Alstom Technology Limited

General Electric Technology Gmbh

Scott A Boyden

Stephen William Piche

Anders Karlsson

David P VanderVeen

A device and method for controlling the rapping of at least one collecting electrode plate () of an electrostatic precipitator () is provided. The device controls such rapping by applying, by means of a power source (), a voltage between at least one collecting electrode plate () and at least one discharge electrode (). The sparking rate between the at least one collecting electrode plate () and at least one discharge electrode () is then measured, with the rapping of the at least one collecting electrode plate () controlled using the measured present sparking rate.

Method of estimating the dust load of an ESP, and a method and a device of controlling the rapping of an ESP

A method and a system for controlling dust particle emissions from an electrostatic precipitator (), which has a first and a second bus-section (), are provided. Dust particle emissions are controlled by observing that a rapping event of the first bus-section () is about to be initiated, verifying, before allowing the rapping event of the first bus-section () to be initiated, that the second bus-section () is ready to receive the dust particles to be released during the rapping event of the first bus-section (), and then initiating, after verification, the rapping event of the first bus-section ().

Method of controlling the order of rapping the collecting electrode plates of an ESP

A virtual analyzer is provided to estimate either an attribute of a reactant applied during performance of, or an amount of a reactant exhausted by, a process having multiple process parameters (MPPs) that is performed to control an amount of a pollutant emitted into the air. The virtual analyzer includes an interface which receives signals corresponding to attributes of the MPPs. If the process is a wet flue gas desulfurization (WFGD) process, the signals include a signal corresponding to a measured pH level of the applied reactant. If the process is a selective catalytic reduction (SCR) process, the signals include a signal corresponding to a measured amount of the reactant exhausted by the process.

APC process parameter estimation

A method of controlling the feeding of a reducing agent, such as urea or ammonia, to a selective catalytic reduction reactor, which is operative for removing NOx from a process gas of a process plant, comprising the steps of estimating a present value of at least one predetermined parameter which is indicative of the amount of NOx that needs to be removed from said process gas in the selective catalytic reduction reactor. The rate of increase of said at least one predetermined parameter is then estimated. When said estimated rate of increase of said at least one predetermined parameter is found to be positive, the amount of said reducing agent being supplied is increased to a higher value than would be the case if said at least one predetermined parameter were to be kept constant at the estimated present value thereof.

Method of controlling the operation of a selective catalytic reduction plant

A parameter value estimator is provided for a process performed primarily to control emission of a particular non-particulate pollutant, such as NOand SO, into the air. The process has multiple process parameters (MPPs) including a parameter representing an amount of the particular non-particulate pollutant emitted. The parameter value estimator includes either a neural network process model or a non-neural network process model. In either case the model represents a relationship between one of the MPPs, other than the parameter representing the amount of the emitted particular non-particulate pollutant, and one or more other of the MPPs. Also included is a processor configured with the logic, e.g. programmed software, to estimate a value of the one MPP based on a value of each of the one or more other MPPs and the one model.

Process parameter estimation in controlling emission of a non-particulate pollutant into the air

An economic parameter estimator is provided for a process that has multiple process parameters (MPPs) and is performed to control emission of a pollutant into the air. The performance of the process is associated with one or more economic factors (EFs). The estimator includes either a neural network process model or a non-neural network process model. In either case, the model represents a relationship between one or more of the MPPs and an economic parameter. Also included is a processor configured with logic, e.g. programmed software, to estimate a monetary value of the economic parameter based on a value of each of the one or more MPPs, a value of each of at least one of the one or more EFs, and the one model.

Estimating an economic parameter related to a process for controlling emission of a pollutant into the air

A controller is provided for directing control of a process performed to control an amount of a pollutant emitted into the air. The process has multiple process parameters (MPPs) The controller includes either a neural network process model or a non-neural network process model. Whichever type model is included, it will represent a relationship between one of the MPPs and other of the MPPs. The controller also includes a control processor having the logic to determine the validity of a measured value of the one MPP based on the one model. The control processor directs control of the process in accordance with the measured value of the one MPP only if the measured value of the one MPP is determined to be valid. On the other hand, if the measured value is determined to be invalid, the control processor may direct control of the process in accordance with an estimated value of the one MPP.

APC process control when process parameters are inaccurately measured

A controller directs a process primarily performed to control emission of a particular pollutant into the air. The process has multiple process parameters (MPPs), including a parameter representing an amount of the particular pollutant. The controller includes either a neural network process model or a non-neural network process model. In either case, the model represents a relationship between a first of the MPPs and one or more of the other MPPs. The one or more other MPPs include a second of the MPPs which is other than the parameter representing the amount of the emitted particular pollutant. Also included is a processor configured with logic to estimate a value of the second MPP, and to direct control of the first MPP based on the estimated value of the second MPP and the model.

Estimated parameter based control of a process for controlling emission of a pollutant into the air

A controller for directing operation of an air pollution control (APC) system, requires a consumable to perform a process to control emissions of a pollutant. The process has multiple process parameters (MPPs). One or more of the MPPs is a controllable process parameters (CTPPs) and one of the MPPs is an amount of the pollutant (AOP) emitted by the system. A defined AOP value (AOPV) represents a limit on an actual value (AV) of the emitted AOP. An interface receives a value corresponding to a unit cost of the consumable. A control processor determines the cost of operating the system based on the received value corresponding to the unit cost of the consumable, and directs control of at least one of CTPPs based on the current value of that CTPP and the determined operating cost.

Cost based control of air pollution control

A controller for directing operation of an air pollution control system performing a process to control emissions of a pollutant has multiple process parameters (MPPs). One or more of the MPPs is a controllable process parameter (CTPP) and one of the MPPs is an amount of the pollutant (AOP) emitted by the system. A defined AOP value (AOPV) represents an objective or limit on an actual value (AV) of the emitted AOP. The controller includes either a neural network process model or a non-neural network process model representing a relationship between each CTPP and the emitted AOP. A control processor has the logic to predict, based on the model, how changes to the current value of each CTPP will affect a future AV of emitted AOP, to select one of the changes in one CTPP based on the predicted affect of that change and on the AOPV, and to direct control of the one CTPP in accordance with the selected change for that CTPP.

Model predictive control of air pollution control processes

A controller directs operation of an air pollution control (APC) system performing a process to control emissions of a pollutant. The process has multiple process parameters (MPPs), one or more of the MPPs being a controllable process parameters (CTPPs) and one of the MPPs being an amount of the pollutant (AOP) emitted by the system. A user input device identifies an optimization objective. A control processor determines a set point for at least one of the one or more CTPPs based on the current values of the MPPs and the identified optimization objective, and directs control of one of the at least one CTPP based on the determined set point for that CTPP.

Optimized air pollution control

A controller directs the operation of an air pollution control (APC) system having one or more controllable operating parameters and a defined operating limit representing a regulatory limit on an allowed amount of pollutant to be emitted by the APC system. An interface receives data representing a value of a regulatory credit available for emitting less of the pollutant than the regulatory limit on the allowed amount of pollutant. A control processor (i) determines a target set point for each of at least one of the one or more controllable operating parameters, which will maximize the regulatory credits earned, based on the received data and (ii) to directs control of each of the at least one controllable operating parameter based on the determined target set point for that parameter.

Maximizing regulatory credits in controlling air pollution

At least one of the multiple process parameters (MPPs) is a controllable process parameter (CTPP) and one is a targeted process parameter (TPP). The process also has a defined target limit (DTV) representing a first limit on an actual average value (AAV) of the TPP. A first logical controller predicts future average values (FAVs) of the TPP based on the AAVs of the TPP over a first prior time period and the DTV. A second logical controller establishes a further target limit (FTV) representing a second limit on the AAV of the TPP based on one or more of the predicted FAVs, and also determines a target set point for each CTPP based on the AAVs of the TPP over a prior time period and the FTV. The second logical controller directs control of each CTPP in accordance with the determined target set point.

Cascaded control of an average value of a process parameter to a desired value

A controller directs performance of a process having multiple process parameters (MPPs), including a controllable process parameter (CTPP), a targeted process parameter (TPP), a defined target value (DTV) representing a limit on an actual average value (AAV) of the TPP over a defined moving time period of length TPL. A storage device stores historical data representing the AVs of the TPP at various times over a prior time period (PTP) having a length of at least TPL. A processor predicts future average values (FAVs) of the TPP over a future time period (FTP) based on the stored historical data and the current values of the MPPs. The processor also determines a target set point for each CTPP based on the predicted FAVs, the current values of the MPPs and the DTV, and directs control of each CTPP in accordance with the determined target set point for that CTPP.

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Knoxville, TN, United States of America

Scott A Boyden