Advanced State Estimations for Gravitational Oil/Water Separator Tanks using a Kalman Filter and Multi-Model Hypothesis Testing

Authors

  • Zaid Cahya Universite Polytechnique Hauts-de-France http://orcid.org/0000-0003-3244-2199
  • Parsaulian Siregar Insitut Teknologi Bandung
  • Estiyanti Ekawati Insitut Teknologi Bandung
  • Irfan Bahiuddin Univesitas Gadjahmada
  • Dito Eka Cahya Indonesia National Agency of Research and Innovation
  • Tsani Hendro Nugroho Indonesia National Agency of Research and Innovation
  • Heru Taufiqurrohman Indonesia National Agency of Research and Innovation
  • Mohammed Boudaoud LAMIH UMR CNRS 8201 UNIVERSITE POLYTECHNIQUE HAUTS DE FRANCE, 59300 VALENCIENNES

DOI:

https://doi.org/10.55981/jet.682

Keywords:

semi-empirical model, multi-model hypothesis testing, Kalman Filter, gravitational oil/water separation, state estimation, measurements

Abstract

This paper presents a new application of the Kalman filter with Hypothesis testing for a fast and robust model-based estimator for measuring level interfaces of atmospheric gravitational oil-water separator tanks. A newly developed semi-empirical linearized model is applied in the estimator algorithm. A multi-model hypothesis-testing algorithm for covering more scenarios was deployed. The proposed method provides a cost-effective and straightforward solution for estimating all state variables in an oil-water separator. Our evaluation results demonstrate that the proposed algorithm achieves high accuracy with an observation error of less than 2% and a false alarm rate of 3.3% under 50-70% working conditions. Furthermore, the estimator can effectively handle process noise with a 10% feed offset. The proposed platform requires only a few installed sensors yet can accurately estimate unknown parameters. The proposed approach offers a robust and practical soft sensor solution for gravitational oil/water separators

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2025-08-31

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Vol. 25 No. 1 (2025)

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How to Cite

[1]
“Advanced State Estimations for Gravitational Oil/Water Separator Tanks using a Kalman Filter and Multi-Model Hypothesis Testing”, J. Elektron. dan Telekomun., vol. 25, no. 1, pp. 9–19, Aug. 2025, doi: 10.55981/jet.682.