The present embodiments provide an incinerator which includes a system for reducing NOx and CO emissions. A computational fluid dynamics module is configured to generate a plurality of models related to a plurality of incinerator parameters. A programmable logic controller dynamically maintains a plurality of set points. Further, the programmable logic controller receives a plurality of output signals from a plurality of sensors and compares the plurality of output signals with the plurality of set points. The programmable logic controller is further to affect an amount of above-fire combustion air, an amount of under-fire combustion air, and an amount of above-fire and under-fire flue gas recirculation to reduce NOx emissions produced by the incinerator.

A system for controlling a boiler apparatus in a power plant to combust under optimized conditions, and a method for optimizing combustion of the boiler apparatus using the same are provided. The boiler control system may include a modeler configured to create a boiler combustion model, an optimizer configured to receive the boiler combustion model from the modeler and perform the combustion optimization operation for the boiler using the boiler combustion model to calculate an optimum control value, and an output controller configured to receive the optimum control value from the optimizer, and control an operation of the boiler by reflecting the optimum control value to a boiler control logic.

An apparatus for combustion optimization is provided. The apparatus for combustion optimization includes a management layer configured to collect currently measured real-time data for boiler combustion, and to determine whether to perform combustion optimization and whether to tune a combustion model and a combustion controller by analyzing the collected real-time data, a data layer configured to derive learning data necessary for designing the combustion model and the combustion controller from the real-time data and previously measured past data for the boiler combustion, a model layer configured to generate the combustion model and the combustion controller through the learning data, and an optimal layer configured to calculate a target value for the combustion optimization by using the combustion model and the combustion controller, and to output a control signal according to the calculated target value.

A method for controlling a gas turbine, having a measurement step, a prediction step which is carried out after the measurement step, and a control step which is carried out after the prediction step. In the measurement step, a state variable of a combustion within a gas turbine is measured. In the prediction step, a future combustion dynamic is predicted using the measured state variable. In the control step, a control signal is output using the prediction of the future combustion dynamic.

Real-time dynamic optimization of a process model in an online model-based process control computing environment. A mixed integer nonlinear programming (MINLP) solver utilizes grouping of first-principle model units to implement constraints of the underlying process. A group identifier parameter and a group complement parameter enable the grouping behavior through association with the first-principles model units.

The invention discloses a system for effecting a control strategy in a reactive flow field of a turbulent flow system. The system is configured to analyze flow field properties such as velocity, heat release rate, or mixture fraction of a device during the onset of the oscillatory instability using measures from complex network theory such as betweenness centrality, degree, or closeness. The system identifies critical regions in the flow field responsible for the oscillatory instability. Further, the system also identifies optimal control strategies to avoid the onset of oscillatory instabilities by analyzing the relative strength of various network parameters and thereby controlling oscillatory instabilities which are detrimental to the fluid dynamic system. The disclosed method and system provide for optimization of control of oscillatory instabilities in fluid dynamic systems.

A method for operating a combustion chamber is provided. The method includes obtaining a carbon monoxide reading at an exit of the combustion chamber via a carbon monoxide sensor, and deriving an oxygen set point trim based at least in part on the carbon monoxide reading and a carbon monoxide set point via a controller. The method further includes determining a stability status of the combustion chamber via a combustion stability sensor, and adjusting an oxygen set point of the combustion chamber with the oxygen set point trim based at least in part on the stability status via the controller. The oxygen set point defines a desired oxygen level at the exit of the combustion chamber.

A system for generating a base point database for use by a full authority digital engine control (FADEC) to control a gas turbine engine includes a memory configured to store a model of the gas turbine engine. The system also includes a model generation processor coupled to the memory and designed to perform an initial simulation of the gas turbine engine using the model to determine ranges of sensitivity of desired engine parameters throughout an operating envelope, divide the operating envelope into a multiple regions based on the ranges of sensitivity of the desired engine parameters, select multiple combinations of base points within each of the multiple regions, perform an additional simulation of the gas turbine engine to determine an accuracy of interpolation between each of the multiple combinations of base points for each of the multiple regions, and select final base points from the multiple combinations of base points based on the accuracy.

Systems and methods for improved gas turbine engine performance are disclosed. The method can include receiving an error function for a wide range of fuels. The error function can provide lower heating value (LHV) corrections over the wide range of fuels. The method can include receiving gas turbine engine operation data for a first period of run time on the gas turbine from one or more sensors of the gas turbine engine. The engine operation data can include a performance data points. The method can include determining an optimum LHV based on the engine operation data for the first period of run time and the error function. The method can then include adjusting fuel consumption of the gas turbine engine based on the optimum LHV.

Real-time dynamic optimization of a process model in an online model-based process control computing environment. A mixed integer nonlinear programming (MINLP) solver utilizes grouping of first-principle model units to implement constraints of the underlying process. A group identifier parameter and a group complement parameter enable the grouping behavior through association with the first-principles model units.