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Decoupled Neuro-Fuzzy Controller for the Cooking-Pressing Process in a Fishmeal Plant

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Resumen

This paper presents the improvement of the cooking-pressing system in a fishmeal plant to produce high-quality meal with low moisture and fat content. Due to limitations in accessing the plant, a computational model based on a Nonlinear AutorRegressive model with eXogenous inputs (NARX) neural network was developed using experimental process data. A decoupled neuro-fuzzy control strategy was proposed, employing Adaptive Neuro-Fuzzy Inference System (ANFIS)-tuned membership functions to optimize the fuzzy controllers. Different fuzzy controller structures - Proportional-Derivative (PD)-type, Proportional-Integral (PI)-type, and hybrid PI-PD-type - were evaluated under typical disturbances and noise to assess robustness in a simulation environment. Implementation considerations for deployment on a real plant were also discussed. Results demonstrate that the proposed neuro-fuzzy controllers meet design specifications and improve disturbance rejection, enhancing the overall efficiency of the cooking-pressing system.

Idioma originalInglés
Título de la publicación alojada2025 16th IEEE International Conference on Industry Applications, INDUSCON 2025 - Proceedings
EditoresJorge Muniz, Galdenoro Botura, Jonatas Boas Leite
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas182-189
Número de páginas8
ISBN (versión digital)9798331558376
DOI
EstadoPublicada - 2025
Evento16th IEEE International Conference on Industry Applications, INDUSCON 2025 - Hybrid, Sao Sebastiao, Brasil
Duración: 14 oct. 202517 oct. 2025

Serie de la publicación

Nombre2025 16th IEEE International Conference on Industry Applications, INDUSCON 2025 - Proceedings

Conferencia

Conferencia16th IEEE International Conference on Industry Applications, INDUSCON 2025
País/TerritorioBrasil
CiudadHybrid, Sao Sebastiao
Período14/10/2517/10/25

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