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CHAPTER SIX
CONCLUSION AND RECOMMENDATIONS
Acid mine drainage (AMD) is one of the most serious and costly environmental threats that the world is currently facing now. To date, several treatments are available to manage the impact of AMD. Due to the increase in the number of treatment options, another problem that decision-makers are facing is how to select the optimum treatment technology with respect to the goal. To achieve a sustainable treatment option, the technology should not only consider the technical aspect but also the economic and environmental aspects. Thus, a need for a generalized framework for the selection of the optimum active treatment technology for AMD is needed.
The selection of the optimum technology option involves a systematic evaluation based on multiple but often conflicting criteria. Thus, the introduction of multi-decision analysis (MCDA) as a tool for decision-makers is necessary for the assessment of alternatives based on the experts’ evaluations and ranking them according to their final score. One of the most common MCDA techniques is the Analytic hierarchy process (AHP). This study proposed the use of the novel Spherical fuzzy sets (SFS) on AHP in the development of a selection framework and determination of the optimum active treatment technology for AMD using experts’ judgments.
Three criteria were considered for the evaluation: 1) technical aspect; 2) economic aspect and; 3) environmental aspect. These criteria were subdivided into eight sub-criteria which were robustness, ease of operation, ease to upgrade, CAPEX, OPEX, effluent quality, sludge production, and resource recovery. Three treatment technology were considered in the selection: 1) the combination of conventional lime neutralization and sulfidogenic bioreactor (CLN + SB); 2) the high-density sludge (HDS) process; 3) the combination of two-step neutralization ferrite formation and sulfidogenic bioreactor
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(TSNFF + SB). The assessment of the alternative with respect to robustness, ease of operation, and ease to upgrade was obtained from the pair-wise comparison obtained from the decision-makers. On the other hand, the assessment for CAPEX, OPEX, effluent quality, sludge production, and resource recovery were obtained from literature values.
A total of seven responses were obtained from the decision-maker. To make the analysis more reliable, the study proposed a novel technique of incorporating the level of confidence of the decision-maker obtained from the questionnaire on the aggregation of the linguistic assessment for each pairwise question. Two aggregation methods were employed – aggregation of individual judgments (homogeneous) and aggregation of individual priorities (heterogeneous). Results showed that for both aggregation methods, a high level of importance was given to the environmental aspect over the technical and economic aspects. Aggregation of results gave a final ranking of TSNFF + SB > HDS >
CLN + SB, giving the highest overall score to A3.
To check the robustness of the analysis, sensitivity analysis was done in order to verify the effect of the changes in the important level of criteria on the alternative rankings. Rank reversals were noted when the priority weight for technical and economic aspects were increased. The sensitivity analysis indicated that the overall score of TSNFF + SB contributed to the high priority weight given to the environmental aspect. If low importance were given to the environmental aspect and high importance to technical and economic, the most preferred treatment option is HDS. The sensitivity analysis also showed that the least preferred treatment option was CLN + SB. Sensitivity analysis also showed that the most preferred treatment option is TSNFF + SB when equal weights were given for the set of criteria.
The result obtained using SFAHP were also compared with the alternative ranking obtained from classical AHP. TSNFF + SB is still the most preferred treatment option when the data were processed using classical AHP. Classical AHP gave a final ranking of TSNFF > HDS > CLN + SB. Sensitivity analysis was also done to compare
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the results from both tools. Results showed that extreme rank reversals were noted when small changes were done for each criterion. The difference in the obtained weights for the criteria, sub-criteria and alternatives can be attributed to the difference in the assumptions and method for both tools. SFAHP moderated the results by accounting an independent hesitancy parameter. The results obtained from SFAHP are more robust and more stable compared with the results obtained from classical AHP.
Overall, the use of spherical fuzzy sets (SFS) in AHP yielded a robust result.
Insights derived from the analysis could be used by mining sites in selecting the optimum treatment technology with respect to the weighting given to the criteria. Furthermore, it is recommended to do comparative studies with other fuzzy sets to compare the idealized score of priority weights for the final ranking of alternatives for each tool. Future research can also combine different MCDA techniques with SFAHP and explore other scoring functions in the computation of the confidence level of the decision-makers for each pair- wise comparison matrix.
