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Design of a Kalman Filter and Three Observers in a CSTR for the Estimation of Concentration and Temperature in Jacket.

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@article{IJASEIT13722,
   author = {Santiago Cortes and Luis E. Cortes and Etty Sierra Vanegas},
   title = {Design of a Kalman Filter and Three Observers in a CSTR for the Estimation of Concentration and Temperature in Jacket.},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {4},
   year = {2021},
   pages = {1405--1412},
   keywords = {Observer; sliding surface; Kalman filter; continuous stirred-tank reactor; Luenberger.},
   abstract = {

The control implementation loops for the chemical process require measurements and variable estimations that are hard, difficult, and expensive; this is due to the lack of reliable devices, delays, wrong measurements, and expensive devices. The state estimation and non-linear systems parameters let restores state variables that the process requires to identify using the input and output known variables. This paper presents four-state estimators, Luenberger observer, Unknown Inputs, Sliding modes, and Kalman Filter, applied to a chemical process in a Continuous Stirred-Tank Reactor (CSTR) at three dynamics: concentration (CA), temperature (T), and temperature of the jacket (Tj). The estimation of the dynamics is carried out from the measurement of the values of the inputs and outputs of the process. Each estimator was tuned to have values close to the real ones. The three dynamics of the CSTR were assessed with perturbations and parametric changes based on the chemical process's phenomenological model. The estimators' results were close to those of the real process, with estimated deviations of the state variables between 5% and 10% of the real value. The SMO algorithm accepts a greater range of variation at nominal flow input F until 30%, while KF, UIO, and OL reach 5% maximum; this makes possible better estimation of chemical process variables in a CSTR using SMO.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13722},    doi = {10.18517/ijaseit.11.4.13722} }

EndNote

%A Cortes, Santiago
%A Cortes, Luis E.
%A Sierra Vanegas, Etty
%D 2021
%T Design of a Kalman Filter and Three Observers in a CSTR for the Estimation of Concentration and Temperature in Jacket.
%B 2021
%9 Observer; sliding surface; Kalman filter; continuous stirred-tank reactor; Luenberger.
%! Design of a Kalman Filter and Three Observers in a CSTR for the Estimation of Concentration and Temperature in Jacket.
%K Observer; sliding surface; Kalman filter; continuous stirred-tank reactor; Luenberger.
%X 

The control implementation loops for the chemical process require measurements and variable estimations that are hard, difficult, and expensive; this is due to the lack of reliable devices, delays, wrong measurements, and expensive devices. The state estimation and non-linear systems parameters let restores state variables that the process requires to identify using the input and output known variables. This paper presents four-state estimators, Luenberger observer, Unknown Inputs, Sliding modes, and Kalman Filter, applied to a chemical process in a Continuous Stirred-Tank Reactor (CSTR) at three dynamics: concentration (CA), temperature (T), and temperature of the jacket (Tj). The estimation of the dynamics is carried out from the measurement of the values of the inputs and outputs of the process. Each estimator was tuned to have values close to the real ones. The three dynamics of the CSTR were assessed with perturbations and parametric changes based on the chemical process's phenomenological model. The estimators' results were close to those of the real process, with estimated deviations of the state variables between 5% and 10% of the real value. The SMO algorithm accepts a greater range of variation at nominal flow input F until 30%, while KF, UIO, and OL reach 5% maximum; this makes possible better estimation of chemical process variables in a CSTR using SMO.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13722 %R doi:10.18517/ijaseit.11.4.13722 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 4 %@ 2088-5334

IEEE

Santiago Cortes,Luis E. Cortes and Etty Sierra Vanegas,"Design of a Kalman Filter and Three Observers in a CSTR for the Estimation of Concentration and Temperature in Jacket.," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 4, pp. 1405-1412, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.4.13722.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Cortes, Santiago
AU  - Cortes, Luis E.
AU  - Sierra Vanegas, Etty
PY  - 2021
TI  - Design of a Kalman Filter and Three Observers in a CSTR for the Estimation of Concentration and Temperature in Jacket.
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 4
Y2  - 2021
SP  - 1405
EP  - 1412
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Observer; sliding surface; Kalman filter; continuous stirred-tank reactor; Luenberger.
N2  - 

The control implementation loops for the chemical process require measurements and variable estimations that are hard, difficult, and expensive; this is due to the lack of reliable devices, delays, wrong measurements, and expensive devices. The state estimation and non-linear systems parameters let restores state variables that the process requires to identify using the input and output known variables. This paper presents four-state estimators, Luenberger observer, Unknown Inputs, Sliding modes, and Kalman Filter, applied to a chemical process in a Continuous Stirred-Tank Reactor (CSTR) at three dynamics: concentration (CA), temperature (T), and temperature of the jacket (Tj). The estimation of the dynamics is carried out from the measurement of the values of the inputs and outputs of the process. Each estimator was tuned to have values close to the real ones. The three dynamics of the CSTR were assessed with perturbations and parametric changes based on the chemical process's phenomenological model. The estimators' results were close to those of the real process, with estimated deviations of the state variables between 5% and 10% of the real value. The SMO algorithm accepts a greater range of variation at nominal flow input F until 30%, while KF, UIO, and OL reach 5% maximum; this makes possible better estimation of chemical process variables in a CSTR using SMO.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13722 DO - 10.18517/ijaseit.11.4.13722

RefWorks

RT Journal Article
ID 13722
A1 Cortes, Santiago
A1 Cortes, Luis E.
A1 Sierra Vanegas, Etty
T1 Design of a Kalman Filter and Three Observers in a CSTR for the Estimation of Concentration and Temperature in Jacket.
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 4
YR 2021
SP 1405
OP 1412
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Observer; sliding surface; Kalman filter; continuous stirred-tank reactor; Luenberger.
AB 

The control implementation loops for the chemical process require measurements and variable estimations that are hard, difficult, and expensive; this is due to the lack of reliable devices, delays, wrong measurements, and expensive devices. The state estimation and non-linear systems parameters let restores state variables that the process requires to identify using the input and output known variables. This paper presents four-state estimators, Luenberger observer, Unknown Inputs, Sliding modes, and Kalman Filter, applied to a chemical process in a Continuous Stirred-Tank Reactor (CSTR) at three dynamics: concentration (CA), temperature (T), and temperature of the jacket (Tj). The estimation of the dynamics is carried out from the measurement of the values of the inputs and outputs of the process. Each estimator was tuned to have values close to the real ones. The three dynamics of the CSTR were assessed with perturbations and parametric changes based on the chemical process's phenomenological model. The estimators' results were close to those of the real process, with estimated deviations of the state variables between 5% and 10% of the real value. The SMO algorithm accepts a greater range of variation at nominal flow input F until 30%, while KF, UIO, and OL reach 5% maximum; this makes possible better estimation of chemical process variables in a CSTR using SMO.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13722 DO - 10.18517/ijaseit.11.4.13722