Development of a neural network Predictive Emission Monitoring System for flue gas measurement

Abstract

Department of Environment in most countries is increasingly tightening clean Air regulation to mandate heavy industries to comply with stack emission limits. One of the latest measures is to enforce the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS) to report emission level online to DOE office. CEMS being hardware based analyzer is expensive and maintenance intensive and often unreliable. Therefore, the need for more economical, reliable and accurate software-based predictive techniques is a feasible equivalent alternative for regulatory compliance. This study has successfully developed a neural network software-based Predictive Emissions Monitoring System (PEMS) to accurately determine stack emission level which can correlate closely with hardware analyzer measurement.