Results in Engineering (Mar 2024)
Streamlining aromatic content detection in automotive gasoline for environmental protection: Utilizing a rapid and simplified prediction model based on some physical characteristics and regression analysis
Abstract
As the demand for aromatic content control in gasoline grows in order to reduce vehicle particulate emissions, the current study established a power regression model of aromatic content in gasoline based only on two physical property inputs: relative density (RD) and final boiling point (FBP). The model has been developed to predict the aromatics quantity in automotive gasoline, saving time and money by avoiding the use of expensive instruments, inconvenient spectra measurements that require many spectrum input variables. The model developed here yields low errors in terms of average absolute deviation (AAD%), average relative error (Er), standard deviation (SD), standard root mean squared error (RMSE) of the prediction set, and standard error of prediction (SEP), with values of 4.293%, −0.143%, 0.053, 1.06, and 1.58, respectively. When compared to earlier spectra-related PLS models, the model’s prediction applicability and error evaluation are adequate and acceptable.
