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Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends

Author

Listed:
  • Javier De la Hoz-M

    (Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, Colombia)

  • Edwan Anderson Ariza-Echeverri

    (Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, Colombia)

  • Diego Vergara

    (Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Catholic University of Avila, C/Canteros s/n, 05005 Ávila, Spain)

Abstract

Wastewater treatment is a critical process for ensuring water quality and public health, particularly in the context of increasing environmental challenges such as pollution and water scarcity. Artificial intelligence (AI) has emerged as a transformative technology capable of optimizing various wastewater treatment processes, such as contaminant removal, energy consumption, and cost-efficiency. This study presents a comprehensive bibliometric analysis of AI applications in wastewater treatment, utilizing data from Scopus and Web of Science covering 4335 publications from 1985 to 2024. Utilizing machine learning techniques such as neural networks, fuzzy logic, and genetic algorithms, the analysis reveals key trends in the role of the AI in optimizing wastewater treatment processes. The results show that AI has increasingly been applied to solve complex problems like membrane fouling, nutrient removal, and biofouling control. Regional contributions highlight a strong focus on advanced oxidation processes, microbial sludge treatment, and energy optimization. The Latent Dirichlet Allocation (LDA) model further identifies emerging topics such as real-time process monitoring and AI-driven effluent prediction as pivotal areas for future research. The findings provide valuable insights into the current state and future potential of AI technologies in wastewater management, offering a roadmap for researchers exploring the integration of AI to address sustainability challenges in the field.

Suggested Citation

  • Javier De la Hoz-M & Edwan Anderson Ariza-Echeverri & Diego Vergara, 2024. "Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends," Resources, MDPI, vol. 13(12), pages 1-28, December.
    • Handle: RePEc:gam:jresou:v:13:y:2024:i:12:p:171-:d:1544891
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    References listed on IDEAS

    as
    1. Javier De la Hoz-M & Mª José Fernández-Gómez & Susana Mendes, 2021. "LDAShiny: An R Package for Exploratory Review of Scientific Literature Based on a Bayesian Probabilistic Model and Machine Learning Tools," Mathematics, MDPI, vol. 9(14), pages 1-21, July.
    2. Yakun Yuan & Shuhu Xiao & Bingfei Yan & Songtao Shen & Runtao Dou & Xiaohe Xu, 2024. "Bibliometric Based Analysis of Hydrogels in the Field of Water Treatment," Sustainability, MDPI, vol. 16(18), pages 1-19, September.
    3. Karime Montes Escobar & José Luis Vicente-Villardon & Javier de la Hoz-M & Lelly María Useche-Castro & Daniel Fabricio Alarcón Cano & Aline Siteneski, 2021. "Frequency of Neuroendocrine Tumor Studies: Using Latent Dirichlet Allocation and HJ-Biplot Statistical Methods," Mathematics, MDPI, vol. 9(18), pages 1-15, September.
    Full references (including those not matched with items on IDEAS)

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