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DOE OSTI · 2588460

PrOMMiS Tutorial

Garciadiego, Alejandro [NETL Site Support Contractor, National Energy Technology Laboratory] (ORCID:0000000300973307)·Tarka, Thomas [NETL] (ORCID:0000000261054366)·Holly, Marcus [NETL Site Support Contractor, National Energy Technology Laboratory]
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Abstract

This is a tutorial on using parameter estimation and process optimization in PrOMMiS. The tutorials are publicly available in the GitHub repository. The presentation attached guides through the flow of the three different tutorials. The tutorials are presented as follows: i) Parameter estimation of oxalate precipitation, ii) Optimization of precipitation model, and iii) Optimization of full process.

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critical minerals and materialsProccess Modeling and Optimization for Minerals Sustainability (PrOMMiS)rare earth elements

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Garciadiego, Alejandro [NETL Site Support Contractor, National Energy Technology Laboratory] (ORCID:0000000300973307), Tarka, Thomas [NETL] (ORCID:0000000261054366), Holly, Marcus [NETL Site Support Contractor, National Energy Technology Laboratory]. 2025-08-26. PrOMMiS Tutorial. https://doi.org/10.2172/2588460

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This is an oral presentation at the TechConnect conference on the work developed by PrOMMiS. PrOMMiS builds on and extends capabilities developed within the Department of Energy’s (DOE) Institute for the Design of Advanced Energy Systems (IDAES), Integrated Platform, and Water Treatment Technoeconomic Assessment Platform (WaterTAP), which have been successfully leveraged by other Department of Energy research areas. The open-source toolkit facilitates validation, reproducibility, and accountability, allowing for easy extension of the framework to other systems. This talk presents an overview of the PrOMMiS capabilities, including unit model library, advances in thermophysical properties models, and capital cost libraries for simulation and optimization of mineral processing technologies. The PrOMMiS applications include (1) conceptual design and superstructure optimization for screening different process configurations and identifying promising technologies; (2) dynamic modeling and optimization to enable the creation of digital twins; (3) surrogate modeling tools to leverage data when predictive thermodynamic models are not currently available; (4) technical risk reduction via uncertainty quantification and robust optimization to identify process designs that are robust to process variability and uncertainties; and (5) deployment of uncertainty quantification tools to maximize knowledge gained from experimental campaigns, while reducing the number of experiments required

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