Optimizing bioremediation techniques for soil decontamination in a linguistic intuitionistic fuzzy framework

Author: mycolabadmin
7/10/2024
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Summary

This research develops mathematical tools to help experts choose the best method for cleaning contaminated soil using living organisms. The study presents new fuzzy logic operators that can handle both numerical and linguistic information, making decisions more understandable to humans. When applied to a contaminated industrial site, the method identified bioaugmentation (adding beneficial microorganisms) as the most effective cleanup approach among four options tested.

Background

Bioremediation uses microorganisms to remediate contaminated soil in an environmentally sustainable way. Traditional decision-making methods struggle with imprecise information and qualitative assessments needed for selecting optimal bioremediation techniques. Linguistic Intuitionistic Fuzzy Sets (LIFSs) provide a framework for managing uncertainties in both membership and non-membership degrees using linguistic expressions.

Objective

To develop innovative aggregation operators (LIFDWA and LIFDWG) within the Linguistic Intuitionistic Fuzzy framework to enable precise and reliable decision-making for selecting the most effective bioremediation technique for soil decontamination. The research aims to create novel score and accuracy functions for multiple attribute decision-making problems in the LIF environment.

Results

The LIFDWA and LIFDWG operators successfully aggregate linguistic intuitionistic fuzzy numbers while maintaining theoretical properties. Application to bioremediation technique selection identified Bioaugmentation as the optimal method across both operators. Comparative analysis demonstrates superior performance and flexibility compared to existing LIFWA and LIFWG operators through parameter adjustment.

Conclusion

The proposed LIFDWA and LIFDWG operators provide effective and reliable tools for MADM problems in the LIF environment with enhanced interpretability and flexibility. The methodology successfully addresses the selection of optimal bioremediation techniques by incorporating parametric adjustability, offering decision-makers nuanced options aligned with their risk tolerance and contextual demands.

Published in:Scientific Reports,
Study Type:Theoretical Framework Development and Application Study,
Source: 10.1038/s41598-024-66863-8, PMID: 38987312, PMCID: PMC11237113