TY - JOUR
T1 - DNN projectional observer for advanced ozonation systems of complex contaminants mixtures
AU - Andrianova, Olga
AU - Poznyak, Tatyana
AU - Poznyak, Alexander
AU - Chairez, Isaac
N1 - Publisher Copyright:
Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)
PY - 2020
Y1 - 2020
N2 - The aim of this study is to provide a class of state observers, based on differential neural networks, to approximate a class of advanced oxidation systems, based on the application of ozone high oxidant power and catalyst (the named catalytic ozonation). The study considers the design of a state observer for uncertain systems with the restrictions of the ozonation system, including the positivity of the states, as well as the control action. The observer includes a projection operator which is motivated by the state constraints. The learning laws of the proposed differential neural networks are obtained using a class of controlled state restricted Lyapunov functions. The detailed stability analysis proves the input to state stability with respect to the modeling error, as well as the bounded uncertainties of the ozonation system. The experimental confirmation of the state estimation is also presented. The experimental case considers the ozonation of a toxic organic contaminant (therephtalic acid) which is a regular pollutant of the plastic industry wastewater.
AB - The aim of this study is to provide a class of state observers, based on differential neural networks, to approximate a class of advanced oxidation systems, based on the application of ozone high oxidant power and catalyst (the named catalytic ozonation). The study considers the design of a state observer for uncertain systems with the restrictions of the ozonation system, including the positivity of the states, as well as the control action. The observer includes a projection operator which is motivated by the state constraints. The learning laws of the proposed differential neural networks are obtained using a class of controlled state restricted Lyapunov functions. The detailed stability analysis proves the input to state stability with respect to the modeling error, as well as the bounded uncertainties of the ozonation system. The experimental confirmation of the state estimation is also presented. The experimental case considers the ozonation of a toxic organic contaminant (therephtalic acid) which is a regular pollutant of the plastic industry wastewater.
KW - Adaptive state observer
KW - Catalytic ozonation
KW - Differential neural network
KW - Projection operators
KW - State restrictions
UR - http://www.scopus.com/inward/record.url?scp=85107577039&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.1967
DO - 10.1016/j.ifacol.2020.12.1967
M3 - Artículo de la conferencia
AN - SCOPUS:85107577039
SN - 1474-6670
VL - 53
SP - 7872
EP - 7877
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
T2 - 21st IFAC World Congress 2020
Y2 - 12 July 2020 through 17 July 2020
ER -