A Statistical Hybrid Predictive Emissions Monitoring System (PEMS) That Accurately Measures Pollutant Emission Ratesfrom Gas Turbines, Boilers, Heaters, Ethanol Plants, Petroleum Refining Operations, and Other Continuous Processes

摘要

Emissions monitoring for large industrial facilities can be a daunting task if continuous emissions monitoring systems (CEMS) are used exclusively. The compliance CEMS typically includes a heated extractive sampling probe, heated sampling line, sample conditioning system, pumps, moisture removal equipment, and dryers along with the gas analyzers, programmable logic controllers, and data acquisition systems that require significant onsite maintenance or outsourced support. A predictive emissions monitoring system (PEMS), on the other hand, utilizes the existing sensors and input information from the process control system. A deployed PEMS does not require the additional equipment of a CEMS or supplemental instrumentation, or extensive quality assurance. Typically the instrumentation installed during plant startup with typical sensor maintenance and replacement performed as recommend by the manufacturer of the control system and instrumentation package. This presentation contains a discussion of the various types of models that can be used for predicting emissions and the theory behind them. In addition, we will present some typical statistical hybrid PEMS systems that have been certified in the last couple of years in the United States. The methods used to deploy predictive emissions monitoring systems for compliance with U.S. pollutant monitoring regulations are also described.