基于决策专家置信度的循环fermatan模糊多属性群决策分析可持续工业废水处理技术

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2025-10-04 DOI:10.1016/j.engappai.2025.112549

Prayosi Chatterjee, Mijanur Rahaman Seikh

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引用次数: 0

摘要

评价可持续工业废水处理技术对维护环境完整性和保护公众健康至关重要。工业过程经常产生含有有毒化合物的废水，如金属污染物、有毒化学品和复杂的有机化合物，对生态系统和人类福祉构成严重风险。本研究提出了一个稳健的多属性群体决策框架，以评估跨12个子标准的5种治疗方案。评价模型采用循环模糊数来捕捉专家判断中的不确定性和不精确性。为了提高数据聚合的准确性，引入了四种新的Schweizer-Sklar加权聚合算子，对不同的置信水平进行了积分。通过主观排名比较法（RANCOM）和客观意见权重标准法（OWCM）相结合的混合方法确定标准权重，保证了优先级的平衡。采用两步归一化替代排序法（AROMAN）对备选方案进行排序，这是一种改进判别和一致性的新技术。结果表明，膜生物反应器是最具可持续性的处理方法，得分为0.821，优于活性污泥法25.34%。排名最低的是化学混凝和絮凝，得分为0.622。通过改变三个参数进行敏感性分析，显示出合理的稳定性，平均相关值为0.71。对比分析显示，平均Spearman等级相关系数为0.86，证实了信度。该研究建议在工业处理厂优先采用膜生物反应器，以提高效率和水的再利用。通过推广有效的处理方案，该研究有助于减少工业污染，促进水的再利用，促进环境的可持续性。

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Analysing sustainable industrial wastewater treatment technologies using circular Fermatean fuzzy multi-attribute group decision making with decision experts’ confidence levels

The evaluation of sustainable industrial wastewater treatment techniques is vital for preserving environmental integrity and protecting public health. Industrial processes often generate wastewater containing toxic compounds like metal contaminants, toxic chemicals, and complex organic compounds, posing serious risks to ecosystems and human well-being. This study proposes a robust multi-attribute group decision-making framework to assess five treatment alternatives across twelve sub-criteria. The evaluation model employs circular Fermatean fuzzy numbers to capture uncertainty and imprecision in expert judgements. To enhance the accuracy of data aggregation, four novel Schweizer–Sklar weighted aggregation operators are introduced, integrating varying confidence levels. Criteria weights are determined through a hybrid approach combining the subjective Ranking Comparison (RANCOM) method and the objective Opinion Weight Criteria Method (OWCM), ensuring balanced prioritization. Alternatives are ranked using the Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN), a novel technique for improved discrimination and consistency. Results reveal that the membrane bioreactor as the most sustainable treatment with score 0.821, outperforming activated sludge process, by 25.34%. The lowest-ranked option is chemical coagulation and flocculation, scoring 0.622. Sensitivity analysis, performed by varying three parameters, shows reasonable stability with an average correlation value of 0.71. Comparative analysis shows an average Spearman’s rank correlation of 0.86, confirming reliability. The study recommends prioritizing membrane bioreactor adoption in industrial treatment plants to enhance efficiency and water reuse. By promoting effective treatment solutions, the study contributes to reducing industrial pollution, enhancing water reuse, and advancing environmental sustainability.

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来源期刊

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.