Hybrid LSTM-Fuzzy System to Model a Sulfur Recovery Unit

Jorge S. S. Júnior, Jérôme Mendes, Francisco Souza, Cristiano Premebida

2023

Abstract

Dealing with the dynamics of an industrial process using machine learning techniques has been a paradigm throughout decades of technological advancement. Motivated by addressing this problem, the present work proposes the hybridization of a neo-fuzzy neuron system (NFN) with a long short-term memory network (LSTM), the NFN-LSTM model. The fuzzy part guarantees interpretability through linguistic terms associated with membership functions that allow an effective mapping of the input variables in its universe of discourse with respect to the output. On the other hand, the LSTM part explores high-level representations useful for sequential data in dynamic processes. In this work, a sulfur recovery unit is used as a case study, whose dynamics are mainly associated with peak values in the estimation of residual hydrogen sulfide. The proposed NFN-LSTM model is compared with state-of-the-art methods, such as standalone LSTM, GAM-ZOTS (generalized additive models using zero-order Takagi-Sugeno fuzzy system), iMU-ZOTS (extension of GAM-ZOTS), ALMMo-1 (autonomous learning of a multimodel system from streaming data), iNoMO-TS (iterative learning of multivariate fuzzy models using novelty detection), and SVR (support vector regression). Analyzing the results, the proposed model performed similarly to standalone LSTM, and both outperformed the other methods. Finally, NFN-LSTM manages to balance interpretability and accuracy.

Paper Citation

in Harvard Style

S. S. Júnior J., Mendes J., Souza F. and Premebida C. (2023). Hybrid LSTM-Fuzzy System to Model a Sulfur Recovery Unit. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 281-288. DOI: 10.5220/0012165100003543

in Bibtex Style

@conference{icinco23,
author={Jorge S. S. Júnior and Jérôme Mendes and Francisco Souza and Cristiano Premebida},
title={Hybrid LSTM-Fuzzy System to Model a Sulfur Recovery Unit},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2023},
pages={281-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012165100003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Hybrid LSTM-Fuzzy System to Model a Sulfur Recovery Unit
SN - 978-989-758-670-5
AU - S. S. Júnior J.
AU - Mendes J.
AU - Souza F.
AU - Premebida C.
PY - 2023
SP - 281
EP - 288
DO - 10.5220/0012165100003543
PB - SciTePress