Abstract: A method and apparatus for controlling a parameter associated with operation of a fossil fuel fired power generating unit. An optimizer determines optimal setpoint values for manipulated variables used to operate the power generating unit by accessing a model that predicts a parameter sensed by a plurality of sensors. The optimizer uses the model to minimize a cost valve of a cost function while observing at least one constraint associated with operation of the power generating unit.

Abstract: A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.

Abstract: A method and apparatus for optimizing air flow to a boiler of a power generating unit using advanced optimization, modeling, and control techniques. Air flow is optimized to maintain flame stability, minimize air pollution emissions, and improve efficiency. An optimizer determines optimal setpoint values for manipulated variables associated with the power generating unit by using a model to minimize the value of a cost function while observing at least one constraint associated with operation of the power generating unit.

Abstract: A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.

Abstract: A method and apparatus for optimizing the operation of a power generating plant using artificial intelligence techniques. One or more decisions D are determined for at least one consecutive time increment, where at least one of the decisions D is associated with a discrete variable for the operation of a power plant device in the power generating plant. In an illustrated embodiment, the power plant device is a soot cleaning device associated with a boiler.

Abstract: A system for determining performance characteristics of a combustion heating process. The system uses input parameters that are controllable by an operator or control system to determine a set of controllable loss components. The controllable loss components may be summed to produce an efficiency index value.

Abstract: A method and apparatus for optimizing air flow to a boiler of a power generating unit using advanced optimization, modeling, and control techniques. Air flow is optimized to maintain flame stability, minimize air pollution emissions, and improve efficiency.

Abstract: Non-linear model with disturbance rejection. A method for training a non linear model for predicting an output parameter of a system is disclosed that operates in an environment having associated therewith slow varying and unmeasurable disturbances. An input layer is provided having a plurality of inputs and an output layer is provided having at least one output for providing the output parameter. A data set of historical data taken over a time line at periodic intervals is generated for use in training the model. The model is operable to map the input layer through a stored representation to the output layer. Training of the model involves training the stored representation on the historical data set to provide rejection of the disturbances in the stored representation.