Control de caudal con un sensor virtual basado en técnicas de deep learning

Herbón, Raúl González; Ossorio, José Ramón Rodríguez; Mateos, Guzmán González; Álvarez, Antonio; Alonso, Serafí­n; Fuertes, Juan J.

doi:10.17979/spudc.9788497498418.0368

XLIII Jornadas De Automática: Libro De Actas: 7, 8 Y 9 De Septiembre De 2022, Logroño (La Rioja) 2022

DOI: 10.17979/spudc.9788497498418.0368

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Control de caudal con un sensor virtual basado en técnicas de deep learning

Raúl González Herbón¹,

José Ramón Rodríguez Ossorio²,

Guzmán González Mateos³

et al.

Abstract: En el ámbito del control industrial, resulta sumamente interesante el empleo de sensores virtuales en los casos en los que no se puede tener acceso a la variable física a controlar, bien sea porque el sensor real es costoso, no puede ser instalado o se encuentra averiado. En este artículo se propone la implementación de un lazo de control del caudal de una planta piloto industrial, comparando el desempeño de este lazo cuando se emplea el caudalímetro real y cuando se emplea un sensor virtual de caudal. El sens… Show more

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2024

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Cited by 1 publication

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Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop

González-Herbón,

González-Mateos,

Rodríguez-Ossorio

et al. 2024

Neural Comput & Applic

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Measurement of certain variables within the industrial sector remains a challenge due to the prohibitive costs of sensors, the intricate installation processes, or the continuous nature of production demands. Moreover, if a backup sensor is required in case the main sensor fails, the installation and maintenance difficulties are further increased. A possibility to address this issue is the indirect estimation of the desired variable by leveraging other correlated measures within the operational process. Data-driven techniques are well-suited for this aim, given their capacity to model potentially complex industrial processes. This paper proposes the implementation of a virtual flow sensor for its integration in the control loop of an industrial process. More specifically, four different data-driven methods have been tested to obtain the virtual sensor: multiple linear regression (MLR), multilayer perceptron (MLP), long-short term memory (LSTM) and deep long-short term memory (DeepLSTM). MAE, RMSE and $$R^2$$ R 2 have been chosen as evaluation metrics for model selection and testing. Furthermore, the robustness of the virtual flow sensor is not only evaluated under ideal operating conditions, but it is also tested under adverse conditions with various noise levels added to the measured signals. Additionally, the performance of the flow control loop using the real and virtual sensors is also evaluated in both ideal and adverse conditions. IAE, ITAE, and IAVU indices are used to assess the control performance. The results prove the robustness of the LSTM-based virtual flow sensor and the effectiveness of the control loop using it, avoiding the modification of the controller and interrupting the process when the real flow sensor fails.

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Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop

González-Herbón,

González-Mateos,

Rodríguez-Ossorio

et al. 2024

Neural Comput & Applic

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show abstract

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Control de caudal con un sensor virtual basado en técnicas de deep learning

Cited by 1 publication

References 16 publications

Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop

Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop

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