The increasing attention devoted to air quality by legislative, scientific, industrial and public sectors has led to the development of different control strategies for the emission level monitoring. In this scenario, Predictive Emission Monitoring System (PEMS) is able to predict emission concentrations thanks to empirical or first principles models fed by real-time process data provided by measurement sensors. It follows that PEMS consistency (and, crucially, its acceptance from regulations-enforcing agencies) strictly depends on input accuracy and that reliable Sensor Validation (SV) strategies are fundamental. In this work, the capability of two different SV techniques, Feed Forward Neural Networks and Locally Weighted Regression, is tested exploiting a commercial software package (ABB's IMP) on actual field data from a fluid catalytic cracking unit. The results showed that both techniques are suitable as complement to PEMS applications, but Locally Weighted Regression results are preferable for performance, economic and operating reasons.
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