Technical publication: Assessment and testing of SV algorithms for Emission Monitoring applications
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.
Technical publication: 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.
Article - Predictive emission monitoring systems | The power of software analyzers
Gregorio Ciarlo, Federico Callero and Maurizio Simonelli, ABB SpA, Italy, discuss the application of predictive emission monitoring systems in applications when fuel characteristics are variable or in a complex process plant
Technical publication: Fulfilling Evolving End-Users Expectations for Site-Wide Emission Monitoring: the Role of PEMS
Environmental regulation evolution in mature countries and the expected leapfrog in emerging countries guidelines are contributing to substantial changes in the emission monitoring techniques. Predictive Emission Monitoring Systems (PEMS) have been around for almost 20 years starting as a niche technology for fond engineers and practitioners and slowly gaining acceptance as an alternative to HW-based Continuous Emission Monitoring Systems (CEMS). Assisted and reassured by rigorous regulations and procedure,
issued and revised by the main environmental Authorities, customers and end-users have progressively learned to consider and use them as a smart option for a number of stationary emission sources rather than a one-fit-all solution.