Predictive emission monitors (PEMS) for NOx generation in process heaters

Worldwide, there is an ever-increasing interest and concern about the destr uctive affects of air pollution on the ecological system. The growing aware ness of these effects has revealed the need to take adequate measures to mo nitor and control the emissions of air pollutants. Process heaters contribu te a major percent to the industrially formed emissions, particularly of NO , and CO. The conventional approach was to monitor these emissions using on -line analyzers on a regular basis called continuous emission monitors (CEM S). Predictive emission monitors (PEMS) have been proven to be as accurate as the GEMS and are in fact more economical from the cost and maintenance p oint of view. This paper presents a PEMS developed based on the emission da ta collected on a 5 MMkcal h(-1) pilot plant furnace. The NOx kinetic param eters were tuned using a heuristic optimizer genetic algorithm (GA) which m inimizes the least squared error between the model and experimental data. T he model thus tuned could be used to predict O-2, NOx, CO, CO2 emissions wi th reasonable accuracy as also for model predictive control of these emissi ons. (C) 2000 Elsevier Science Ltd. All rights reserved.