The statistical hybrid approach is a unique empirical predictive system that requires only a fixed sample of paired process and emissions data. A statistical hybrid PEMS has the following features:
- Robust model that is accurate across the full load range of the unit
- Valid for normal operations and during transitional states such as startup and shutdown
- Equivalent accuracy as a CEMS with superior reliability – tied to the plant control system
- Flexibility to be implemented using existing process instrumentation and data interfaces
- Certified as an alternative system under U.S. EPA for continuous compliance monitoring
- Can be assessed using quality control procedures required under programs of the U.S. EPA
- Can be developed and retrained by non-technical onsite staff or consultants
- Can be tested against EPA reference methods
- Has been demonstrated under 40 CFR Part 60, Performance Specification 16
- Has been demonstrated under 40 CFR Part 75, Subpart E.
The statistical hybrid model exploits the existing statistical relationships of the historical training dataset depending on the input parameters available and how they are represented in the empirical data. The historical dataset is fixed prior to certification when used in compliance monitoring. This allows the PEMS to calculate a model envelope that defines the operating conditions represented in the historical training dataset. Alarms can be configured to detect when the process is operated outside the model envelope. All normal operating conditions including startups, shutdowns, and transitional states can be tested such that envelope excursions are minimized. This type of historical training dataset (containing all normal operating conditions) is deemed to be ‘robust’. Robust statistical hybrid models produce minimal monitor downtime over long periods.