Plastic pollution is a growing global crisis, with devastating environmental consequences. However, a novel artificial intelligence technique using bipolar dual hesitant fuzzy sets (BDHFs) could hold the key to revolutionizing plastic recycling. This cutting-edge research explores how BDHF-based multi-criteria decision-making (MCDM) can help identify the most efficient and sustainable recycling methods, balancing factors like environmental impact, economic feasibility, and technical viability. By incorporating both positive and negative information, BDHF sets offer a more comprehensive and nuanced understanding of the complex challenges faced in plastic waste management. With the potential to drastically reduce plastic pollution, this research represents a significant step towards a more sustainable future. Plastic pollution, Recycling, Sustainability, Artificial intelligence
Tackling the Plastic Pollution Crisis
Plastic has become a ubiquitous part of modern life, with global production reaching over 8 billion metric tons annually. While the versatility and affordability of plastic have made it an essential material, its widespread use and improper disposal have led to a growing environmental crisis. Plastic pollution is a major threat to ecosystems, wildlife, and human health, as it can persist in landfills and oceans for hundreds or even thousands of years, releasing harmful chemicals and contributing to the release of greenhouse gases like methane.
The Importance of Plastic Recycling
Recycling has emerged as a crucial strategy in addressing the plastic pollution crisis. By converting waste materials into new resources, recycling can help conserve natural resources, reduce energy consumption, and mitigate the environmental impact of plastic waste. However, the process of plastic recycling is complex, with numerous techniques and considerations to evaluate. Factors such as environmental impact, economic feasibility, social and technical factors, and safety all play a critical role in determining the most effective recycling approach.
Introducing Bipolar Dual Hesitant Fuzzy Sets (BDHFs)
To navigate the complexities of plastic recycling, researchers have turned to the power of artificial intelligence and multi-criteria decision-making (MCDM) techniques. In this groundbreaking study, the researchers have introduced a novel extension of hesitant fuzzy sets known as bipolar dual hesitant fuzzy sets (BDHFs). BDHFs offer a more comprehensive and nuanced approach to decision-making by incorporating both positive and negative information, allowing for a clearer understanding of the trade-offs and uncertainties involved in the plastic recycling process.
The BDHF-ELECTRE Methodology
The researchers have developed a hybrid MCDM framework that combines the power of BDHFs with the renowned ELECTRE (Elimination and Choice Expressing Reality) method. The BDHF-ELECTRE approach leverages the unique characteristics of BDHFs to address the inherent uncertainties and ambiguities in the plastic recycling decision-making process.
Evaluating Plastic Recycling Techniques
In this study, the researchers examined three primary plastic recycling techniques: chemical recycling, mechanical recycling, and energy recovery (thermal recycling). These alternatives were evaluated against four key criteria: environmental impact, economic factors, social and technical considerations, and safety.
Determining the Optimal Recycling Technique
By applying the BDHF-ELECTRE methodology, the researchers were able to determine the optimal plastic recycling technique. The results showed that mechanical recycling emerged as the most efficient and versatile option, ranking higher than chemical recycling and energy recovery. This finding highlights the importance of considering a comprehensive set of factors when selecting the best recycling approach, as mechanical recycling offers a balance of environmental benefits, cost-effectiveness, and technical feasibility.
Sensitivity Analysis and Validation
To ensure the robustness and reliability of the proposed BDHF-ELECTRE method, the researchers conducted a sensitivity analysis by varying the values of the concordance and discordance dominance matrices. This analysis confirmed the stability of the ranking results, further validating the effectiveness of the BDHF-ELECTRE technique.
Implications and Future Directions
The successful application of the BDHF-ELECTRE methodology in the context of plastic recycling has significant implications for the scientific community and broader society. By leveraging the power of artificial intelligence and MCDM techniques, this research offers a systematic and effective framework for addressing the complex challenges of plastic waste management.
The findings of this study could inform policymakers, waste management authorities, and the plastic industry, guiding them towards more sustainable and efficient recycling strategies. Additionally, the BDHF-ELECTRE approach could be applied to a wide range of decision-making scenarios, beyond just plastic recycling, where complex trade-offs and uncertainties need to be navigated.
Looking to the future, the researchers suggest exploring the integration of additional hybrid MCDM techniques and incorporating more comprehensive qualitative and quantitative criteria to further enhance the decision-making process. As the global community continues to grapple with the plastic pollution crisis, innovative solutions like the BDHF-ELECTRE method offer a promising path towards a more sustainable and environmentally-conscious future.
Author credit: This article is based on research by Lakshmanaraj Ramya, Chakkarapani Sumathi Thilagasree, Thippan Jayakumar, Antony Kishore Peter, Emelia Akashah P. Akhir, Massimiliano Ferrara, Ali Ahmadian.
For More Related Articles Click Here
