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Analysis of institutional authors

Guerra-Rodriguez, SoniaAuthorRodriguez-Chueca, JorgeAuthor

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May 26, 2024
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Predictive modeling of Enterococcus sp. removal with limited data from different advanced oxidation processes: A machine learning approach

Publicated to: Journal of Environmental Chemical Engineering. 12 (3): 112530- - 2024-06-01 12(3), DOI: 10.1016/j.jece.2024.112530

Authors:

Pascacio, P; Vicente, DJ; Salazar, F; Guerra-Rodríguez, S; Rodríguez-Chueca, J
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Affiliations

Int Ctr Numer Methods Engn CIMNE, Barcelona 08034, Spain - Author
Univ Politecn Catalunya UPC, Flumen Res Inst, Barcelona 08034, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Ind, Dept Ind Chem & Environm Engn, 28006 Madrid, Spain - Author
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Abstract

The removal of contaminants through Advanced Oxidation Processes (AOPs) is a complex task that demands the simultaneous consideration of multiple operating parameters, such as type and concentration of oxidant and catalyst, type and intensity of radiation, composition of aqueous matrix, etc. Designing efficient AOPs often requires expensive and time-consuming laboratory experiments. To improve this process, this study proposes a Machine Learning approach based on a Random Forest (RF) model, to predict Enterococcus sp. concentration in wastewater treated with various AOPs, even when dealing with limited data. To assess our approach under diverse conditions, a data partitioning methodology is used to categorize the different AOPs into three distinct study cases of increasing complexity, from Case I to Case III. The evaluation of the RF model's performance, combined with the data partitioning methodology, demonstrated its usefulness in predicting missing or additional disinfection values at any instant during the AOPs. Specifically, in Case I, the model excels at generalizing predictions across various AOP treatments, followed by Case II and III, which achieve Root Mean Squared Error (RMSE) values below or comparable to the average RMSE of Case I (0.72) in 8 out of 15 and 2 out of 4 treatments, respectively. Moreover, the effects of imbalanced data on model performance are discussed. This highlights the potential of our approach to assess AOPs performance and facilitate the design of new experiments of the same treatment type without the need for additional laboratory trials, even in challenging conditions.
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Keywords

Advanced oxidation processesChlorineData partitionEnterococcus sp.FentoMachine learninMachine learningRandom forestWaste-waterWastewater treatment

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Journal of Environmental Chemical Engineering due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 25/176, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Chemical.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-26:

  • WoS: 15
  • Scopus: 17
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-26:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 25 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/95384/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 9
  • Downloads: 2
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (RODRIGUEZ CHUECA, JORGE JESUS).

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Awards linked to the item

The publication is part of Projects TED2021-129969A-C32 and TED2021-129969B-C33 funded by MCIN/AEI/10.13039/501100011033 and by "European Union NextGenerationEU/PRTR". Sonia Guerra -Rodriguez acknowledges the Universidad Politecnica de Madrid (UPM) for the financial support provided through the predoctoral contract granted within the "Programa Propio". Jorge Rodriguez- Chueca acknowledges Comunidad de Madrid by the pluriannual agreement with the Polytechnic University of Madrid in the line of action Programme of Excellence for University Teaching Staff (M190020074BJJRC) . This work was also funded by the Spanish Ministry of Economy and Competitiveness through the "Severo Ochoa Programme for Centres of Excellence in R & D" (CEX2018-000797-S) and the Generalitat de Catalonia through the CERCA Program.
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