Machine learning estimation of crude oil viscosity as function of api, temperature, and oil composition: model optimization and design space

HIGHLIGHTS

• who: Daihong Li et al. from the School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia have published the paper: Machine learning estimation of crude oil viscosity as function of API, temperature, and oil composition: Model optimization and design space, in the Journal: PLOS ONE of January/17,/2023
• what: In this research, three methods are implemented: Multiple Layers Perceptrons (MLP), Decision Tree (DT), and GRNN for estimation of crude oil viscosity based on compositional data . The comparison of these methods shows the fact that the models are very close . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.