Avoidance of Operational Sampling Errors in Drinking Water Analysis
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/31856 https://doi.org/10.2166/aqua.2022.074 |
Resumo: | The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health. |
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Avoidance of Operational Sampling Errors in Drinking Water AnalysisDrinking WaterEindhoven Classification ModelKnowledge Representation and ReasoningLogic ProgrammingSampling ErrorsWater QualityThe internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.IWA - International Water Association2022-04-27T11:54:44Z2022-04-272022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/31856http://hdl.handle.net/10174/31856https://doi.org/10.2166/aqua.2022.074engFernandes, A., Figueiredo, M., Ribeiro, J., Neves, J. & Vicente, H., Avoidance of Operational Sampling Errors in Drinking Water Analysis. Journal of Water Supply: Research and Technology - AQUA, 71, 373–386, 2022.0003-7214 (paper)1605-3974 (electronic)https://iwaponline.com/aqua/article/71/3/373/87050/Avoidance-of-operational-sampling-errors-inLAVQ-REQUIMTE; CIEPteresa.vila.fernandes@ulusofona.ptmtf@uevora.ptjribeiro@estg.ipvc.ptjneves@di.uminho.pthvicente@uevora.ptFernandes, AnaFigueiredo, MargaridaRibeiro, JorgeNeves, JoséVicente, Henriqueinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:31:34Zoai:dspace.uevora.pt:10174/31856Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:20:49.097534Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Avoidance of Operational Sampling Errors in Drinking Water Analysis |
title |
Avoidance of Operational Sampling Errors in Drinking Water Analysis |
spellingShingle |
Avoidance of Operational Sampling Errors in Drinking Water Analysis Fernandes, Ana Drinking Water Eindhoven Classification Model Knowledge Representation and Reasoning Logic Programming Sampling Errors Water Quality |
title_short |
Avoidance of Operational Sampling Errors in Drinking Water Analysis |
title_full |
Avoidance of Operational Sampling Errors in Drinking Water Analysis |
title_fullStr |
Avoidance of Operational Sampling Errors in Drinking Water Analysis |
title_full_unstemmed |
Avoidance of Operational Sampling Errors in Drinking Water Analysis |
title_sort |
Avoidance of Operational Sampling Errors in Drinking Water Analysis |
author |
Fernandes, Ana |
author_facet |
Fernandes, Ana Figueiredo, Margarida Ribeiro, Jorge Neves, José Vicente, Henrique |
author_role |
author |
author2 |
Figueiredo, Margarida Ribeiro, Jorge Neves, José Vicente, Henrique |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Fernandes, Ana Figueiredo, Margarida Ribeiro, Jorge Neves, José Vicente, Henrique |
dc.subject.por.fl_str_mv |
Drinking Water Eindhoven Classification Model Knowledge Representation and Reasoning Logic Programming Sampling Errors Water Quality |
topic |
Drinking Water Eindhoven Classification Model Knowledge Representation and Reasoning Logic Programming Sampling Errors Water Quality |
description |
The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-27T11:54:44Z 2022-04-27 2022-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/31856 http://hdl.handle.net/10174/31856 https://doi.org/10.2166/aqua.2022.074 |
url |
http://hdl.handle.net/10174/31856 https://doi.org/10.2166/aqua.2022.074 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fernandes, A., Figueiredo, M., Ribeiro, J., Neves, J. & Vicente, H., Avoidance of Operational Sampling Errors in Drinking Water Analysis. Journal of Water Supply: Research and Technology - AQUA, 71, 373–386, 2022. 0003-7214 (paper) 1605-3974 (electronic) https://iwaponline.com/aqua/article/71/3/373/87050/Avoidance-of-operational-sampling-errors-in LAVQ-REQUIMTE; CIEP teresa.vila.fernandes@ulusofona.pt mtf@uevora.pt jribeiro@estg.ipvc.pt jneves@di.uminho.pt hvicente@uevora.pt |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
IWA - International Water Association |
publisher.none.fl_str_mv |
IWA - International Water Association |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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