Abstract
Digital technologies have helped ensure that mining-influenced water (MIW) is treated and managed effectively in water treatment plants. This paper presents two new intelligent mine water management (iMineWa) tools, eMetsi, and a machine learning graphical user interface (ML-GUI), to help improve practices in mine water treatment plants. eMetsi, which means electronic water in Setswana language, is a framework that uses near-field communication (NFC) technology in conjunction with mobile and website applications in mine water sampling. It incorporates application of NFC microchips to sampling bottles, usage of mobile application for recording on-site data during sampling, and website application for data display and storage. eMetsi enables fast data communication between the samplers, laboratory technicians, and client, and thus helps users to attain sampling and sample analysis targets. ML-GUI is an artificial intelligence-driven user interface that can be used to build predictive models. Even mine water treatment plant operators without programming experience can use ML-GUI to build ML models, which they can use to perform forecasting analysis to plan what chemicals and methods they need to use to manage MIW. Finally, eMetsi and ML-GUI could potentially also be used in other industries such as municipal waste water treatment plants, water resource management, and agriculture.
Resumen
Las tecnologías digitales han contribuido a asegurar que el agua afectada por la minería (MIW) sea tratada y gestionada eficazmente en las plantas de tratamiento. Este artículo presenta dos nuevas herramientas de gestión inteligente del agua de las minas (iMineWa), eMetsi y una interfaz gráfica de usuario de aprendizaje automático (ML-GUI), para mejorar las prácticas en las plantas de tratamiento de agua de minas. eMetsi, que significa agua electrónica en lengua setswana, es un marco que utiliza la tecnología de comunicación de campo cercano (NFC) asociado a aplicaciones móviles y de sitios web en el muestreo del agua de minas. Incorpora la aplicación de microchips NFC a las botellas de muestreo, el uso de una aplicación móvil para registrar los datos in situ durante el muestreo y una aplicación web para visualización y almacenamiento de datos. eMetsi permite una rápida comunicación de datos entre los muestreadores, los técnicos de laboratorio y el cliente y, de ese modo, ayuda a los usuarios a cumplir con los objetivos de muestreo y el análisis de muestras. ML-GUI es una interfaz de usuario basada en inteligencia artificial que puede utilizarse para construir modelos predictivos. Es posible que incluso los operarios de las plantas de tratamiento de aguas mineras, sin experiencia en programación, pueden utilizar ML-GUI para construir modelos de ML, en función de realizar análisis de previsión para planificar qué productos químicos y métodos deben utilizar para gestionar los MIW. Por último, eMetsi y ML-GUI podrían utilizarse también en otros sectores como, por ejemplo, las plantas de tratamiento de residuos municipales, la gestión de recursos hídricos y la agricultura.
摘 要
数字技术有助于确保水处理厂对受采矿影响的水体 (MIW) 进行有效处理和管理. 本文介绍了两种新的智能矿井水管理工具 (iMineWa) 以帮助改进矿井水处理厂的实践:—是eMetsi, 另外—个是机器学习图形用户界面 (ML-GUI). eMetsi, 在塞茨瓦纳语中意为电子水处理, 是将近场通信 (NFC) 技术与移动和网站应用程序相结合的能应用于矿井水取样过程的框架. 它结合了NFC微芯片在采样瓶上的应用, 使用移动应用程序记录采样过程中的现场数据, 以及网站应用程序进行数据的显示和存储. eMetsi可实现采样器, 实验室技术人员和客户之间的快速数据通信, 从而帮助用户实现采样和样品分析的目标. ML-GUI是—种人工智能驱动的用户界面, 可用于构建预测模型. 即使是没有编程经验的矿井水处理厂操作员也可以使用ML-GUI构建ML模型, 他们可以使用该模型进行预测分析, 以方便他们需要使用哪些化学用品和方法来管理受采矿影响的水体. 最后, eMetsi和ML-GUI也可应用于其他行业, 如城市垃圾处理厂, 水资源管理和农业.
Zusammenfassung
Digitale Technologien haben dazu beigetragen, dass bergbaubeeinflusstes Wasser (MIW) in Wasseraufbereitungsanlagen effektiv behandelt und verwaltet wird. In diesem Beitrag werden zwei neue intelligente Instrumente für das Grubenwassermanagement (iMineWa) vorgestellt: eMetsi und eine grafische Benutzeroberfläche für maschinelles Lernen (ML-GUI), die dazu beitragen sollen, die Praktiken in Grubenwasseraufbereitungsanlagen zu verbessern. eMetsi, was in der Sprache Setswana „elektronisches Wasser“ bedeutet, ist ein Rahmenwerk, das die Nahfeldkommunikationstechnologie (NFC) in Verbindung mit mobilen und Website-Anwendungen bei der Probenahme von Grubenwasser nutzt. Es umfasst die Anbringung von NFC-Mikrochips an Probenahmeflaschen, die Verwendung einer mobilen Anwendung für die vor-Ort Aufzeichnung von Daten während der Probenahme und eine Website-Anwendung für die Anzeige und Speicherung dieser Daten. eMetsi ermöglicht eine schnelle Datenkommunikation zwischen den Probenehmern, den Labortechnikern und dem Kunden und hilft so den Benutzern, die Ziele für die Probenahme und -analyse zu erreichen. ML-GUI ist eine durch künstliche Intelligenz gesteuerte Benutzeroberfläche, die zur Erstellung von Vorhersagemodellen verwendet werden kann. Selbst Betreiber von Grubenwasseraufbereitungsanlagen ohne Programmiererfahrung können mit der ML-GUI ML-Modelle erstellen, die sie zur Durchführung von Vorhersageanalysen verwenden können. Dies erlaubt die zeitnahe Bereitstellung von Chemikalien und Methoden, die zur Bewirtschaftung von Grubenwasser nötig sind. Schließlich können eMetsi und ML-GUI auch in anderen Branchen eingesetzt werden, z. B. in kommunalen Kläranlagen, bei der Bewirtschaftung von Wasserressourcen und in der Landwirtschaft.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
Thanks to the National Research Foundation (NRF Grant UID 86948) of South Africa under the SARChI Chair for Mine Water Management and the Tshwane University of Technology (TUT) for funding this project and supporting this research. Technical expertise from Adewale Owolawi and Chester Muyeza in the development of the eMetsi application is hereby acknowledged. This work forms part of the Ph.D. thesis submitted to TUT by the first author. We also thank three anonymous reviewers, whose comments helped improve this work.
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More, K.S., Wolkersdorfer, C. Intelligent Mine Water Management Tools—eMetsi and Machine Learning GUI. Mine Water Environ 42, 111–120 (2023). https://doi.org/10.1007/s10230-023-00917-7
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DOI: https://doi.org/10.1007/s10230-023-00917-7