Short-term memory artificial neural network modelling to predict concrete corrosion in wastewater treatment plant inlet chambers using sulphide sensors

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dc.contributor.authorMendizabal, J.
dc.contributor.authorVernon, D.
dc.contributor.authorMartin, Ben
dc.contributor.authorBajón-Fernández, Yadira
dc.contributor.authorSoares, Ana
dc.date.accessioned2025-01-10T14:51:54Z
dc.date.available2025-01-10T14:51:54Z
dc.date.freetoread2025-01-10
dc.date.issued2025-01-01
dc.date.pubOnline2024-12-26
dc.description.abstractSulphide accumulation in lengthy rising mains can lead to significant concrete corrosion and odour issues at manholes and wastewater treatment plants (WWTPs). Monitoring dissolved sulphide, typically relies on auto-sampling or grab samples followed by laboratory analysis, remains underdeveloped. This study aimed to identify sources of concrete corrosion sources at a WWTP inlet chamber and develop a sulphide prediction model using artificial intelligence (AI). A dissolved sulphide sensor was installed at three rising mains (RM1 to RM3) and the combined inlet at a full-scale WWTP, providing a 5-minute resolution data that revealed a daily hydrogen sulphide (H2S) pattern that inversely correlated with the flow rate. RM1 exhibited the highest sulphide load, peaking at 3.6 kg/d during cold months and 4.2 kg/d during warm months. RM3 and RM2 recorded loads of 2.96 kg/d and 0.98 kg/d, respectively, during cold months. A long short-term memory (LSTM) artificial neural network (ANN) model was developed to predict H₂S concentrations at RM1, using flow rate, temperature, and time of day as inputs. The model achieved a root mean square error (RMSE) of 0.34 and a Nash-Sutcliffe efficiency (NSE) of 0.57, accurately predicting the daily H2S pattern. This study's main contributions include insights into sulphide dynamics from high-resolution sensor data, which could support corrosion management as part of a septicity warning system or feedforward control for sulphide treatment. Additionally, the AI-based prediction model offers potential for sensor repurposing, saving both capital and operational costs.
dc.description.journalNameJournal of Water Process Engineering
dc.description.sponsorshipEngineering and Physical Sciences Research Council
dc.description.sponsorshipThe authors gratefully acknowledge financial support from the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/R512515/1] through their funding of the STREAM Industrial Doctorate Centre, and the Industry project sponsor Thames Water.
dc.identifier.citationMendizabal J, Vernon D, Martin B, et al., (2025) Short-term memory artificial neural network modelling to predict concrete corrosion in wastewater treatment plant inlet chambers using sulphide sensors. Journal of Water Process Engineering, Volume 69, January 2025, Article number 106821
dc.identifier.eissn2214-7144
dc.identifier.elementsID561709
dc.identifier.issn2214-7144
dc.identifier.paperNo106821
dc.identifier.urihttps://doi.org/10.1016/j.jwpe.2024.106821
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23363
dc.identifier.volumeNo69
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S2214714424020531?via%3Dihub
dc.relation.isreferencedbyhttps://doi.org/10.57996/cran.ceres-2689
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4011 Environmental Engineering
dc.subjectBioengineering
dc.subjectMachine Learning and Artificial Intelligence
dc.subject4004 Chemical engineering
dc.subject4005 Civil engineering
dc.subject4011 Environmental engineering
dc.titleShort-term memory artificial neural network modelling to predict concrete corrosion in wastewater treatment plant inlet chambers using sulphide sensors
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-12-17

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