CCSI Toolset 3.19 Release

Dataset · Tue Oct 10 04:00:00 EDT 2023

DOI:https://doi.org/10.18141/2589392· OSTI ID:2589392

Beattie, Keith ^[1]; Bianchi, Ludovico ^[1]; Boverhof, Joshua ^[1]; Sotorrio, Pedro ^[1]; Paul, Brandon ^[1]; Berket, Karlo ^[1]; Martin, Miranda ^[1]; Gess, Brady ^[1]

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1. National Energy Technology Laboratory

CCSI Toolset 3.19 Release Highlights A gradient generation tool was developed to support GENN models in FOQUS. Certain machine learning tools train gradient-enhanced neural network (GENN) models which can be more accurate for complex datasets given a priori knowledge of model derivatives. However, the derivatives must be known beforehand and are not often available for process data. This tool automatically predicts the gradients for a training dataset in a form usable by common GENN trainers, such as Surrogate Modeling Toolbox. Support was added for Surrogate Modeling Toolbox GENN models in FOQUS, including updates to the run methods, node properties, test framework, documentation and optional dependencies list. Users can train/save Surrogate Modeling Toolbox gradient-enhanced neural network (GENN) models with custom objects and produce .pkl files compatible with the Machine Learning/Artificial Intelligence Plugin in FOQUS. A simpler implementation of the ordering algorithm in the Sequential Design of Experiments (SDOE) module was included. The SDOE examples documentation was updated. The Optimality-Based Design of Experiments was updated to improve the error handling when the results are None.

• Research Organization: NETL
• Sponsoring Organization: USDOE Office of Fossil Energy (FE)
• OSTI ID: 2589392
• Country of Publication: United States
• Language: English

Related Subjects

AS
FOQUS
Process Systems Engineering