Model Based Monitoring Using Integrated Data Validation, Simulation and Parameter Estimation
Efficient and safe plant operation can only be
achieved if the operators are able to monitor all key process
parameters. Instrumentation is used to measure many process
variables, like temperatures, pressures, flow rates, compositions or
other product properties. Therefore Performance monitoring is a
suitable tool for operators. In this paper, we integrate rigorous
simulation model, data reconciliation and parameter estimation to
monitor process equipments and determine key performance
indicator (KPI) of them. The applied method here has been
implemented in two case studies.
[1] G., Heyen and B., Kalitventzeff, Process monitoring and Data
Reconciliation, in L. Puigjaner and G. Heyen (eds), Computer Aided
Process Engineering, Wiley-VCH, 2006.
[2] N., Arora, L. T., Biegler and G., Heyen, Data Reconciliation
Framework, in B. Braunschweig and R. Gani (eds) Software
Architectures and Tools for Computer Aided Process Engineering,
Elsevier, 2002.
[3] B., Kalitventzeff, G., Heyen, M., Mateus Tavares, Data Validation, a
Technology for intelligent Manufacturing, in L. Puigjaner and G. Heyen
(eds), Computer Aided Process Engineering, Wiley-VCH, 2006..
[4] http://www.belsim.com/Vali.aspx, accessed November 25, 2006.
[5] http://www.pepite.be/en/produits/PEPITo, accessed November 25, 2006.
[6] S., Narasimhan and C., Jordache, Data reconciliation and gross error
detection: an intelligent use of process data, Houston, TX, USA: Gulf
Publishing Company, 2000.
[7] J.A., Romagnoli and M.C., Sanchez, Data processing and reconciliation
for chemical process operations, Academic Press, 2000.
[8] M., Piccolo, P.L., Douglas and P.L., Lee, Data reconciliation using
Aspen Plus, Developments in chemical engineering and mineral
Processing, Volume 4, Issue 3-4, Pages 157-182, 1996.
[9] Aspen Plus User Guide, AspenTech Co., 2008.
[10] http://www.lassc.ulg.ac.be/webCheng00/meca0468-1, November 2006.
[1] G., Heyen and B., Kalitventzeff, Process monitoring and Data
Reconciliation, in L. Puigjaner and G. Heyen (eds), Computer Aided
Process Engineering, Wiley-VCH, 2006.
[2] N., Arora, L. T., Biegler and G., Heyen, Data Reconciliation
Framework, in B. Braunschweig and R. Gani (eds) Software
Architectures and Tools for Computer Aided Process Engineering,
Elsevier, 2002.
[3] B., Kalitventzeff, G., Heyen, M., Mateus Tavares, Data Validation, a
Technology for intelligent Manufacturing, in L. Puigjaner and G. Heyen
(eds), Computer Aided Process Engineering, Wiley-VCH, 2006..
[4] http://www.belsim.com/Vali.aspx, accessed November 25, 2006.
[5] http://www.pepite.be/en/produits/PEPITo, accessed November 25, 2006.
[6] S., Narasimhan and C., Jordache, Data reconciliation and gross error
detection: an intelligent use of process data, Houston, TX, USA: Gulf
Publishing Company, 2000.
[7] J.A., Romagnoli and M.C., Sanchez, Data processing and reconciliation
for chemical process operations, Academic Press, 2000.
[8] M., Piccolo, P.L., Douglas and P.L., Lee, Data reconciliation using
Aspen Plus, Developments in chemical engineering and mineral
Processing, Volume 4, Issue 3-4, Pages 157-182, 1996.
[9] Aspen Plus User Guide, AspenTech Co., 2008.
[10] http://www.lassc.ulg.ac.be/webCheng00/meca0468-1, November 2006.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:60171", author = "Reza Hayati and Maryam Sadi and Saeid Shokri and Mehdi Ahmadi Marvast and Saeid Hassan Boroojerdi and Amin Hamzavi Abedi", title = "Model Based Monitoring Using Integrated Data Validation, Simulation and Parameter Estimation", abstract = "Efficient and safe plant operation can only be
achieved if the operators are able to monitor all key process
parameters. Instrumentation is used to measure many process
variables, like temperatures, pressures, flow rates, compositions or
other product properties. Therefore Performance monitoring is a
suitable tool for operators. In this paper, we integrate rigorous
simulation model, data reconciliation and parameter estimation to
monitor process equipments and determine key performance
indicator (KPI) of them. The applied method here has been
implemented in two case studies.", keywords = "Data Reconciliation, Measurement, Optimization,Parameter Estimation, Performance Monitoring.", volume = "5", number = "2", pages = "172-5", }