02420nas a2200169   4500008004100000245015100041210006900192260000800261300001200269490000700281520175000288100001602038700001902054700002402073700002302097856013002120       2017                        eng d00aPrediction of degrees API values of crude oils by use of saturates/aromatics/resins/ asphaltenes analysis: computational-intelligence-based models0 aPrediction of degrees API values of crude oils by use of saturat  cJUN  a817-8530 v223 aThe degrees API value is an important physicochemical characteristic of crude oils often used in determining their properties and quality. There exist models-predominantly linear ones-for predicting the degrees API magnitude from the molecular composition of a crude oil. This approach is tedious and time-consuming because it requires quantitative determination of numerous crude-oil components. Usually, the hydrocarbons present in a crude oil are grouped according to their molecular average structures into saturates, aromatics, resins, and asphaltenes (SARA) fractions. An degrees API-value prediction model dependent on these four fractions is relatively easier to develop, although this approach has been rarely used. A rigorous scrutiny suggests that some of the dependencies between the individual SARA fractions and the corresponding degrees API value could be nonlinear. Accordingly, in this study, SARA-fraction-based nonlinear models have been developed for the prediction of values using three computational-intelligence (CI) formalisms: genetic programming (GP), artificialneural networks (ANNs), and support-vector regression (SVR). The SARA analyses and degrees API values of 403 crude-oil samples covering wide ranges have been used in developing these models. A comparison of the CI-based models with an existing linear model indicates that all the former class of models possess a significantly better degrees API-value prediction and generalization performance than those exhibited by the linear model. Also, the SVR-based model has been found to be the most accurate degrees API-value predictor. Because of their better prediction accuracy, CI-based models can be gainfully used to predict degrees API values of crude oils.1 aGoel, Purva1 aSaurabh, Kumar1 aPatil-Shinde, Veena1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/prediction-degrees-api-values-crude-oils-use-saturatesaromaticsresins-asphaltenes-analysis