@article{(Open Science Index):https://publications.waset.org/pdf/10000012,
	  title     = {Correlation and Prediction of Biodiesel Density},
	  author    = {Nieves M. C. Talavera-Prieto and  Abel G. M. Ferreira and  António T. G. Portugal and  Rui J. Moreira and  Jaime B. Santos},
	  country	= {},
	  institution	= {},
	  abstract     = {The knowledge of biodiesel density over large ranges
of temperature and pressure is important for predicting the behavior
of fuel injection and combustion systems in diesel engines, and for
the optimization of such systems. In this study, cottonseed oil was
transesterified into biodiesel and its density was measured at
temperatures between 288 K and 358 K and pressures between 0.1
MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg⋅m-
3. Experimental pressure-volume-temperature (pVT) cottonseed data
was used along with literature data relative to other 18 biodiesels, in
order to build a database used to test the correlation of density with
temperarure and pressure using the Goharshadi–Morsali–Abbaspour
equation of state (GMA EoS). To our knowledge, this is the first that
density measurements are presented for cottonseed biodiesel under
such high pressures, and the GMA EoS used to model biodiesel
density. The new tested EoS allowed correlations within 0.2 kg·m-3
corresponding to average relative deviations within 0.02%. The built
database was used to develop and test a new full predictive model
derived from the observed linear relation between density and degree
of unsaturation (DU), which depended from biodiesel FAMEs
profile. The average density deviation of this method was only about
3 kg.m-3 within the temperature and pressure limits of application.
These results represent appreciable improvements in the context of
density prediction at high pressure when compared with other
equations of state.
	    journal   = {International Journal of Energy and Power Engineering},
	  volume    = {8},
	  number    = {12},
	  year      = {2014},
	  pages     = {1377 - 1386},
	  ee        = {https://publications.waset.org/pdf/10000012},
	  url   	= {https://publications.waset.org/vol/96},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 96, 2014},