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A review of the state-of-the-art wastewater quality characterization and measurement technologies. Is the shift to real-time monitoring nowadays feasible?
Università degli studi di Udine, Polytechnic Department of Engineering and Architecture (DPIA), Udine, 33100, Italy.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-3097-459x
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-5341-3656
2024 (English)In: Journal of Water Process Engineering, E-ISSN 2214-7144, Vol. 60, article id 105061Article in journal (Refereed) Published
Abstract [en]

Efficient characterization of wastewater stream quality is vital to ensure the safe discharge or reuse of treated wastewater (WW). There are numerous parameters employed to characterize water quality, some required by directives (e.g. biological oxygen demand (BOD), total nitrogen (TN), total phosphates (TP)), while others used for process controls (e.g. flow, temperature, pH). Well-accepted methods to assess these parameters have traditionally been laboratory-based, taking place either off-line or at-line, and presenting a significant delay between sampling and result. Alternative characterization methods can run in-line or on-line, generally being more cost-effective. Unfortunately, these methods are often not accepted when providing information to regulatory bodies. The current review aims to describe available laboratory-based approaches and compare them with innovative real-time (RT) solutions. Transitioning from laboratory-based to RT measurements means obtaining valuable process data, avoiding time delays, and the possibility to optimize the (WW) treatment management. A variety of sensor categories are examined to illustrate a general framework in which RT applications can replace longer conventional processes, with an eye toward potential drawbacks. A significant enhancement in the RT measurements can be achieved through the employment of advanced soft-sensing techniques and the Internet of Things (IoT), coupled with machine learning (ML) and artificial intelligence (AI).

Place, publisher, year, edition, pages
Elsevier Ltd , 2024. Vol. 60, article id 105061
Keywords [en]
advanced sensors, real-time controls, wastewater characterization, wastewater treatment process
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-66278DOI: 10.1016/j.jwpe.2024.105061ISI: 001218917700001Scopus ID: 2-s2.0-85187565972OAI: oai:DiVA.org:mdh-66278DiVA, id: diva2:1845835
Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-05-29Bibliographically approved

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Ivan, Heidi LynnSkvaril, Jan

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