Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/5322
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dc.contributor.authorSheik, Abdul Gaffaren_US
dc.contributor.authorKrishna, Suresh Babu Naiduen_US
dc.contributor.authorPatnaik, Reezaen_US
dc.contributor.authorAmbati, Seshagiri Raoen_US
dc.contributor.authorBux, Faizalen_US
dc.contributor.authorKumari, Sheena K.en_US
dc.date.accessioned2024-06-27T13:01:17Z-
dc.date.available2024-06-27T13:01:17Z-
dc.date.issued2024-05-10-
dc.identifier.citationSheik, A.G. et al. 2024. Digitalization of phosphorous removal process in biological wastewater treatment systems: challenges, and way forward. Environmental research, 252: pp. 18. doi:10.1016/j.envres.2024.119133en_US
dc.identifier.issn0013-9351-
dc.identifier.issn1096-0953 (Online)-
dc.identifier.otherpubmed: 38735379-
dc.identifier.urihttps://hdl.handle.net/10321/5322-
dc.description.abstractPhosphorus in wastewater poses a significant environmental threat, leading to water pollution and eutrophication. However, it plays a crucial role in the water-energy-resource recovery-environment (WERE) nexus. Recovering Phosphorus from wastewater can close the phosphorus loop, supporting circular economy principles by reusing it as fertilizer or in industrial applications. Despite the recognized importance of phosphorus recovery, there is a lack of analysis of the cyber-physical framework concerning the WERE nexus. Advanced methods like automatic control, optimal process technologies, artificial intelligence (AI), and life cycle assessment (LCA) have emerged to enhance wastewater treatment plants (WWTPs) operations focusing on improving effluent quality, energy efficiency, resource recovery, and reducing greenhouse gas (GHG) emissions. Providing insights into implementing modeling and simulation platforms, control, and optimization systems for Phosphorus recovery in WERE (P-WERE) in WWTPs is extremely important in WWTPs. This review highlights the valuable applications of AI algorithms, such as machine learning, deep learning, and explainable AI, for predicting phosphorus (P) dynamics in WWTPs. It emphasizes the importance of using AI to analyze microbial communities and optimize WWTPs for different various objectives. Additionally, it discusses the benefits of integrating mechanistic and data-driven models into plant-wide frameworks, which can enhance GHG simulation and enable simultaneous nitrogen (N) and Phosphorus (P) removal. The review underscores the significance of prioritizing recovery actions to redirect Phosphorus from effluent to reusable products for future considerations.en_US
dc.format.extent18 pen_US
dc.format.mediumPrint-Electronic-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.relation.ispartofEnvironmental research; Vol. 252en_US
dc.subjectArtificial intelligence and process controlen_US
dc.subjectEnergy recoveryen_US
dc.subjectLife cycle assessmenten_US
dc.subjectPhosphorusen_US
dc.subjectResource recoveryen_US
dc.subjectWastewater treatment processen_US
dc.subject03 Chemical Sciencesen_US
dc.subject05 Environmental Sciencesen_US
dc.subject06 Biological Sciencesen_US
dc.subjectToxicologyen_US
dc.titleDigitalization of phosphorous removal process in biological wastewater treatment systems : challenges, and way forwarden_US
dc.typeArticleen_US
dc.date.updated2024-05-20T08:40:27Z-
dcterms.dateAccepted2024-5-10-
dc.identifier.doi10.1016/j.envres.2024.119133-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.openairetypeArticle-
Appears in Collections:Research Publications (Water and Wastewater Technology)
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