Abstract: A control system for adjusting total air flow or oxygen in flue gas for a fossil fired power generating or steam generating unit, that includes a plurality of sensors that supply data to a tunable controller adapted to sense total air flow and/or oxygen flow; with the sensors also supplying data relating to carbon monoxide (CO) and/or combustibles and/or loss of ignition (LOI) and/or carbon in ash (CIA), and where the tunable controller can set a desired target or target range for at least one of CO, combustibles, CIA, or LOI and adjust the total air flow and/or O2 via direct control or bias signals. The system can respond to discrete events, analog events and/or thresholds.

Abstract: A system and method to control of sootblowers in a fossil fueled power plant, in particular to plant applications systems using a graphical programming environment in combination with a set of rules to activate sootblowers. The system can be constrained by time limits and/or rule based time limits. Actual blower activation is typically based on the current status of key control variables in the process which alter the actual activation time within a constraints system. The system does not typically require knowledge or models of specific cleanliness relationships. The result is a system without sequences or queues that readily adapts to changing system conditions.

Abstract: A system and method to control of sootblowers in a fossil fueled power plant, in particular to plant applications systems using a graphical programming environment in combination with a set of rules to activate sootblowers. The system can be constrained by time limits and/or rule based time limits. Actual blower activation is typically based on the current status of key control variables in the process which alter the actual activation time within a constraints system. The system does not typically require knowledge or models of specific cleanliness relationships. The result is a system without sequences or queues that readily adapts to changing system conditions.

Abstract: A system and method to control of sootblowers in a fossil fueled power plant, in particular to plant applications systems using a graphical programming environment in combination with a set of rules to activate sootblowers. The system can be constrained by time limits and/or rule based time limits. Actual blower activation is typically based on the current status of key control variables in the process which alter the actual activation time within a constraints system. The system does not typically require knowledge or models of specific cleanliness relationships. The result is a system without sequences or queues that readily adapts to changing system conditions.

Abstract: A control system for adjusting total air flow or oxygen in flue gas for a fossil fired power generating or steam generating unit, that includes a plurality of sensors that supply data to a tunable controller adapted to sense total air flow and/or oxygen flow; with the sensors also supplying data relating to carbon monoxide (CO) and/or combustibles and/or loss of ignition (LOI) and/or carbon in ash (CIA), and where the tunable controller can set a desired target or target range for at least one of CO, combustibles, CIA, or LOI and adjust the total air flow and/or O2 via direct control or bias signals. The system can respond to discrete events, analog events and/or thresholds.

Abstract: A system and method to control of sootblowers in a fossil fueled power plant, in particular to plant applications systems using a graphical programming environment in combination with a set of rules to activate sootblowers. The system can be constrained by time limits and/or rule based time limits. Actual blower activation is typically based on the current status of key control variables in the process which alter the actual activation time within a constraints system. The system does not typically require knowledge or models of specific cleanliness relationships. The result is a system without sequences or queues that readily adapts to changing system conditions.

Abstract: A system and method for generating modeling software for processing control, used in power plant control, cement plants or other industrial control applications, using models such as expert systems, fuzzy logic, genetic optimization algorithms, and neural networks to convert sensor data into actionable data, information and/or diagnostics. The present invention includes a graphical programming environment, graphical programming tools, graphical user interface (GUI), visual feedback, real-time refresh, run-time object swap, logic standby (safety recovery), modeling and optimization to allows a user to create a control system for an industrial process, and that allows the user to change the process without any manual compile, assemble or load steps other than a save and refresh pushbutton.

Abstract: A system and method to control of sootblowers in a fossil fueled power plant, in particular to plant applications systems using a graphical programming environment in combination with a set of rules to activate sootblowers. The system can be constrained by time limits and/or rule based time limits. Actual blower activation is typically based on the current status of key control variables in the process which alter the actual activation time within a constraints system. The system does not typically require knowledge or models of specific cleanliness relationships. The result is a system without sequences or queues that readily adapts to changing system conditions.

Abstract: A system and method to control of sootblowers in a fossil fueled power plant, in particular to plant applications systems using a graphical programming environment in combination with a set of rules to activate sootblowers. The system can be constrained by time limits and/or rule based time limits. Actual blower activation is typically based on the current status of key control variables in the process which alter the actual activation time within a constraints system. The system does not typically require knowledge or models of specific cleanliness relationships. The result is a system without sequences or queues that readily adapts to changing system conditions.

Abstract: The present invention presents two new model types and a new method for evaluating a model used in the control application. These include a compound model, a hybrid model and a directional change coefficient model. The present invention allows the mixing of models with different inputs and outputs and the switching between these models based criteria for measuring optimization accuracy. The present invention allows switching between these models. The compound model is a model type that allows zooming in on the process to model parts of the data space with higher fidelity or resolution without loosing the capability to model the complete data space. The modeler does not loose any functionally over a regular neural network, but instead gains the ability to define the conditions when the model should use network weights best matched to the defined local conditions. The hybrid model is an extended version of a compound model.

Abstract: A system and method for generating modeling software for processing control, such as power plant control, cement plant or other industrial control application, using models such as expert systems, fuzzy logic, genetic optimization algorithms, and neural networks to convert sensor data into actionable data, information and/or diagnostics. The present invention includes a graphical programming environment, graphical programming tools, graphical user interface (GUI), visual feedback, real-time refresh, run-time object swap, logic standby (safety recovery), modeling and optimization to allows a user to create a control system for an industrial process, and that allows the user to change the process without any manual compile, assemble or load steps other than a save and refresh pushbutton.

Abstract: The present invention presents two new model types and a new method for evaluating a model used in the control application. These include a compound model, a hybrid model and a directional change coefficient model. The present invention allows the mixing of models with different inputs and outputs and the switching between these models based criteria for measuring optimization accuracy. The present invention allows switching between these models. The compound model is a model type that allows zooming in on the process to model parts of the data space with higher fidelity or resolution without loosing the capability to model the complete data space. The modeler does not loose any functionally over a regular neural network, but instead gains the ability to define the conditions when the model should use network weights best matched to the defined local conditions. The hybrid model is an extended version of a compound model.

Abstract: A closed loop control system for controlling generator cooling pressure, purity and dew point at power plants with hydrogen cooled generators to achieve optimum efficiency. The present invention uses feedback from plant status monitoring software to dynamically control parameters such as hydrogen pressure, purity and dew point to achieve optimum efficiency and provide necessary reserve capacity. The hydrogen pressure setpoint can be manipulated based on plant conditions such as output voltage, MVAR reserve capacity, likelihood of increased demand and other parameters. In general, an attempt is made to lower the pressure setpoint to achieve efficiency. Margin is built in to account for time lag in raising hydrogen pressure in the case of increased demand.

Abstract: A closed loop control system for controlling generator cooling pressure, purity and dew point at power plants with hydrogen cooled generators to achieve optimum efficiency. The present invention uses feedback from plant status monitoring software to dynamically control parameters such as hydrogen pressure, purity and dew point to achieve optimum efficiency and provide necessary reserve capacity. The hydrogen pressure setpoint can be manipulated based on plant conditions such as output voltage, MVAR reserve capacity, likelihood of increased demand and other parameters. In general, an attempt is made to lower the pressure setpoint to achieve efficiency. Margin is built in to account for time lag in raising hydrogen pressure in the case of increased demand.

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.