TY - JOUR
T1 - ANN-based Internal Model Control strategy applied in the WWTP industry
AU - Pisa, Ivan
AU - Morell, Antoni
AU - Vicario, Jose Lopez
AU - Vilanova, Ramon
N1 - Funding Information:
This research is supported by the Catalan Government under Projects 2017 SGR 1202 and 2017 SGR 1670, 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 and also by the Spanish Government under Projects TEC2017-84321-C4-4-R and DPI2016-77271-R co-funded with European Union ERDF funds.
Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/9
Y1 - 2019/9
N2 - Wastewater Treatment Plants (WWTPs) are industries where highly complex and non-linear processes are performed to reduce the pollutant concentrations of residual waters. However, some nitrogen and phosphorus derived pollutants are generated in these processes. As a consequence, certain control strategies have been developed to maintain these pollutants under certain limits. Benchmark Simulation Model No.1 (BSM1), a framework emulating the behaviour of a general purpose WWTP, considers a default controller strategy based on Proportional Integral (PI) controllers. Nevertheless, these controllers are based on linearised models of the WWTP behaviour. For that reason, this work proposes a new control approach based on Internal Model Controllers (IMC) adopting Artificial Neural Networks (ANNs), which are able to model the real plant behaviour without performing linearisation. Results show that the proposed IMC is improving the default controller performance around a 16% and a 53% in terms of the Integral Absolute Error (IAE) and the Integral Square Error (ISE), respectively.
AB - Wastewater Treatment Plants (WWTPs) are industries where highly complex and non-linear processes are performed to reduce the pollutant concentrations of residual waters. However, some nitrogen and phosphorus derived pollutants are generated in these processes. As a consequence, certain control strategies have been developed to maintain these pollutants under certain limits. Benchmark Simulation Model No.1 (BSM1), a framework emulating the behaviour of a general purpose WWTP, considers a default controller strategy based on Proportional Integral (PI) controllers. Nevertheless, these controllers are based on linearised models of the WWTP behaviour. For that reason, this work proposes a new control approach based on Internal Model Controllers (IMC) adopting Artificial Neural Networks (ANNs), which are able to model the real plant behaviour without performing linearisation. Results show that the proposed IMC is improving the default controller performance around a 16% and a 53% in terms of the Integral Absolute Error (IAE) and the Integral Square Error (ISE), respectively.
KW - Artificial Neural Networks
KW - BSM1
KW - Internal Model Controller
KW - Wastewater Treatment Plants
UR - http://www.scopus.com/inward/record.url?scp=85074208117&partnerID=8YFLogxK
U2 - 10.1109/ETFA.2019.8868241
DO - 10.1109/ETFA.2019.8868241
M3 - Article
AN - SCOPUS:85074208117
SP - 1477
EP - 1480
ER -