General Electric Company

Neuco, Inc.

Fred Francis Pickard

Stephen William Piche

John Robert James

John McDermott

Neel J Parikh

Yan Liu

Nurali Virani

Robert Nicholas Petro

A method for modeling a system that includes a disturbance rejection model configured for modeling an operation of the system so to generate a predicted value for a system output. The disturbance rejection model having a network for mapping system inputs to the system output, and input-output pairings, each representing a unique pairing of one of the system inputs with the system output. The method may include the steps of: calculating a confidence metric for a selected input-output pairing of the disturbance rejection model; and recommending a modification be made to the disturbance rejection model based upon the confidence metric calculated for the selected one of the input-output pairing. The confidence metric may indicate a probability that a predicted sign of a gain in the system output made by the disturbance rejection model is correct when the system input of the selected input-output pairing is varied.

System and method for process modeling and control using disturbance rejection models

A system that includes: a gas turbine having a combustion system; a control system operably connected to the gas turbine for controlling an operation thereof; and a combustion auto-tuner, which is communicatively linked to the control system, that includes an optimization system having an empirical model of the combustion system and an optimizer; sensors configured to measure the inputs and outputs of the combustion system; a hardware processor; and machine-readable storage medium on which is stored instructions that cause the hardware processor to execute a tuning process for tuning the operation of the combustion system. The tuning process includes the steps of: receiving current measurements from the sensors for the inputs and outputs; given the current measurements received from the sensors, using the optimization system to calculate an optimized control solution for the combustion system; and communicating the optimized control solution to the control system.

Control and tuning of gas turbine combustion

A method for modeling an operation of a system that may include a disturbance rejection model that is configured to generate a predicted value for a system output at a future time. The disturbance rejection model may include a neural network for mapping system inputs to the system output. The method may include the steps of: training the disturbance rejection model per a training dataset; and calculating a confidence metric for the disturbance rejection model. The confidence metric is configured to indicate a probability that a predicted sign of a gain in the system output at the future time made by the disturbance rejection model is correct.

System modeling, control and optimization

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

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Norfolk, MA, United States of America

Fred Francis Pickard