ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control

Industrial bioleaching processes often suffer from suboptimal yields and monitoring gaps due to the extreme acidity and corrosiveness of the environment. This article presents a lightweight, web-based digital twin (DT) framework for semi-industrial bioleaching optimization, in tegrating low-cost RGB...

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Autores: Tarres Puertas, Marta Isabel|||0000-0002-4473-6947, Vives Pons, Jordi|||0000-0002-1931-8495, Dorado Castaño, Antonio David|||0000-0003-0238-5867
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:dnet:upcommonspor::9430cc375e30c050552ba6ced90cb8d0
Acceso en línea:https://hdl.handle.net/2117/460917
https://dx.doi.org/10.1109/TII.2026.3679379
Access Level:acceso abierto
Palabra clave:Bioleaching
Digital twin (DT)
Industry IoT
Multimodal sensing
Optical sensing
Support vector machines (SVM)
Unity-WebGL
Àrees temàtiques de la UPC::Enginyeria química::Biotecnologia
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spelling ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process controlTarres Puertas, Marta Isabel|||0000-0002-4473-6947Vives Pons, Jordi|||0000-0002-1931-8495Dorado Castaño, Antonio David|||0000-0003-0238-5867BioleachingDigital twin (DT)Industry IoTMultimodal sensingOptical sensingSupport vector machines (SVM)Unity-WebGLÀrees temàtiques de la UPC::Enginyeria química::BiotecnologiaIndustrial bioleaching processes often suffer from suboptimal yields and monitoring gaps due to the extreme acidity and corrosiveness of the environment. This article presents a lightweight, web-based digital twin (DT) framework for semi-industrial bioleaching optimization, in tegrating low-cost RGB sensing (transformed to hue, saturation, and value space for robustness), IoT connectivity, and support vector machine (SVM) regression within a Unity-WebGL platform. To ensure industrial-grade reliability, the predictive pipeline utilizes a group-based three way split strategy to eliminate data leakage and ensure generalization to unseen experimental batches. While traditional random-split approaches often yield overfit results,our SVM-based regressorachievesageneralized R2 =0.55,providing stable and physically consistent predictions of copper concentration. A targeted ablation study demonstrates that noncontact optical sensing independently outperforms traditional pH probes (R2 = 0.52 vs. R2 = 0.42),offering critical operational resilience in corrosive media(pH <2.0). Model transparency is further validated through residual diagnostics and response surface analysis, confirming homoscedastic behavior and chemical consistency. Furthermore, a pilot evaluation indicates that the DT enables 57.3% faster anomaly detection than legacy supervisory control and data acquisition systems, facilitating proactive intervention. The framework’s decoupled WebGL architecture ensures zero-install deployment, offering a scalable blueprint for real-time bioprocess monitoring in data-scarce industrial environments.This work was supported by the Spanish Agencia Estatal de Investigación Project under Grant PID2020-117520RA-I00 and in part by the Generalitat de Catalunya under Grant SGR 01041.Peer Reviewed9 - Indústria, Innovació i Infraestructura20262026-04-1420262026-04-22journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/460917https://dx.doi.org/10.1109/TII.2026.3679379reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-117520RA-I00 DEVELOPMENT OF A SMART AUTOMATED BIOBASED PROCESS FOR THE RECOVERY OF VALUABLE METALS FROM END-OF-LIFE MOBILE PHONESopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:upcommonspor::9430cc375e30c050552ba6ced90cb8d02026-05-27T15:37:01Z
dc.title.none.fl_str_mv ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control
title ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control
spellingShingle ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control
Tarres Puertas, Marta Isabel|||0000-0002-4473-6947
Bioleaching
Digital twin (DT)
Industry IoT
Multimodal sensing
Optical sensing
Support vector machines (SVM)
Unity-WebGL
Àrees temàtiques de la UPC::Enginyeria química::Biotecnologia
title_short ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control
title_full ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control
title_fullStr ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control
title_full_unstemmed ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control
title_sort ML-driven unity/WebGL digital twin with RGB sensing for bioleaching process control
dc.creator.none.fl_str_mv Tarres Puertas, Marta Isabel|||0000-0002-4473-6947
Vives Pons, Jordi|||0000-0002-1931-8495
Dorado Castaño, Antonio David|||0000-0003-0238-5867
author Tarres Puertas, Marta Isabel|||0000-0002-4473-6947
author_facet Tarres Puertas, Marta Isabel|||0000-0002-4473-6947
Vives Pons, Jordi|||0000-0002-1931-8495
Dorado Castaño, Antonio David|||0000-0003-0238-5867
author_role author
author2 Vives Pons, Jordi|||0000-0002-1931-8495
Dorado Castaño, Antonio David|||0000-0003-0238-5867
author2_role author
author
dc.subject.none.fl_str_mv Bioleaching
Digital twin (DT)
Industry IoT
Multimodal sensing
Optical sensing
Support vector machines (SVM)
Unity-WebGL
Àrees temàtiques de la UPC::Enginyeria química::Biotecnologia
topic Bioleaching
Digital twin (DT)
Industry IoT
Multimodal sensing
Optical sensing
Support vector machines (SVM)
Unity-WebGL
Àrees temàtiques de la UPC::Enginyeria química::Biotecnologia
description Industrial bioleaching processes often suffer from suboptimal yields and monitoring gaps due to the extreme acidity and corrosiveness of the environment. This article presents a lightweight, web-based digital twin (DT) framework for semi-industrial bioleaching optimization, in tegrating low-cost RGB sensing (transformed to hue, saturation, and value space for robustness), IoT connectivity, and support vector machine (SVM) regression within a Unity-WebGL platform. To ensure industrial-grade reliability, the predictive pipeline utilizes a group-based three way split strategy to eliminate data leakage and ensure generalization to unseen experimental batches. While traditional random-split approaches often yield overfit results,our SVM-based regressorachievesageneralized R2 =0.55,providing stable and physically consistent predictions of copper concentration. A targeted ablation study demonstrates that noncontact optical sensing independently outperforms traditional pH probes (R2 = 0.52 vs. R2 = 0.42),offering critical operational resilience in corrosive media(pH <2.0). Model transparency is further validated through residual diagnostics and response surface analysis, confirming homoscedastic behavior and chemical consistency. Furthermore, a pilot evaluation indicates that the DT enables 57.3% faster anomaly detection than legacy supervisory control and data acquisition systems, facilitating proactive intervention. The framework’s decoupled WebGL architecture ensures zero-install deployment, offering a scalable blueprint for real-time bioprocess monitoring in data-scarce industrial environments.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-04-14
2026
2026-04-22
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/460917
https://dx.doi.org/10.1109/TII.2026.3679379
url https://hdl.handle.net/2117/460917
https://dx.doi.org/10.1109/TII.2026.3679379
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://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-117520RA-I00 DEVELOPMENT OF A SMART AUTOMATED BIOBASED PROCESS FOR THE RECOVERY OF VALUABLE METALS FROM END-OF-LIFE MOBILE PHONES
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/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
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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