Evaluation of secondary effluent organic matter removal by an in-series forward osmosis-ultrafiltration hybrid process using parallel factor analysis with self-organizing maps

Seong Nam Nam, Kyungkeun Jo, Sewoon Kim, Byung Moon Jun, Min Jang, Chang Min Park, Jonghun Han, Jiyong Heo, Yeomin Yoon

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This study explores removal of effluent organic matter (EfOM) from secondary municipal wastewater effluent by a custom-devised in-series forward osmosis-ultrafiltration (FO-UF) hybrid system in which naturally-driven osmotic pressure, induced by the concentration difference between the feed solution and the draw solution, is a sole driving force for post UF filtration. Water flux and reverse salt flux are greatly affected by concentration and type of draw solution as well as different FO membrane configurations of the active layer facing feed solution (AL–FS) and the active layer facing draw solution (AL–DS). Maximum fluorescence intensity values obtained from parallel factor analysis (PARAFAC) revealed that the FO-UF hybrid process removes EfOM up to 97% of initial concentration over 2 h experimentation, owing to 6–8% of additional removal by post UF membrane. PARAFAC and self-organizing maps (SOM) modeling revealed that humic-like substances are removed preferentially over protein-like and smaller-sized organics in both AL–FS and AL–DS modes. However, in AL–DS mode, fouling, possibly due to humic-like substances, followed by deteriorating water quality, is observed earlier than in AL–FS mode. During the FO-UF processes, overall properties of EfOM in feed solution are shifted to hydrophilic and smaller-sized organic matter. This work provides a concept of pump-less FO-UF hybrid filtration and demonstrates the proposed system as a feasible option for EfOM removal in water reuse. As the first of its kind, the incorporation of PARAFAC with SOM modeling in FO process gives an insight into the fouling and transport of EfOM in the AL-FS and AL-DS modes.

Original languageEnglish
Article number142640
JournalChemical Engineering Journal
Volume464
DOIs
StatePublished - 15 May 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Effluent organic matter
  • Forward osmosis
  • Machine learning
  • Parallel factor analysis
  • Self-organizing map

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