Attention-Based Deep Learning Methods for Predicting Gas Turbine Emissions

Rebecca Lauren Potts*, Georgios Leontidis

*Corresponding author for this work

Research output: Contribution to conferencePosterpeer-review

3 Downloads (Pure)

Abstract

Predictive emissions monitoring systems (PEMS) for gas turbines are critical for monitoring harmful pollutants being released into the atmosphere, while reducing the use of expensive continuous emissions monitoring systems (CEMS) which require daily maintenance to achieve accurate readings. We consider two attention-based deep learning models, FT-Transformer and SAINT, and compare with classical tree-based XGBoost to predict emissions from gas turbines. We find that the attention-based models outperform XGBoost for both prediction tasks, i.e. carbon monoxide (CO) and nitrogen oxides (NOx).
Original languageEnglish
Number of pages3
Publication statusPublished - 23 Jan 2023
EventNorthern Lights Deep Learning Conference 2023 (Extended Abstracts) - Tromso, Tromso, Norway
Duration: 9 Jan 202313 Jan 2023

Conference

ConferenceNorthern Lights Deep Learning Conference 2023 (Extended Abstracts)
Country/TerritoryNorway
CityTromso
Period9/01/2313/01/23

Bibliographical note

This work was supported by the Engineering
and Physical Sciences Research Council
[EP/W522089/1].

Keywords

  • Artificial intelligence
  • Deep Learning
  • Gas Turbines
  • predicting emissions

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