Abstract:
An oxy-combustion boiler unit is disclosed which includes a furnace for combusting fuel and for emitting flue gas resulting from combustion. The furnace has first, second and third combustion zones, and an air separation unit for separating oxygen gas from air and providing a first portion of the separated oxygen to a first oxidant flow, a second portion to a second oxidant flow, and a third portion of the separated oxygen gas to the first, second, and third zones of the furnace. A controller can cause the separated oxygen gas to be distributed so that the first and second oxygen flows have a desired oxygen content, and so that the first, second, and third zones of the furnace receive a desired amount of oxygen based on a combustion zone stoichiometry control.
Abstract:
A control system for optimizing a chemical loop system includes one or more sensors for measuring one or more parameters in a chemical loop. The sensors are disposed on or in a conduit positioned in the chemical loop. The sensors generate one or more data signals representative of an amount of solids in the conduit. The control system includes a data acquisition system in communication with the sensors and a controller in communication with the data acquisition system. The data acquisition system receives the data signals and the controller generates the control signals. The controller is in communication with one or more valves positioned in the chemical loop. The valves are configured to regulate a flow of the solids through the chemical loop.
Abstract:
A control system for optimizing a chemical looping (“CL”) plant includes a reduced order mathematical model (“ROM”) that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to a CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.
Abstract:
An electricity production system configured to operate in accordance with a method of operating an electricity production system that at least includes the steps of: determining an oxygen distribution between oxygen gas to be separated by an air separation unit (“ASU”) and oxygen gas stored in a storage tank of the ASU to be fed to the boiler unit, determining a carbon capture value for a gas processing unit, determining a power consumption value for the gas processing unit and the ASU, determining a total power demand value based on the power consumption value of the gas processing unit and the ASU, and on a determined electricity demand, and controlling the boiler unit, the turbine, the ASU, and the gas processing unit based on the determined total power demand along with correcting signals generated from a coordinated Model Predictive Control.
Abstract:
A control system for optimizing a chemical looping (“CL”) plant includes a reduced order mathematical model (“ROM”) that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to a CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.
Abstract:
An automatic tuning control system and method for controlling air pollution control systems such as a dry flue gas desulfurization system is described. The automatic tuning control system includes one or more PID controls and one or more supervisory MPC controller layers. The supervisory MPC controller layers are operable for control of an air pollution control system and operable for automatic tuning of the air pollution control systems using particle swarm optimization through simulation using one or more dynamic models, and through control system tuning of each of the PID controls, MPC controller layers and an integrated MPC/PID control design.
Abstract:
A control system for optimizing a chemical loop system includes one or more sensors for measuring one or more parameters in a chemical loop. The sensors are disposed on or in a conduit positioned in the chemical loop. The sensors generate one or more data signals representative of an amount of solids in the conduit. The control system includes a data acquisition system in communication with the sensors and a controller in communication with the data acquisition system. The data acquisition system receives the data signals and the controller generates the control signals. The controller is in communication with one or more valves positioned in the chemical loop. The valves are configured to regulate a flow of the solids through the chemical loop.
Abstract:
An automatic tuning control system and method for controlling air pollution control systems such as a dry flue gas desulfurization system is described. The automatic tuning control system includes one or more PID controls and one or more supervisory MPC controller layers. The supervisory MPC controller layers are operable for control of an air pollution control system and operable for automatic tuning of the air pollution control systems using particle swarm optimization through simulation using one or more dynamic models, and through control system tuning of each of the PID controls, MPC controller layers and an integrated MPC/PID control design.
Abstract:
Method and system for adjusting a measured reheat outlet steam temperature (“RPV”) to approximate a reheat outlet steam temperature setpoint (“RSP”) in a boiler. An RPV is compared to an RSP. If the RPV is less than the RSP and a position of a fuel nozzle tilt (“TILTPV”) is below a high limit of the fuel nozzle tilt (“TILTHIGH”), the TILTPV is increased while a flow rate of a secondary flue gas recirculation (“SFGRPV”) is kept constant. If the RPV is less than the RSP and the TILTPV is at the TILTHIGH, the SFGRPV is increased. If the RPV is greater than the RSP and the SFGRPV is greater than a low limit of flow rate of the SFGR (“SFGRLOW”), the SFGRPV is decreased, while the TILTPV is kept constant. If the RPV is greater than the RSP and the SFGRPV is at the SFGRLOW, the TILTPV is decreased.