An accurate Predictive Emissions Monitoring System (PEMS) for an ethylene furnace

Abstract

AbstractThe Predictive Emissions Monitoring System (PEMS) has been recently proposed as an alternative to hardware continuous emissions monitors (CEM). PEMS has two distinct advantages over hardware CEM: much lower costs to install and maintain, and ability to provide information on emissions under various conditions. Using a state‐of‐the‐art computer program, an accurate PEMS was developed for an ethylene furnace at Exxon Chemical Company's Baytown Olefins Plant, Baytown, Texas. First, a test plan was carefully designed to collect furnace process and stack emissions data over the total operating range of the furnace. The emissions data (NOx, O2 and CO) were collected and measured by an independent company for 5 days. The related process data were recorded in the DCS system during the same 5‐day period. A dataset with some 7,000 patterns of related process and emissions data was generated. The PEMS system contains 2 parts: emissions prediction and sensor validation models. The main purpose of the sensor validation model is to validate the input sensor's raw data and reconstruct new sensor values, if necessary, before they are used in the prediction model. Its usage provides a high degree of confidence for the PEMS system. Two separate emissions prediction models were built, one for NOx and O2, and the other for CO. Both final models have the same 9 input sensors. A PEMS relative accuracy test audit (RATA) was performed by an independent company to test the PEMS system under the three firing rate conditions (low, medium, and high). Test results indicate that the PEMS system satisfied both the relative accuracy (RA) and the stringent statistical test requirements including the F‐test, t‐test and r‐correlation.