Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: an advanced approach toward sustainable and carbon-neutral wastewater treatment

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dc.contributor.authorCairone, Stefano
dc.contributor.authorOliva, Giuseppina
dc.contributor.authorRomano, Fabiana
dc.contributor.authorPasquarelli, Federica
dc.contributor.authorMariniello, Aniello
dc.contributor.authorZorpas, Antonis A.
dc.contributor.authorPollard, Simon J. T.
dc.contributor.authorChoo, Kwang-Ho
dc.contributor.authorBelgiorno, Vincenzo
dc.contributor.authorZarra, Tiziano
dc.contributor.authorNaddeo, Vincenzo
dc.date.accessioned2025-04-16T08:51:59Z
dc.date.available2025-04-16T08:51:59Z
dc.date.freetoread2025-04-16
dc.date.issued2025-05
dc.date.pubOnline2025-03-17
dc.description.abstractIntegrating carbon capture and utilization (CCU) technologies into wastewater treatment plants (WWTPs) is essential for mitigating greenhouse gas (GHG) emissions and enhancing environmental sustainability, but further advancements in process monitoring and control are critical to optimizing treatment performance. This study investigates the application of artificial intelligence (AI) modeling to enhance process monitoring and control in a novel integrated CCU biotechnology with a moving bed biofilm reactor (MBBR) sequenced with an algal photobioreactor (aPBR). This system reduces GHG and odour emissions simultaneously. Several machine learning (ML) models, including artificial neural networks (ANNs), support vector machines (SVM), random forest (RF), and least-squares boosting (LSBoost), were tested. The LSBoost was the most suitable for modeling the MBBR + aPBR system, exhibiting the highest accuracy in predicting CO2 (R2 = 0.97) and H2S (R2 = 0.95) emissions from the MBBR. LSBoost also achieved the highest accuracy for predicting CO2 (R2 = 0.85) and H2S (R2 = 0.97) outlet concentrations from the aPBR. These findings underscore the importance of aligning AI algorithms to the characteristics of the treatment technology. The proposed AI models outperformed conventional statistical methods, demonstrating their ability to capture the complex, nonlinear dynamics typical of processes in environmental technologies. This study highlights the potential of AI-driven monitoring and control systems to significantly improve the efficiency of CCU biotechnologies in WWTPs for climate change mitigation and sustainable wastewater management.
dc.description.journalNameChemosphere
dc.description.sponsorshipThis work was supported by the University of Salerno through FARB projects (300393FRB22OLIVA, 300393FRB22NADDE, 300393FRB23NADDE, 300393FRB22ZARRA). Additionally, the outcomes of this study have benefited from insights and developments within the SPORE-MED project, part of the PRIMA program funded by the European Union (Agreement 2322).
dc.format.mediumPrint-Electronic
dc.identifier.citationCairone S, Oliva G, Romano F, et al., (2025) Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: an advanced approach toward sustainable and carbon-neutral wastewater treatment. Chemosphere, Volume 376, May 2025, Article number 144299
dc.identifier.eissn1879-1298
dc.identifier.elementsID567460
dc.identifier.issn0045-6535
dc.identifier.paperNo144299
dc.identifier.urihttps://doi.org/10.1016/j.chemosphere.2025.144299
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23778
dc.identifier.volumeNo376
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/abs/pii/S0045653525002413?via%3Dihub
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4004 Chemical Engineering
dc.subject40 Engineering
dc.subject4011 Environmental Engineering
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectBioengineering
dc.subjectAdvanced process control
dc.subjectBioprocess modeling
dc.subjectCarbon neutrality
dc.subjectGaseous emission control
dc.subjectIntegrated algal biotechnology
dc.subjectOdour treatment technology
dc.subjectSupervised machine learning
dc.subjectEnvironmental Sciences
dc.subjectMeteorology & Atmospheric Sciences
dc.subject.meshArtificial Intelligence
dc.subject.meshWastewater
dc.subject.meshCarbon
dc.subject.meshWaste Disposal, Fluid
dc.subject.meshBiotechnology
dc.subject.meshNeural Networks, Computer
dc.subject.meshGreenhouse Gases
dc.subject.meshBiofilms
dc.subject.meshMachine Learning
dc.subject.meshSupport Vector Machine
dc.subject.meshCarbon Sequestration
dc.subject.meshPhotobioreactors
dc.subject.meshCarbon Dioxide
dc.subject.meshBiofilms
dc.subject.meshCarbon Dioxide
dc.subject.meshCarbon
dc.subject.meshBiotechnology
dc.subject.meshWaste Disposal, Fluid
dc.subject.meshArtificial Intelligence
dc.subject.meshCarbon Sequestration
dc.subject.meshPhotobioreactors
dc.subject.meshMachine Learning
dc.subject.meshSupport Vector Machine
dc.subject.meshGreenhouse Gases
dc.subject.meshNeural Networks, Computer
dc.subject.meshWastewater
dc.subject.meshArtificial Intelligence
dc.subject.meshWastewater
dc.subject.meshCarbon
dc.subject.meshWaste Disposal, Fluid
dc.subject.meshBiotechnology
dc.subject.meshNeural Networks, Computer
dc.subject.meshGreenhouse Gases
dc.subject.meshBiofilms
dc.subject.meshMachine Learning
dc.subject.meshSupport Vector Machine
dc.subject.meshCarbon Sequestration
dc.subject.meshPhotobioreactors
dc.subject.meshCarbon Dioxide
dc.titleEnhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: an advanced approach toward sustainable and carbon-neutral wastewater treatment
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2025-03-04

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