Abstract: 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.

Abstract: 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.

Abstract: A controller directs the operation of an air pollution control (APC) system performing a process, having one or more controllable operating parameters, to control emissions of a pollutant. An interface receives financial data associated with the operation of the APC system. A control processor determines a target set point of each of at least one of the one or more controllable operating parameters that will maximize profits or minimize losses from the operation of the APC system, based on the received financial data. The control processor also directs control of each of the at least one controllable operating parameter based on the determined target set point for that parameter.

Abstract: 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.

Abstract: 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.

Abstract: 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. The analyzer also includes either a neural network process model or a non-neural network process model.

Abstract: 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.

Abstract: 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.

Abstract: A parameter value estimator is provided for a process performed primarily to control emission of a particular non-particulate pollutant, such as NOx and SO2, 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.

Abstract: 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.

Abstract: 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 TPLAAV. 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 TPLAAV. 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.