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Integration of interpretive structural modelling with Bayesian network for biodiesel performance analysis

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  • Sajid, Zaman
  • Khan, Faisal
  • Zhang, Yan

Abstract

The performance of a biodiesel system can be affected by varied risk factors. The numbers of such risk factors are large and their interdependencies are vague and complex. The purpose of this study is to define relationships among such risk factors and integrate them with an objective risk analysis approach. In the present study, an interpretive structural modelling (ISM) approach was used to identify relationships among risk factors while a Bayesian Network (BN) approach was employed to define the strength of dependence and conduct a risk analysis. The results indicate that among 25 risk factors, operational safety is a key biodiesel performance factor. The analysis also highlights that the impacts of occupational health and natural resources depletion are strongly dependent on environmental parameters. Occupational health is also strongly dependent on plant safety. The results show that the interdependency between occupational health and natural resources depletion is weak.

Suggested Citation

  • Sajid, Zaman & Khan, Faisal & Zhang, Yan, 2017. "Integration of interpretive structural modelling with Bayesian network for biodiesel performance analysis," Renewable Energy, Elsevier, vol. 107(C), pages 194-203.
    • Handle: RePEc:eee:renene:v:107:y:2017:i:c:p:194-203
    DOI: 10.1016/j.renene.2017.01.058
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    Cited by:

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    15. Fuli Zhou & Yandong He & Felix T. S. Chan & Panpan Ma & Francesco Schiavone, 2022. "Joint Distribution Promotion by Interactive Factor Analysis using an Interpretive Structural Modeling Approach," SAGE Open, , vol. 12(1), pages 21582440221, February.

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