A Spectral AutoML approach for industrial soft sensor development: Validation in an oil refinery plant

Daniela C. M. de Souza, Luís Cabrita, Cláudia F. Galinha, Tiago J. Rato, Marco S. Reis

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Spectral AutoML is a platform for fast development of PAT soft sensors that considers the combined effect of pre-processing, band selection, band-wise resolution definition, hyper-parameter tuning and model estimation. Spectral AutoML was compared with models developed under the classic paradigm, and their performance assessed on an independent test set. The validation study regards the prediction of 12 different diesel fuels properties, using FTIR-ATR spectra. The proposed framework led to clearly better predictions in 8 out of the 12 properties, and minor improvements in 3 properties. The Spectral AutoML results were obtained overnight, without interfering in the daily work of the users, while the benchmark models resulted from several months of work and fine tuning of the methods. The results demonstrated the added value of the proposed Spectral AutoML approach in terms of prediction accuracy, development time of the models and reduced dependence on resident experts.

Original languageEnglish
Article number107324
JournalComputers and Chemical Engineering
Volume150
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Diesel
  • Infrared Spectroscopy
  • Property prediction
  • Soft sensors
  • Spectral pre-processing
  • Waveband selection

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