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

  Zaid Cahya (1*), Parsaulian Siregar (2), Estiyanti Ekawati (3), Irfan Bahiuddin (4), Dito Eka Cahya (5), Tsani Hendro Nugroho (6), Heru Taufiqurrohman (7), Mohammed Boudaoud (8)

(1) Universite Polytechnique Hauts-de-France - France orcid
(2) Insitut Teknologi Bandung - Indonesia
(3) Insitut Teknologi Bandung - Indonesia
(4) Univesitas Gadjahmada - Indonesia
(5) Indonesia National Agency of Research and Innovation - Indonesia
(6) Indonesia National Agency of Research and Innovation - Indonesia
(7) Indonesia National Agency of Research and Innovation - Indonesia
(8) LAMIH UMR CNRS 8201 UNIVERSITE POLYTECHNIQUE HAUTS DE FRANCE, 59300 VALENCIENNES - France
(*) Corresponding Author

Received: October 18, 2024; Revised: March 13, 2025
Accepted: March 18, 2025; Published: August 31, 2025


How to cite (IEEE): Z. Cahya, P. Siregar, E. Ekawati, I. Bahiuddin, D. E. Cahya, T. H. Nugroho, H. Taufiqurrohman,  and M. Boudaoud, "Advanced State Estimations for Gravitational Oil/Water Separator Tanks using a Kalman Filter and Multi-Model Hypothesis Testing," Jurnal Elektronika dan Telekomunikasi, vol. 25, no. 1, pp. 9-19, Aug. 2025. doi: 10.55981/jet.682

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

  http://dx.doi.org/10.55981/jet.682

Keywords


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

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