling and identification; Node imbalance; Observer; Process control. Computer integrated manufacturing
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2012.04.004

► A data reconciliation method based on a mass/energy balance sub-model is proposed. ► This observer uses the plant node imbalances autocovariance function to improve the precision of data. ► Observer performance is evaluated in terms of variance reduction and robustness against modeling errors. ► Results show a superior performance in comparison with classical sub-model based methods. ► This observer also reveals less performance degradation than the Kalman filter in presence of model uncertainties. To reduce impacts of measurement errors on plant variables, data reconciliation is widely applied in process industries. Reconciled measurements are used in applications such as performance monitoring, process control, or real-time optimization. However, precise estimation generally relies on accurate and detailed process models which could be difficult to build in practice. The trade-off between estimate precision and model complexity is a relevant challenge motivating the development of effective observers with limited modeling efforts. This paper proposes a data reconciliation method based on a simple mass and/or energy conservation sub-model that also considers the autocovariance function of plant node imbalances. The observer is applied to simulated benchmark plants and its performance is evaluated in terms of variance reduction and robustness against modeling errors. Results show a superior performance in comparison with classical sub-model based methods and reveal less performance degradation than the Kalman filter in presence of model uncertainties.