Ranking of Waste Management Options Under Conditions of Possibilistic Uncertainty Using Fuzzy SAW

https://doi.org/10.22146/ajche.50182

Raymond Girard R. Tan(1*)

(1) Chemical Engineering Department De La Salle University-Manila 2401 TaftAve., Manila 1004 PHILIPPINES
(*) Corresponding Author

Abstract


Integrated waste management involves the use of appropriate techniques ranging from pollution prevention/cleaner production (P2/CP) to conventional end-of-pipe controls. Design or retrofit of process plants usually entails selection of an optimal waste management measure from a number of alternatives. The selection process involves consideration of multiple criteria and data uncertainty, the latter being arguably possibilistic (fuzzy) rather than probabilistic (random) in nature. A fuzzy simple additive weighting (SAW) algorithm is proposed for such problems and demonstrated on a case study. The principal feature of the techniques shown is the retention of fuzzy confidence levels during the assessment of different technological options. Keywords:Cleaner production, fuzzynumbers, pollutioncontrol, pollution prevention, and possibility theory.

Keywords


Cleaner production, fuzzy numbers, pollution control, pollution prevention, and possibility

Full Text:

Full Text


References

  1. Chen, S. J., and Hwang, C. L. (1992). Fuzzy multiple attribute decision-making, Springer-Verlag, Berlin 
  2. Chu, T. C. (2002). "Selecting plant location via a fuzzy TOPSIS approach," Int. J. Adv. Manuf. Techn., 20, 859–64. 
  3. Crittenden, B., and Kolaczkowski, S. (1995). Waste minimisation. A practical guide, Institute of Chemical Engineers (IChemE), United Kingdom 
  4. Culaba, A, B., and Purvis, M. R. I. (1999). "A methodology for the life-cycle and sustainability analysis of manufacturing processes," J. Clean. Prod., 7, 435-45.
  5. Dubois, D., and Prade, H. (1988). Possibility theory: An approach to the computerized processing of uncertainty, Plenum Press, New York. 
  6. Geldermann, J., Spengler, T., and Rentz, O. (2000). "Fuzzy outranking for environmental assessment. Case study: Iron and steel making industry," Fuzzy Set. Syst., 115, 45-65. 
  7. Kaufmann, A., and Gupta, M. M. (1991). Introduction to fuzzy arithmetic: Theory and applications, International Thomson Computer Press, London. 
  8. Le Teno, J. F., and Mareschal, B. (1998). "An interval version of PROMETHEE for the comparison of building products' design with ill-defined data on environmental quality," Eur. J. Oper. Res., 109, 522-29. 
  9. Mauris, G., Lasserre, V., and Foulloy, L. (2001). "A fuzzy approach for the expression of uncertainty in measurement," Measurement, 29, 165-77. 
  10. Moore, R., and Lodwick, W. (2003). "Interval analysis and fuzzy set theory," Fuzzy Set. Syst., 135, 5-9. 
  11. Pineda-Henson, R., Culaba, A. B., and Mendoza, G. A. (2002). "Evaluating environmental performance of pulp and paper manufacturing using the analytic hierarchy process and life cycle assessment,” Journal of Industrial Ecology, 6, 15–29. 
  12. Sharratt, P. (1999). "Environmental criteria in design," Comput. Chem. Eng., 23, 1469–75. 
  13. Stansbury, J., Bogardi, I., Lee, Y. W., and Woldt, W. (1992). "Multiobjective decision-making under uncertainty," In: A. Goicochea, L. Duckstein, and S. Zionts, eds., “Multiple criteria decision-making,” Proceedings of the 9th International Conference--Theory and Applications in Business, Industry, and Government, Springer Verlag, Berlin. 
  14. Tan. R. R. (2002). “Streamlined environmental life-cycle assessment using fuzzy semiquantitative evaluation matrices," Proceedings of the 3rd Pacific Asia Conference on Mechanical Engineering, Manila, Philippines. 
  15. Tan, R. R., and Culaba, A. B. (2001). "Life cycle impact assessment using possibilistic compromise program ming," Proceedings of the 50th National Convention of the Philippine Association for the Advancement of Science, Manila, Philippines. 
  16. Tan, R. R., Culaba, A. B., and Purvis, M. R. I. (2002). "Application of possibility theory in the life cycle inventory assessment of biofuels," International Journal of Energy Research, 26, 737-45. 
  17. Tan, R. R., Culaba, A. B., and Purvis, M. R. I. (2003). "Development of a life-cycle model using possibilistic uncertainty propagation and compromise programming for the evaluation of alternative motor vehicle fuels," Proceedings of the First International Conference on Humanoid, Nanotech nology, Information Technology, Com munications and Control, Environment and Management, Manila, Philippines, 



DOI: https://doi.org/10.22146/ajche.50182

Article Metrics

Abstract views : 24 | views : 47

Refbacks

  • There are currently no refbacks.