Controller based on empirical model
US-2024019844-A1 · Jan 18, 2024 · US
US2021286922A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2021286922-A1 |
| Application number | US-201816484778-A |
| Country | US |
| Kind code | A1 |
| Filing date | Feb 5, 2018 |
| Priority date | Feb 10, 2017 |
| Publication date | Sep 16, 2021 |
| Grant date | — |
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A test planning device builds up a boiler model data by using a plurality of input parameters of the boiler classified into a plurality of parameter groups. The apparatus selects one of the plurality of parameter groups as a parameter group subjected to learning, presents test conditions in which an input parameter thereof is defined as a variable, and an input parameter of a parameter group not subjected to learning is defined as a fixed value. The device modifies the model data on the basis of the result of comparison between an actual process value and a virtual process value using the present test conditions, selects a new parameter groups subjected to learning, and presents new test conditions which use the input parameter of the test conditions in which the input parameter of the previous parameter group subjected to learning is optimal, as the fixed value.
Opening claim text (preview).
1 - 6 . (canceled) 7 . A test planning device to present test conditions of a plurality of input parameters for model data of a boiler comprising: an input parameter presentation section to present the test conditions of the plurality of input parameters; a simulation section to compute virtual process values by applying the test conditions of the input parameters to the model data in which virtual operations of the boiler are simulated; an actual process value acquisition section to acquire actual process values made available by setting the test conditions of the input parameters to the boiler and actually operating the boiler; a model data learning section to perform modification processing for the model data; and an output control section to output the virtual process values and the actual process values made available through application of the test conditions, wherein the test conditions of the input parameters are such that the plurality of input parameters are classified into a plurality of parameter groups based on a mutual relationship between each of the actual process values and each of the input parameters, the parameter groups are configured such that the plurality of input parameters are divided into a plurality of areas along an order in which a combustion gas of the boiler flows from a downstream side to an upstream side, the input parameter presentation section selects one parameter group subjected to learning from the plurality of parameter groups along the order and presents the test conditions in which the input parameters of the one parameter group subjected to learning are defined as variables while the other remaining parameter groups are defined as those not subjected to learning, and in which the input parameters of the parameter groups not subjected to learning are defined as fixed values, and the model data learning section performs the modification processing for the model data based on the actual process values when deviation of the actual process values and the virtual process values respectively is out of a predetermined allowable range. 8 . The test planning device according to claim 7 , wherein when the input parameter presentation section selects a new parameter group subjected to learning from the plurality of parameter groups subjected to learning, it presents new test conditions in which input parameters of the new learning parameter group are defined as variables, and the input parameters of the test condition, of the test conditions presented using the parameter group subjected to learning, in which the input parameters selected and conducted as the parameter groups subjected to learning in the past are relatively satisfactory in test result are defined as fixed values. 9 . The test planning device according to claim 7 , further comprising a learning trial number determination section to determine a learning trial number in accordance with a predetermined learning trial number determination condition based on the number of variables set to the respective input parameters included in the parameter group subjected to learning. 10 . The test planning device according to claim 7 , wherein when the deviation of the actual process values and the virtual process values computed by the simulation section using the model data subjected to the modification processing is out of the predetermined allowable range, the input parameter presentation section changes an interval between the input parameters defined as the variables of the parameter group subjected to learning, or a range of the input parameters. 11 . A test planning method to present test conditions of a plurality of input parameters to model data in which virtual operations of a boiler are simulated, comprising: a step of acquiring a plurality of input parameters classified into a plurality of parameter groups which are configured such that the plurality of input parameters are divided into a plurality of areas along an order in which a combustion gas of the boiler flows from a downstream side to an upstream side, based on a mutual relationship between actual process values made available by setting the plurality of input parameters to the boiler and actually operating the boiler and the respective input parameters; a step of presenting test conditions of a plurality of input parameters of a parameter group subjected to learning, which is the one parameter group selected among the plurality of parameter groups along the order, are defined as variables, and the input parameters of other parameter groups not subjected to learning are defined as fixed values; a step of acquiring actual process values made available by setting the test conditions of the input parameters to the boiler and actually operating the boiler; a step of computing virtual process values by applying the test conditions of the input parameters to the model data; a step of when deviation of the actual process values and the virtual process values is out of a predetermined allowable range, performing modification processing for the model data using the actual process values; and a step of outputting the actual process and the virtual process values made available by applying the test conditions to the modified model data.
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involving the use of models or simulators · CPC title
Human interface functionality, e.g. monitoring system providing help to the user in the selection of tests or in its configuration · CPC title
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