Process monitoring using a combination of data driven techniques and model based data validation

Process monitoring is made difficult when measurements are subjected to errors, since pertinent information is hidden in the measurement noise. To address this issue, one can use model based data validation, or rely on statistical techniques to analyze large historical data sets (data mining). An industrial case study is presented here, where a model based approach (data validation) is compared to data driven techniques.