Soft-Sensor for Estimation of Gasoline Octane Number in Platforming Processes with Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
Gasoline Octane Number is the standard measure of
the anti-knock properties of a motor in platforming processes, that is
one of the important unit operations for oil refineries and can be
determined with online measurement or use CFR (Cooperative Fuel
Research) engines. Online measurements of the Octane number can
be done using direct octane number analyzers, that it is too
expensive, so we have to find feasible analyzer, like ANFIS
estimators.
ANFIS is the systems that neural network incorporated in fuzzy
systems, using data automatically by learning algorithms of NNs.
ANFIS constructs an input-output mapping based both on human
knowledge and on generated input-output data pairs.
In this research, 31 industrial data sets are used (21 data for training
and the rest of the data used for generalization). Results show that,
according to this simulation, hybrid method training algorithm in
ANFIS has good agreements between industrial data and simulated
results.
[1] Guilandoust MT, Morris AJ, Tham MT. Adaptive inferential control.Proceedings of IEE Part D 1987;134(3):171-9
[2] Montague GA, Morris AJ, Tham MT.Enhancing bioprocess operability withgeneric software sensors. Journal of Biotechnology 1992;25:183-201H.
[3] A. L. Huebner, "Tutorial: Fundamentals of Naphtha Reforming," AIChE
Spring Meeting 1999, Houston, TX, 14-18 March 1999.
[4] DAVID S. J. "STAN" JONES "Handbook of Petroleum Processing".2006 Springer
[5] Mark Lapinski, Lance Baird, and Robert James"HANDBOOK OF PETROLEUM REFINING PROCESSES" 2004 The McGraw-Hill Companies.
[6] Wang S, Jin X. Model-based optimal control of VAV air -conditioning system using genetic algorithm. Building and Environment 2000;35(6):471-87.
[7] Evren Guner."Adaptive NeURO-Fuzzy Inference Systems Applications in Chemical Processes" a Thesis Submitted to the graduate school of
natural and applied Sciences the middle east Technical University
[1] Guilandoust MT, Morris AJ, Tham MT. Adaptive inferential control.Proceedings of IEE Part D 1987;134(3):171-9
[2] Montague GA, Morris AJ, Tham MT.Enhancing bioprocess operability withgeneric software sensors. Journal of Biotechnology 1992;25:183-201H.
[3] A. L. Huebner, "Tutorial: Fundamentals of Naphtha Reforming," AIChE
Spring Meeting 1999, Houston, TX, 14-18 March 1999.
[4] DAVID S. J. "STAN" JONES "Handbook of Petroleum Processing".2006 Springer
[5] Mark Lapinski, Lance Baird, and Robert James"HANDBOOK OF PETROLEUM REFINING PROCESSES" 2004 The McGraw-Hill Companies.
[6] Wang S, Jin X. Model-based optimal control of VAV air -conditioning system using genetic algorithm. Building and Environment 2000;35(6):471-87.
[7] Evren Guner."Adaptive NeURO-Fuzzy Inference Systems Applications in Chemical Processes" a Thesis Submitted to the graduate school of
natural and applied Sciences the middle east Technical University
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:52074", author = "Hamed.Vezvaei and Sepideh.Ordibeheshti and Mehdi.Ardjmand", title = "Soft-Sensor for Estimation of Gasoline Octane Number in Platforming Processes with Adaptive Neuro-Fuzzy Inference Systems (ANFIS)", abstract = "Gasoline Octane Number is the standard measure of
the anti-knock properties of a motor in platforming processes, that is
one of the important unit operations for oil refineries and can be
determined with online measurement or use CFR (Cooperative Fuel
Research) engines. Online measurements of the Octane number can
be done using direct octane number analyzers, that it is too
expensive, so we have to find feasible analyzer, like ANFIS
estimators.
ANFIS is the systems that neural network incorporated in fuzzy
systems, using data automatically by learning algorithms of NNs.
ANFIS constructs an input-output mapping based both on human
knowledge and on generated input-output data pairs.
In this research, 31 industrial data sets are used (21 data for training
and the rest of the data used for generalization). Results show that,
according to this simulation, hybrid method training algorithm in
ANFIS has good agreements between industrial data and simulated
results.", keywords = "Adaptive Neuro-Fuzzy Inference Systems, GasolineOctane Number, Soft-sensor, Catalytic Naphtha Reforming", volume = "5", number = "11", pages = "944-5", }