Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends
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 wast...
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MDPI AG
2024-12-01
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| author | Javier De la Hoz-M Edwan Anderson Ariza-Echeverri Diego Vergara |
| author_facet | Javier De la Hoz-M Edwan Anderson Ariza-Echeverri Diego Vergara |
| author_sort | Javier De la Hoz-M |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6f2274722ade45ecac51b60dd113ebdf |
| institution | DOAJ |
| issn | 2079-9276 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Resources |
| spelling | doaj-art-6f2274722ade45ecac51b60dd113ebdf2025-08-20T02:43:46ZengMDPI AGResources2079-92762024-12-01131217110.3390/resources13120171Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research TrendsJavier De la Hoz-M0Edwan Anderson Ariza-Echeverri1Diego Vergara2Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, ColombiaFacultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, ColombiaTechnology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Catholic University of Avila, C/Canteros s/n, 05005 Ávila, SpainWastewater 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.https://www.mdpi.com/2079-9276/13/12/171effluent predictionwater optimizationpollutant recyclingmachine learninggenetic algorithmsmicrobial treatment |
| spellingShingle | Javier De la Hoz-M Edwan Anderson Ariza-Echeverri Diego Vergara Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends Resources effluent prediction water optimization pollutant recycling machine learning genetic algorithms microbial treatment |
| title | Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends |
| title_full | Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends |
| title_fullStr | Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends |
| title_full_unstemmed | Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends |
| title_short | Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends |
| title_sort | exploring the role of artificial intelligence in wastewater treatment a dynamic analysis of emerging research trends |
| topic | effluent prediction water optimization pollutant recycling machine learning genetic algorithms microbial treatment |
| url | https://www.mdpi.com/2079-9276/13/12/171 |
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