APC process parameter estimation
First Claim
1. A virtual analyzer for estimating 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, comprising:
- an interface configured to receive signals corresponding to attributes of the MPPs, including (i) if the process is a wet flue gas desulfurization (WFGD) process, a signal corresponding to a measured pH level of the applied reactant, and (ii) if the process is a selective catalytic reduction (SCR) process, a signal corresponding to a measured amount of the reactant exhausted by the process;
one of a neural network process model and a non-neural network process model, the one model representing a relationship between either (i) if the process is a WFGD process, the pH level of the applied reactant and the attributes of the MPPs other than the measure pH level of the applied reactant or (ii) if the process is the SCR process, the amount of the reactant exhausted by the process and the attributes of the MPPs other than the measured amount of the reactant exhausted by the process; and
a processor configured with logic (i) if the process is the WFGD process, to estimate a pH level of the applied reactant based on the attributes of the MPPs, other than the measured pH level of the applied reactant, that correspond to the received signals and on the one model, and (ii) if the process is the SCR process, to estimate an amount of the reactant exhausted by the process based on the attributes of the MPPs, other than the measured amount of the reactant exhausted by the process, that correspond to the received signals and on the one model.
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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. Whichever type of model is utilize, if the process is a WFGD process, the model represents a relationship between the pH level of the applied reactant and the attributes of the MPPs other than the measure pH level of the applied reactant. On the other hand, if the process is the SCR process, the model represents a relationship between the amount of the reactant exhausted by the process and the attributes of the MPPs other than the measured amount of the reactant exhausted by the process. The analyzer also includes a processor. If the process is the WFGD process, the processor has the logic to estimate a pH level of the applied reactant based on the attributes of the MPPs, other than the measured pH level of the applied reactant, that correspond to the received signals and on the one model. On the other hand, if the process is the SCR process, the processor has the logic to estimate an amount of the reactant exhausted by the process based on the attributes of the MPPs, other than the measured amount of the reactant exhausted by the process, that correspond to the received signals and on the one model.
50 Citations
18 Claims
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1. A virtual analyzer for estimating 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, comprising:
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an interface configured to receive signals corresponding to attributes of the MPPs, including (i) if the process is a wet flue gas desulfurization (WFGD) process, a signal corresponding to a measured pH level of the applied reactant, and (ii) if the process is a selective catalytic reduction (SCR) process, a signal corresponding to a measured amount of the reactant exhausted by the process;
one of a neural network process model and a non-neural network process model, the one model representing a relationship between either (i) if the process is a WFGD process, the pH level of the applied reactant and the attributes of the MPPs other than the measure pH level of the applied reactant or (ii) if the process is the SCR process, the amount of the reactant exhausted by the process and the attributes of the MPPs other than the measured amount of the reactant exhausted by the process; and
a processor configured with logic (i) if the process is the WFGD process, to estimate a pH level of the applied reactant based on the attributes of the MPPs, other than the measured pH level of the applied reactant, that correspond to the received signals and on the one model, and (ii) if the process is the SCR process, to estimate an amount of the reactant exhausted by the process based on the attributes of the MPPs, other than the measured amount of the reactant exhausted by the process, that correspond to the received signals and on the one model. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for estimating 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, comprising:
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receiving signals corresponding to attributes of the MPPs, including (i) if the process is a wet flue gas desulfurization (WFGD) process, a signal corresponding to a measured pH level of the applied reactant, and (ii) if the process is a selective catalytic reduction (SCR) process, a signal corresponding to a measured amount of the reactant exhausted by the process; and
estimating (i) if the process is the WFGD process, a pH level of the applied reactant based on the attributes of the MPPs, other than the measured pH level of the applied reactant, that correspond to the received signals, and on one of a neural network process model and a non-neural network process model that in either case represents a relationship between the pH level of the applied reactant and the attributes of the MPPs other than the measure pH level of the applied reactant model, or (ii) if the process is the SCR process, an amount of the reactant exhausted by the process based on the attributes of the MPPs, other than the measured amount of the reactant exhausted by the process, that correspond to the received signals and on one of a neural network process model and a non-neural network process model that in either case represents a relationship between the amount of the reactant exhausted by the process and the attributes of the MPPs other than the measured amount of the reactant exhausted by the process. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A wet flue gas desulfurizing system, comprising:
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a wet flue gas desulfurizer configured (i) to receive SO2 laden wet flue gas, (ii) to apply limestone slurry to remove SO2 from the received SO2 laden wet flue gas, and (iii) to exhaust desulfurized flue gas;
a pH sensor to measure a pH level of the applied limestone slurry;
one of a neural network process model and a non-neural network process model, the one model representing a relationship between the pH level of the applied limestone slurry and an amount of SO2 in the received SO2 laden wet flue gas and an amount of SO2 in the exhausted desulfurized flue gas;
a virtual analyzer having the logic to estimate the pH level of the applied limestone slurry based on an actual amount of SO2 in the received SO2 laden wet flue gas, an actual amount of SO2 in the exhausted desulfurized flue gas and the one model; and
a processor having the logic to compare the estimated pH level of the applied limestone slurry with the measured pH level of the applied limestone slurry, and to determine the validity of the measured pH level of the applied limestone slurry based on the comparison. - View Dependent Claims (16)
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17. A selective catalytic reduction system, comprising:
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selective catalytic reducter configured (i) to receive NOx laden flue gas, (ii) to apply ammonia to remove NOx from the received NOx laden flue gas, and (iii) to exhaust reduced NOx flue gas;
a ammonia sensor to measure an amount of ammonia in the exhausted reduced NOx flue gas;
one of a neural network process model and a non-neural network process model, the one model representing a relationship between the amount of the ammonia in the exhausted reduced NOx flue gas and an actual amount of NOx in the received NOx laden flue gas and an actual amount of NOx in the exhausted reduced NOx flue gas;
a virtual analyzer having the logic to estimate the amount of ammonia in the exhausted reduced NOx flue gas based on the actual amount of NOx in the received NOx laden flue gas and the actual amount of NOx in the exhausted reduced NOx flue gas, and on the one model; and
a processor having the logic to compare the estimated amount of ammonia in the exhausted reduced NOx flue gas with the measured amount of ammonia in the exhausted reduced NOx flue gas, and to determine the validity of the measured amount of ammonia in the exhausted reduced NOx flue gas based on the comparison. - View Dependent Claims (18)
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Specification