Uncertainty quantification of a deep learning fuel property prediction model

Kiran K. Yalamanchi, Sahil Kommalapati, Pinaki Pal*, Nursulu Kuzhagaliyeva, Abdullah S. AlRamadan, Balaji Mohan, Yuanjiang Pei, S. Mani Sarathy, Emre Cenker, Jihad Badra

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

Original languageEnglish (US)
Article number100211
JournalApplications in Energy and Combustion Science
Volume16
DOIs
StatePublished - Dec 2023

Bibliographical note

Funding Information:
The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in the said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. This work was supported by the U.S. Department of Energy (DOE ) , Office of Science under contract DE-AC02-06CH11357 . Swing, a High Performance LCRC graphics processing unit (GPU) cluster facility at Argonne National Laboratory was used for this study. The authors would like to acknowledge funding support from Aramco Americas – Detroit for this work. The work at KAUST was supported by the Saudi Aramco FUELCOM 3 program and by KAUST award number OSR-2019-CRG7-4077 .

Funding Information:
The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in the said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. This work was supported by the U.S. Department of Energy (DOE), Office of Science under contract DE-AC02-06CH11357. Swing, a High Performance LCRC graphics processing unit (GPU) cluster facility at Argonne National Laboratory was used for this study. The authors would like to acknowledge funding support from Aramco Americas – Detroit for this work. The work at KAUST was supported by the Saudi Aramco FUELCOM 3 program and by KAUST award number OSR-2019-CRG7-4077.

Publisher Copyright:
© 2023

Keywords

  • Aleatoric uncertainty
  • Bayesian neural network
  • Deep learning
  • Epistemic uncertainty
  • Fuel property prediction
  • Monte Carlo ensemble methods
  • Uncertainty quantification

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Fuel Technology
  • Energy (miscellaneous)

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