What is the basis for the PEMS predictive engine? Is it a neural network or a first principle (physical correlation and gas theory)?
The engine is a unique statistical hybrid model. It is neither a neural network nor a first principle type, but it is an empirical method. All CMC PEMS run the same core module – the statistical hybrid engine. The model does not use a theoretical methodology such as a first principle formula nor does it require an iterative model development and testing regimen with experts onsite. There is no specialized staff required to build or maintain the emission model. Continuous Emissions Monitoring (CEM) or Reference Method (RM) test data is used to build the initial model. The empirical SmartCEM model utilizes historical data, paired emissions data and process data, to generate predictions in real-time. The predictions are derived directly from the historical training dataset using input parameters from the process that are available from the existing control system and are configured in the PEMS model. Unlike more complicated empirical systems such as neural network and first principle form
