Abstract
Data reconciliation and gross error detection plays an important role in the process control as measured signals are often contaminated by measurement errors. The simultaneous reconciliation of flow rate and composition measurements results in nonlinear constraints. A Correntropy based data reconciliation and gross error detection method is proposed for the type of bilinear systems with specific to the flow rate and measurement composition. Correntropy is a robust estimator and is effective in reducing the effect of gross errors. Moreover, unmeasured variables can be effectively identified. The Correntropy based data reconciliation and gross error detection method is applied to the mineral processing plant and air separation process for application. Results demonstrate that the proposed method can overcome the weakness of being sensitive to the effect of gross errors.
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