Abstract
The aim of this study is to implement an accurate model for prediction of density of ionic liquids at various temperatures based on an extensive data base gathered from literature published works. The data base includes 602 density data points of 146 ionic liquids at various temperatures. The developed model is based on adaptive neuro-fuzzy inference system (ANFIS) algorithms. The predictions of the developed model were analyzed by various methods including both statistical and graphical approaches. The predictions of the developed model were compared with outcomes of literature correlations for predictions of density of pure liquids. Results show that the developed model accurately predicts the experimental data with an overall R2 and AARD% values of 0.985 and 0.657%, respectively. Moreover, the developed model effectively outperforms literature correlations and presents more accurate and reliable predictions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
