Design of explicit models for predicting the efficiency of heavy oil-sand detachment process by floatation technology

V. Vijayaraghavan, E. V. Lau, Ankit Goyal, Xiaodong Niu, A. Garg, Liang Gao

    Research output: Contribution to journalArticleResearchpeer-review

    6 Citations (Scopus)


    Oil spills on land and shoreline is a common problem. Oil has great affinity to sand thus contaminate soil significantly. Among the many physical and biological processes to separate oil and sand, the floatation process has been widely utilized due to its cost effectiveness. However, there is lack of research in understanding the effect of input process parameters on the efficiency of floatation technology. To solve this problem, a computational model based on Automated Neural Network Search (ANNS) approach is devised in the present work. The computational model was able to predict the response of the floatation process with respect to the variations in the input parameters with a high degree of accuracy. Global sensitivity analysis predicted that the input parameter, namely sand particle size has maximum influence on determining the efficiency of the floatation process. The computational model developed in the present work could serve as a valuable non-conventional technique of measurement and optimization of the floatation process without the need to conduct time consuming experiments.

    Original languageEnglish
    Pages (from-to)122-129
    Number of pages8
    Publication statusPublished - Apr 2019


    • Artificial neural network
    • Floatation technology
    • Oil spilling
    • Oil-sand detachment efficiency
    • Response optimization
    • Sand contamination

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