The present invention relates to an emission monitoring system used to monitor emissions produced by a source of emissions. More particularly, the present invention relates to an emission monitoring system having a continuous emission monitoring system and a predictive emission monitoring system.
Recent stringent regulations adopted for the purposes of improving air quality by control of the emission of pollutants from combustion sources such as generating plants and factories have created an urgent necessity for effective stack gas monitoring equipment. Two basic systems, continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems have emerged to allow those emitting pollutants to comply with local, state and federal regulations. The CEM system continuously monitors the emissions of a stack, for example, by extracting gas directly from the stack, measuring the gas directly in the stack with an optical sensor or other suitable sensor, or by using remote sensors that detect emission concentrations by projecting light out to the stack or by sensing the light radiating from "hot" molecules emitted from the stack. Gas analyzers are provided to analyze the gas into each of its constituent parts. Output signals from the gas analyzer or the remote sensors are provided to a data acquisition unit that records values proportional to the input signals received. The recorded values are stored and retrieved as necessary to comply with reporting regulations. In addition, status signals are provided by the gas analyzer indicative of the operational status of each device.
Recently, federal regulations have been amended to allow the use of PEM systems in order to comply with reporting requirements. Likewise, at the state and local levels, legislation has been enacted or is pending that permits the use of PEM systems. Generally, a PEM system models the source of emissions that generates the emissions and predicts the quantity of emissions that are produced given the operating state of the process. Commonly, regression (linear and nonlinear) techniques are used in such modeling. The PEM system is "trained" by monitoring multiple inputs such as pressures, temperatures, flow rates, etc., and one or more output parameters such as NO.sub.x, CO, O.sub.2, etc. After training, in normal operation, the PEM system monitors only the multiple inputs and calculates estimated output parameter values that closely match the actual pollutant levels.
U.S. Pat. No. 5,386,373 describes a PEM system that predicts the output emissions of a plant. A stored representation of the plant is provided in association with a virtual sensor predictive network to provide as an output, a prediction of the actual pollutant level output of the plant. The stored representation in the virtual sensor predictive network is learned from measuring pollutant levels and associated values for controls of the plant and values of corresponding sensors measuring operating parameters of the plant. In operation, a CEM system is temporarily connected to the plant to monitor the level of pollutants emitted from the plant. The pollutant levels are then correlated with the plant controls and the sensor outputs to form a training data base used to train the virtual sensor predictive network. Once trained, the CEM system can be removed with output emissions predicted by the virtual sensor predictive network. When necessary, a new pollutant sensor or a portable pollutant sensor is periodically utilized to check the operation of the virtual sensor network to ensure that it is operating correctly and that no parameters of the plant have changed such that the prediction is now incorrect or the model no longer represents the plant.