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Treffer: 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.

Title:
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.
Authors:
Cairone S; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, Fisciano, SA, 84084, Italy., Oliva G; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, Fisciano, SA, 84084, Italy., Romano F; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, Fisciano, SA, 84084, Italy., Pasquarelli F; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, Fisciano, SA, 84084, Italy., Mariniello A; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, Fisciano, SA, 84084, Italy., Zorpas AA; Laboratory of Chemical Engineering and Engineering Sustainability, Faculty of Pure and Applied Sciences, Open University of Cyprus, Giannou Kranidioti 89, Latsia, Nicosia, 2231, Cyprus., Pollard SJT; Cranfield University, Water Science Institute, Faculty of Engineering and Applied Sciences, Bedfordshire, Cranfield, MK43 0AL, UK., Choo KH; Department of Environmental Engineering, Kyungpook National University (KNU), 80 Daehak-ro, Bukgu, Daegu, 41566, Republic of Korea., Belgiorno V; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, Fisciano, SA, 84084, Italy., Zarra T; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, Fisciano, SA, 84084, Italy., Naddeo V; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, Fisciano, SA, 84084, Italy. Electronic address: vnaddeo@unisa.it.
Source:
Chemosphere [Chemosphere] 2025 May; Vol. 376, pp. 144299. Date of Electronic Publication: 2025 Mar 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Science Ltd Country of Publication: England NLM ID: 0320657 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1298 (Electronic) Linking ISSN: 00456535 NLM ISO Abbreviation: Chemosphere Subsets: MEDLINE
Imprint Name(s):
Publication: Oxford : Elsevier Science Ltd
Original Publication: Oxford, New York, : Pergamon Press.
MeSH Terms:
Artificial Intelligence* , Wastewater*/chemistry , Waste Disposal, Fluid*/methods , Biotechnology*/methods , Carbon* , Carbon Sequestration*, Neural Networks, Computer ; Photobioreactors ; Carbon Dioxide/analysis ; Greenhouse Gases/analysis
Contributed Indexing:
Keywords: Advanced process control; Bioprocess modeling; Carbon neutrality; Gaseous emission control; Integrated algal biotechnology; Odour treatment technology; Supervised machine learning
Substance Nomenclature:
0 (Wastewater)
7440-44-0 (Carbon)
142M471B3J (Carbon Dioxide)
0 (Greenhouse Gases)
Entry Date(s):
Date Created: 20250318 Date Completed: 20250513 Latest Revision: 20250513
Update Code:
20250514
DOI:
10.1016/j.chemosphere.2025.144299
PMID:
40101473
Database:
MEDLINE

Weitere Informationen

Integrating 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 CO <subscript>2</subscript> (R <sup>2</sup>  = 0.97) and H <subscript>2</subscript> S (R <sup>2</sup>  = 0.95) emissions from the MBBR. LSBoost also achieved the highest accuracy for predicting CO <subscript>2</subscript> (R <sup>2</sup>  = 0.85) and H <subscript>2</subscript> S (R <sup>2</sup>  = 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.
(Copyright © 2025. Published by Elsevier Ltd.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.