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US 12,761,611 B2Granted
Method for generating artificial intelligence model for process control, process control system based on artificial intelligence model, and reactor comprising same
AssigneeSabic SK Nexlene Company Pte. Ltd.
InventorsDong Hun Kwak, Byeong Eon Park, Dae Ho Shin, Choon Sik Shim, Dong Kyu Park
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Abstract
The present invention relates to a method for generating an artificial intelligence model for process control, a process control system based on the artificial intelligence model, and a reactor comprising same, the present invention may easily derive an optimal reactor input condition for achieving the target operation condition of the reactor and the target physical property value of the product by using the AI model.
Claims8
An artificial intelligence (AI) model-based process control system comprising:
- an AI control model unit including a data storage unit storing a plurality of pieces of preset reactor process data, a data correction unit generating training data by removing outliers from the stored reactor process data, and a data derivation unit for deriving, using an AI model trained using the generated training data, an optimal reactor input condition for satisfying a reactor operation condition and a physical property value of a product produced by a reactor;
- an input unit obtaining data including a reactor target operation condition and a target physical property value of the product and providing the obtained data to the AI control model unit;
- and
- an output unit receiving the optimal reactor input condition for satisfying the reactor target operation condition and the target physical property value of the product from the AI control model unit and controlling input of the reactor based on the optimal reactor input condition,
- wherein the data derivation unit derives the optimal reactor input condition based on the reactor target operation condition and the target physical property value of the product provided by the input unit, and derives a predicted operation condition of the reactor and a predicted physical property value of the product produced by the reactor based on the optimal reactor input condition,
- wherein the AI control model unit further includes:
- a data analysis unit comparing the predicted operation condition of the reactor and the predicted physical property value of the product by the reactor derived by the data derivation unit with an actual operation condition of the reactor and an actual physical property value of the product by the reactor included in the training data;
- and
- a data retraining unit retraining the AI model when a comparison result provided from the data analysis unit satisfies with condition (1) or condition (2) below:
- (1) when an error rate between the actual operation condition of the reactor and the predicted operation condition of the reactor exceeds a preset tolerance,
- (2) when the error rate between the actual physical property value of the product and the predicted physical property value of the product exceeds a preset tolerance,
- wherein the optimal reactor input condition includes (a) below:
- (a):
- one or more of a composition, temperature, flow rate, and pressure of a raw material introduced into the reactor or combinations thereof.
11 more elements
A reactor including an artificial intelligence (AI) model-based process control system,
- the artificial intelligence (AI) model-based process control system comprising:
- an AI control model unit including a data storage unit storing a plurality of pieces of preset reactor process data, a data correction unit generating training data by removing outliers from the stored reactor process data, and a data derivation unit deriving, using an AI model trained using the generated training data, an optimal reactor input condition for satisfying a reactor operation condition and a physical property value of a product of the reactor;
- an input unit obtaining data including a reactor target operation condition and a target physical property value of the product and providing the obtained data to the AI control model unit;
- and
- an output unit receiving the optimal reactor input condition for satisfying the reactor target operation condition and the target physical property value of the product from the AI control model unit and controlling input of the reactor under the optimal reactor input condition,
- wherein the optimal reactor input condition includes (a) below:
- (a):
- one or more of a composition, temperature, flow rate, and pressure of a raw material introduced into the reactor or combinations thereof;
- wherein the data derivation unit derives the optimal reactor input condition based on the reactor target operation condition and the target physical property value of the product provided by the input unit, and derives a predicted operation condition of the reactor and a predicted physical property value of the product produced by the reactor based on the optimal reactor input condition,
- wherein the AI control model unit further includes:
- a data analysis unit comparing the predicted operation condition of the reactor and the predicted physical property value of the product by the reactor derived by the data derivation unit with an actual operation condition of the reactor and an actual physical property value of the product by the reactor included in the training data;
- and
- a data retraining unit retraining the AI model when a comparison result provided from the data analysis unit satisfies with condition (1) or condition (2) below:
- (1) when an error rate between the actual operation condition of the reactor and the predicted operation condition of the reactor exceeds a preset tolerance,
- (2) when the error rate between the actual physical property value of the product and the predicted physical property value of the product exceeds a preset tolerance.
12 more elements
Dependencies are inferred from the claim text.