Artificial intelligence for enhanced flotation monitoring in the mining industry: A ConvLSTM-based approach

Bendaouia, Ahmed; Abdelwahed, El Hassan; Qassimi, Sara; Boussetta, Abdelmalek; Benzakour, Intissar; Amar, Oumkeltoum; Hasidi, Oussama

doi:10.1016/j.compchemeng.2023.108476

Cited by 15 publications

(5 citation statements)

References 30 publications

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“…The ConvLSTM can recognize interrelationships between variables and sequential patterns in power system data, notably constant voltage levels over time. Several studies have applied ConvLSTM performance to real applications, such as the recognition of human activity [20], the prediction of photovoltaic output power [21], a flood forecast model [22], mining industry flotation monitoring [23], and others. In this study, the ConvLSTM network is presented as a novel solution to improve voltage stability in power systems.…”

Section: Establishing the Suggested Model Using The Predetermined Par...mentioning

confidence: 99%

Convolutional Long Short-Term Memory (ConvLSTM)-Based Prediction of Voltage Stability in a Microgrid

Abbass,

Lis,

Awais

et al. 2024

Energies

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The maintenance of an uninterrupted electricity supply to meet demand is of paramount importance for maintaining the stable operation of an electrical power system. Machine learning and deep learning play a crucial role in maintaining that stable operation. These algorithms have the ability to acquire knowledge from past data, enabling them to efficiently identify and forecast potential scenarios of instability in the future. This work presents a hybrid convolutional long short-term memory (ConvLSTM) technique for training and predicting nodal voltage stability in an IEEE 14-bus microgrid. Analysis of the findings shows that the suggested ConvLSTM model exhibits the highest level of precision, reaching a value of 97.65%. Furthermore, the ConvLSTM model has been shown to perform better compared to alternative machine learning and deep learning models such as convolutional neural networks, k-nearest neighbors, and support vector machine models, specifically in terms of accurately forecasting voltage stability. The IEEE 14-bus system tests indicate that the suggested method can quickly and accurately determine the stability status of the system. The comparative analysis obtained the results and further justified the efficiency and voltage stability of the proposed model.

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Section: Establishing the Suggested Model Using The Predetermined Par...mentioning

confidence: 99%

Convolutional Long Short-Term Memory (ConvLSTM)-Based Prediction of Voltage Stability in a Microgrid

Abbass,

Lis,

Awais

et al. 2024

Energies

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“…This offers a challenging demand for the development of practical, flexible, and cost-effective solutions that can effectively work in different industrial situations to be considered in future research [27]. In addition, we should take into account the security problem of the data, complexity of integration, and scalability problem [28] in order to provide the smooth implementation of the integrated monitoring systems empowered by DT technology [22,29].…”

Section: Monitoring Systemmentioning

confidence: 99%

“…Nevertheless, the inclusion of such systems in the current networked world also poses serious challenges in regard to system integration, cybersecurity, and adaptation to different production zones [29]. A vital research viewpoint is the successive gaps between theoretical development and the actual realization of the scaled 'Pseudo-mobile work lifestyle' [30].…”

Section: Production Management Systemmentioning

confidence: 99%

Scalable Compositional Digital Twin-Based Monitoring System for Production Management: Design and Development in an Experimental Open-Pit Mine

El Bazi,

Laayati,

Darkaoui

et al. 2024

Designs

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While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry.

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“…Bendaouia et al [40] also investigated the Convolutional Long Short-Term Memory model for real-time monitoring of chemical composition grades in flotation froth. The model's deployment architecture enables real-time monitoring of elemental concentrate grades, facilitating adjustments to flotation parameters for enhanced process efficiency.…”

Section: Predictions Of Flotation Performance and Feature Importance ...mentioning

confidence: 99%

“…The growing interest in monitoring and controlling the flotation process with machine learning models is evident. The year 2024 has already started with some insightful research studies presented by Bendaouia et al [38,40] in the area of froth image extraction and analysis, and it is expected that more research in this field will be presented.…”

Section: Summaries and Future Workmentioning

confidence: 99%

Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Szmigiel,

Apel,

Skrzypkowski

et al. 2024

Minerals

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Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation presents challenges in controlling this mechanism and achieving optimal efficiency. Consequently, there is a growing dependence on machine learning methods in mineral processing research. This paper provides a comprehensive overview of machine learning and artificial intelligence techniques, presenting their potential applications in flotation processes. The review demonstrates advancements discussed in scholarly research over the past decade and highlights a growing interest in utilizing machine learning methods for monitoring and optimizing flotation processes, as demonstrated by the increasing number of studies in this field. Recent trends also suggest that the course of flotation process monitoring, and control will increasingly focus on the refinement and deployment of deep learning networks developed specifically for froth image extraction and analysis.

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Artificial intelligence for enhanced flotation monitoring in the mining industry: A ConvLSTM-based approach

Cited by 15 publications

References 30 publications

Convolutional Long Short-Term Memory (ConvLSTM)-Based Prediction of Voltage Stability in a Microgrid

Convolutional Long Short-Term Memory (ConvLSTM)-Based Prediction of Voltage Stability in a Microgrid

Scalable Compositional Digital Twin-Based Monitoring System for Production Management: Design and Development in an Experimental Open-Pit Mine

Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

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