Predicting the Efficiency of the Oil Removal from Surfactant and Polymer Produced Water by Using Liquid-Liquid Hydrocyclone: Comparison of Prediction Abilities between Response Surface Methodology and Adaptive Neuro-Fuzzy Inference System

Ku Esyra Hani Ku Ishak, Mohammed Abdalla Ayoub

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

The present study developed, evaluated and compared the prediction and simulating efficiency of both, the response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) approaches for oil removal using a liquid-liquid hydrocyclone (LLHC) from surfactant and polymer (SP) produced water. Six parameters were involved in the process: The surfactant concentration, polymer concentration, salinity, initial oil concentration, feed flowrate and split ratio. For RSM, D-optimal design was used, while the ANFIS model was developed in term of this process with the Gaussian membership function. All models were compared statistically based on the training and testing data set by the coefficient of determination (R2), root-mean-square error (RMSE), average absolute percentage error (AAPE), standard deviation (STD), minimum error, and maximum error. The R2 for RSM and the ANFIS model for the testing set were of 0.972 and 0.999, respectively. Both models made good predictions. Trend analysis has been done to confirm the applicability of the models. From the results, it shows that the ANFIS model was more precise compared to the RSM model, which proves that the ANFIS is a powerful tool for modelling and optimizing the efficiency of the oil removal from the LLHC in the presence of SP.

Original languageEnglish
Article number8911322
Pages (from-to)179605-179619
Number of pages15
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • ANFIS
  • liquid-liquid hydrocyclone
  • oil-water separation
  • produced water treatment
  • RSM
  • SP produced water

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering

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