Integrated Artificial Intelligence Based Material Selection for Industrial Assets
AssigneeSaudi Arabian Oil Company
InventorsJames Arukhe, Olanrewaju Olawuyi
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Abstract
A computer-implemented method that enables the selection of materials for industrial assets is described. The method includes obtaining historical data from a database. Industry standards are integrated with this historical data, and the data is filtered to create multiple training datasets that comply with these standards. The method further involves training one or more machine learning models using these datasets. A recommendation for material selection is generated based on the predictions from the trained models, using a validation mechanism to ensure compliance with industry standards.
Claims20
A computer-implemented method, comprising:
- obtaining, using at least one hardware processor, historical data from a database, wherein the historical data comprises operational conditions, material properties, and performance metrics;
- integrating, using the at least one hardware processor, industry standards with the historical data, wherein the historical data is filtered to obtain multiple training datasets that comply with industry standards;
- and
- training, using the at least one hardware processor, one or more machine learning models to predict material performance using the multiple training datasets, wherein predictions from the trained one or more machine learning models are cross-referenced to predict material performance in response to input operating conditions.
An apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
- obtaining historical data from a database, wherein the historical data comprises operational conditions, material properties, and performance metrics;
- integrating industry standards with the historical data, wherein the historical data is filtered to obtain multiple training datasets that comply with industry standards;
- and
- training one or more machine learning models to predict material performance using the multiple training datasets, wherein predictions from the trained one or more machine learning models are cross-referenced to predict material performance in response to input operating conditions.
A system, comprising:
- one or more memory modules;
- one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory models to perform operations comprising:
- obtaining historical data from a database, wherein the historical data comprises operational conditions, material properties, and performance metrics;
- integrating industry standards with the historical data, wherein the historical data is filtered to obtain multiple training datasets that comply with industry standards;
- and
- training one or more machine learning models to predict material performance using the multiple training datasets, wherein predictions from the trained one or more machine learning models are cross-referenced to predict material performance in response to input operating conditions.
Dependencies are inferred from the claim text.