Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource

[EN] The availability of real-time measurements of primary quality water variables is one of the key challenges in the wastewater treatment industry. However, due to the cost and maintenance requirements of sensors and probes for on-line measurement of primary quality variables, the prediction of th...

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Autores: Aguado García, Daniel|||0000-0002-6417-366X, Serralta Sevilla, Joaquín|||0000-0001-5015-0689, Noriega-Hevia, G., Seco, A.
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/212226
Acceso en línea:https://riunet.upv.es/handle/10251/212226
Access Level:acceso abierto
Palabra clave:Anaerobic digester
Machine learning
Membrane contactor
Nitrogen recovery
Soft-sensor
TECNOLOGIA DEL MEDIO AMBIENTE
06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
id ES_71bbd8cd60836ddc70db87dffff96446
oai_identifier_str oai:riunet.upv.es:10251/212226
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource
title Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource
spellingShingle Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource
Aguado García, Daniel|||0000-0002-6417-366X
Anaerobic digester
Machine learning
Membrane contactor
Nitrogen recovery
Soft-sensor
TECNOLOGIA DEL MEDIO AMBIENTE
06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
title_short Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource
title_full Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource
title_fullStr Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource
title_full_unstemmed Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource
title_sort Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource
dc.creator.none.fl_str_mv Aguado García, Daniel|||0000-0002-6417-366X
Serralta Sevilla, Joaquín|||0000-0001-5015-0689
Noriega-Hevia, G.
Seco, A.
author Aguado García, Daniel|||0000-0002-6417-366X
author_facet Aguado García, Daniel|||0000-0002-6417-366X
Serralta Sevilla, Joaquín|||0000-0001-5015-0689
Noriega-Hevia, G.
Seco, A.
author_role author
author2 Serralta Sevilla, Joaquín|||0000-0001-5015-0689
Noriega-Hevia, G.
Seco, A.
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Hidráulica y Medio Ambiente
Instituto Universitario de Ingeniería del Agua y del Medio Ambiente
Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
AGENCIA ESTATAL DE INVESTIGACION
Agencia Estatal de Investigación
European Regional Development Fund
Universitat Politècnica de València
Ministerio de Economía y Competitividad
Ministerio de Asuntos Económicos y Transformación Digital
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Anaerobic digester
Machine learning
Membrane contactor
Nitrogen recovery
Soft-sensor
TECNOLOGIA DEL MEDIO AMBIENTE
06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
topic Anaerobic digester
Machine learning
Membrane contactor
Nitrogen recovery
Soft-sensor
TECNOLOGIA DEL MEDIO AMBIENTE
06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
description [EN] The availability of real-time measurements of primary quality water variables is one of the key challenges in the wastewater treatment industry. However, due to the cost and maintenance requirements of sensors and probes for on-line measurement of primary quality variables, the prediction of these variables via data-driven approaches using as inputs easy-to-measure process variables has attracted research interest. In this paper, different machine learning techniques: feed-forward artificial neural network, random forest, support vector machine, gaussian process regression and partial least squares were used to predict in real-time the total ammonium nitrogen concentration during the operation of a hollow fibre membrane contactor. This recently developed technology allows the recovery of nitrogen from nitrogen rich streams (i.e. supernatant of anaerobic digesters in wastewater treatment plants) as ammonium sulphate (a marketable fertilizer). These contactors are usually operated in batch mode, pumping the high nitrogen concentration feed from the storage tank, where the total ammonium nitrogen concentration decreases progressively as the fertilizer is produced. Knowing the real-time concentration of total ammonium nitrogen in the storage tank would enable the optimization of the process operation, avoiding its operation with conservative fixed-time batch duration. The pH is an easy-to-measure process variable usually available in wastewater treatment plants that was used as input of the tested datadriven models, together with two extracted features from this variable (its derivative and increments after each reagent dosing). The number of total ammonium nitrogen measurements in the collected database is 2350 data points (corresponding to 8 complete batches, which were divided into 6 for training the data-driven models and 2 for testing them), ranging from 987 to 2.5 mg NH4+-N/L which covers almost the complete range of total ammonium nitrogen concentration values in the membrane contactor. The predictive ability of the developed predictive models was evaluated on the test data set by four indices, namely: the root-mean-square error, the slope and the intercept of the linear fit between the measured and predicted concentrations and the determination coefficient. The results showed a strong predictive ability of the fitted ANN that outperformed the other approaches exhibiting a determination coefficient of 0.99 and the lowest root-mean-square error (19.87 mg/L) in the test set. Permutation variable importance demonstrated that all machine learning techniques depended mainly on the two variables extracted from the pH: its derivative and increments, which resulted to be more important than the pH itself to predict the total ammonium nitrogen concentration.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
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dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/212226
url https://riunet.upv.es/handle/10251/212226
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 CTM2017-86751-C2-1-R ESTUDIO EXPERIMENTAL DE LA APLICACION DE LA TECNOLOGIA DE MEMBRANAS PARA POTENCIAR LA RECUPERACION DE RECURSOS EN LAS EDAR ACTUALES
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 CTM2017-86751-C2-2-R MODELACION Y CONTROL PARA LA IMPLEMENTACION DE LA LA TECNOLOGIA DE MEMBRANAS EN LAS EDAR ACTUALES PARA SU TRANSFORMACION EN ESTACIONES DE RECUPERACION DE RECURSOS
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 CTM2014-54980-C2-1-R OBTENCION DE BIONUTRIENTES Y ENERGIA DEL AGUA RESIDUAL URBANA MEDIANTE CULTIVO DE MICROALGAS, TRATAMIENTOS ANAEROBIOS, CRISTALIZACION DE FOSFORO, ABSORCION DE NH3 Y COMPOSTAJE
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 CTM2014-54980-C2-2-R DESARROLLO DE UN SISTEMA DE CONTROL Y DE SOPORTE A LA DECISION PARA LA OBTENCION DE BIONUTRIENTES Y ENERGIA EN PROCESOS DE TRATAMIENTO DE AGUAS RESIDUALES URBANAS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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spelling Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resourceAguado García, Daniel|||0000-0002-6417-366XSerralta Sevilla, Joaquín|||0000-0001-5015-0689Noriega-Hevia, G.Seco, A.Anaerobic digesterMachine learningMembrane contactorNitrogen recoverySoft-sensorTECNOLOGIA DEL MEDIO AMBIENTE06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos[EN] The availability of real-time measurements of primary quality water variables is one of the key challenges in the wastewater treatment industry. However, due to the cost and maintenance requirements of sensors and probes for on-line measurement of primary quality variables, the prediction of these variables via data-driven approaches using as inputs easy-to-measure process variables has attracted research interest. In this paper, different machine learning techniques: feed-forward artificial neural network, random forest, support vector machine, gaussian process regression and partial least squares were used to predict in real-time the total ammonium nitrogen concentration during the operation of a hollow fibre membrane contactor. This recently developed technology allows the recovery of nitrogen from nitrogen rich streams (i.e. supernatant of anaerobic digesters in wastewater treatment plants) as ammonium sulphate (a marketable fertilizer). These contactors are usually operated in batch mode, pumping the high nitrogen concentration feed from the storage tank, where the total ammonium nitrogen concentration decreases progressively as the fertilizer is produced. Knowing the real-time concentration of total ammonium nitrogen in the storage tank would enable the optimization of the process operation, avoiding its operation with conservative fixed-time batch duration. The pH is an easy-to-measure process variable usually available in wastewater treatment plants that was used as input of the tested datadriven models, together with two extracted features from this variable (its derivative and increments after each reagent dosing). The number of total ammonium nitrogen measurements in the collected database is 2350 data points (corresponding to 8 complete batches, which were divided into 6 for training the data-driven models and 2 for testing them), ranging from 987 to 2.5 mg NH4+-N/L which covers almost the complete range of total ammonium nitrogen concentration values in the membrane contactor. The predictive ability of the developed predictive models was evaluated on the test data set by four indices, namely: the root-mean-square error, the slope and the intercept of the linear fit between the measured and predicted concentrations and the determination coefficient. The results showed a strong predictive ability of the fitted ANN that outperformed the other approaches exhibiting a determination coefficient of 0.99 and the lowest root-mean-square error (19.87 mg/L) in the test set. Permutation variable importance demonstrated that all machine learning techniques depended mainly on the two variables extracted from the pH: its derivative and increments, which resulted to be more important than the pH itself to predict the total ammonium nitrogen concentration.This research was financially supported by the Spanish Ministry of Economy and Competitiveness (MINECO projects CTM2014-54980-C2-1/2-R and CTM2017-86751-C2-1/2-R) with the European Regional Development Fund (ERDF) as well as the Universitat Politecnica de Valencia via a predoctoral FPI fellowship to Guillermo Noriega. We would also like to thank the anonymous reviewers for their suggestions that have contributed to improving this work.ElsevierDepartamento de Ingeniería Hidráulica y Medio AmbienteInstituto Universitario de Ingeniería del Agua y del Medio AmbienteEscuela Técnica Superior de Ingeniería de Caminos, Canales y PuertosAGENCIA ESTATAL DE INVESTIGACIONAgencia Estatal de InvestigaciónEuropean Regional Development FundUniversitat Politècnica de ValènciaMinisterio de Economía y CompetitividadMinisterio de Asuntos Económicos y Transformación DigitalRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/212226reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 CTM2017-86751-C2-1-R ESTUDIO EXPERIMENTAL DE LA APLICACION DE LA TECNOLOGIA DE MEMBRANAS PARA POTENCIAR LA RECUPERACION DE RECURSOS EN LAS EDAR ACTUALESAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 CTM2017-86751-C2-2-R MODELACION Y CONTROL PARA LA IMPLEMENTACION DE LA LA TECNOLOGIA DE MEMBRANAS EN LAS EDAR ACTUALES PARA SU TRANSFORMACION EN ESTACIONES DE RECUPERACION DE RECURSOSMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 CTM2014-54980-C2-1-R OBTENCION DE BIONUTRIENTES Y ENERGIA DEL AGUA RESIDUAL URBANA MEDIANTE CULTIVO DE MICROALGAS, TRATAMIENTOS ANAEROBIOS, CRISTALIZACION DE FOSFORO, ABSORCION DE NH3 Y COMPOSTAJEMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 CTM2014-54980-C2-2-R DESARROLLO DE UN SISTEMA DE CONTROL Y DE SOPORTE A LA DECISION PARA LA OBTENCION DE BIONUTRIENTES Y ENERGIA EN PROCESOS DE TRATAMIENTO DE AGUAS RESIDUALES URBANASopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2122262026-06-13T07:49:27Z
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