TY - JOUR
T1 - Industrial control under non-ideal measurements
T2 - Data-based signal processing as an alternative to controller retuning
AU - Pisa, Ivan
AU - Morell, Antoni
AU - Vilanova, Ramón
AU - Vicario, Jose Lopez
N1 - Funding Information:
Funding: This research was funded by the Catalan Government under Projects 2017 SGR 1202 and 2017 SGR 1670 as well as by La Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya i del Fons Social Europeu under FI grant number 2020 FI_B2 00038. The APC was funded by the Spanish Government under Projects TEC2017-84321-C4-4-R and DPI2016-77271-R co-funded with European Regional Development Funds of the European Union.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/2/2
Y1 - 2021/2/2
N2 - Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.
AB - Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.
KW - Artificial neural networks
KW - Data-driven methods
KW - Denoising autoencoders
KW - Industrial control
KW - Wastewater treatment plants
UR - http://www.scopus.com/inward/record.url?scp=85100579080&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/s21041237
DO - https://doi.org/10.3390/s21041237
M3 - Article
C2 - 33578649
AN - SCOPUS:85100579080
VL - 21
SP - 1
EP - 31
JO - Sensors
JF - Sensors
SN - 1424-3210
IS - 4
M1 - 1237
ER -