@article{(Open Science Index):https://publications.waset.org/pdf/2949,
	  title     = {Soft-Sensor for Estimation of Gasoline Octane Number in Platforming Processes with Adaptive Neuro-Fuzzy Inference Systems (ANFIS)},
	  author    = {Hamed.Vezvaei and  Sepideh.Ordibeheshti and  Mehdi.Ardjmand},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Chemical and Molecular Engineering},
	  volume    = {5},
	  number    = {11},
	  year      = {2011},
	  pages     = {927 - 931},
	  ee        = {https://publications.waset.org/pdf/2949},
	  url   	= {https://publications.waset.org/vol/59},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 59, 2011},
	}