A Sensor Fault-Resilient Framework for Predictive Emission Monitoring Systems

The acronym PEMS stands for Predictive Emission Monitoring Systems and designates software analyzers able to provide a reliable and real-time estimate of emission concentrations by means of a data-driven model using real process measurements as input data. The model is built by resorting to measured process values along with true emission values from a portable Continuous Emission Monitoring System (CEMS) during the data collection period. Once on-line, PEMS performance in terms of emission prediction accuracy is strongly affected by the quality of the sensors input data. In order to ensure that the performance requirements imposed by the regulatory environmental agencies are met, a so-called Sensor Evaluation System (SES) must be included in the design of a new Robust PEMS (R-PEMS). The main goal of this paper is to introduce a technical solution that is capable of: i) detecting whether the sensors input data to the PEMS is faulty; ii) identifying which sensor is faulty; iii) whenever possible, substituting the faulty sensor input with a reconciled value with the objective of recovering the PEMS performance prior to the fault. Finally, we empirically verify the performances of the proposed SES using a real data set collected at a oil refinery.