One System, Many Models: Designing a Surrogate Model for Sulfur Thermal Energy Storage: Preprint

Kevin Menear, Dmitry Duplyakin, Madeleine Oliver, Munjal Shah, Michael Martin, Janna Martinek, Karthik Nithyanandam, Zhiwen Ma

Research output: Contribution to conferencePaper


Industrial process heating (IPH) relies primarily on thermal energy generated by fossil fuel combustion to produce, treat, and alter manufactured goods. Thermal energy storage (TES) helps reduce the carbon footprint of IPH systems by facilitating the utilization of renewable and waste heat sources. A promising new TES technology uses elemental sulfur as the heat-storage medium. The design of sulfur TES systems can be evaluated with the aid of computational fluid dynamics (CFD). However, the computational cost of such CFD efforts is prohibitive to comprehensive optimizations over design parameters. To reduce this computational cost, machine learning (ML) models can be developed to act as surrogates for CFD. In this paper, we describe the process of building and evaluating surrogate ML models for facilitating optimization of sulfur TES systems for IPH. To enforce the thermodynamic relationship between the two modeled quantities, we develop a hybrid model for sulfur temperature using both direct predictions of temperature and calculations of temperature from predictions of the heat transfer coefficient. The hybrid model enforces this constraint at the expense of the slightly reduced accuracy compared to two disjoint models. The overall high accuracy observed in our model evaluation demonstrates the usefulness of such surrogate modeling for studying TES systems. This work contributes to the field of TES surrogate modeling by offering a novel accurate hybrid approach to predicting simultaneously the heat transfer coefficient and temperature of the heat storage medium.
Original languageAmerican English
Number of pages13
StatePublished - 2023
EventAAAI Fall Symposium Series - Arlington, VA
Duration: 17 Nov 202219 Nov 2022


ConferenceAAAI Fall Symposium Series
CityArlington, VA

NREL Publication Number

  • NREL/CP-2C00-84235


  • computational fluid dynamics
  • design optimization
  • industrial process heating
  • machine learning
  • surrogate model
  • thermal energy storage
  • thermodynamic constraints


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