LAPSE:2025.0423
Published Article
LAPSE:2025.0423
Leveraging Machine Learning for Real-Time Performance Prediction of Near Infrared Separators in Waste Sorting Plant
June 27, 2025
Abstract
Many small and medium enterprises (SME) often fail to fully utilize the data they collect due to a lack of technical expertise. The ecoKI platform, a low-code solution that simplifies machine learning application for SMEs, showed a promising answer to the challenge. This study explores the application of ecoKI platform to design process monitoring tools for waste sorting plants. NIR separator data were processed through ecoKIs building blocks to train two neural network architecturesMLP and LSTMfor predicting NIR separation efficiency. The results showed that the models accurately predicted NIR output and effectively identified regions where NIR separation performance declined, demonstrating the potential of data-driven approaches for real-time performance monitoring. This work highlights how SMEs can leverage existing data for operational efficiency and decision-making, offering an accessible solution for industries with limited machine learning expertise. The approach is adaptable to various industrial contexts, paving the way for future advancements in automated, data-driven optimization of equipment performance.
Many small and medium enterprises (SME) often fail to fully utilize the data they collect due to a lack of technical expertise. The ecoKI platform, a low-code solution that simplifies machine learning application for SMEs, showed a promising answer to the challenge. This study explores the application of ecoKI platform to design process monitoring tools for waste sorting plants. NIR separator data were processed through ecoKIs building blocks to train two neural network architecturesMLP and LSTMfor predicting NIR separation efficiency. The results showed that the models accurately predicted NIR output and effectively identified regions where NIR separation performance declined, demonstrating the potential of data-driven approaches for real-time performance monitoring. This work highlights how SMEs can leverage existing data for operational efficiency and decision-making, offering an accessible solution for industries with limited machine learning expertise. The approach is adaptable to various industrial contexts, paving the way for future advancements in automated, data-driven optimization of equipment performance.
Record ID
Keywords
Machine Learning in Waste Management, Performance Monitoring, Waste Sorting Automation
Subject
Suggested Citation
Iqbal IM, Wang X, Viedt I, Urbas L. Leveraging Machine Learning for Real-Time Performance Prediction of Near Infrared Separators in Waste Sorting Plant. Systems and Control Transactions 4:1688-1693 (2025) https://doi.org/10.69997/sct.130911
Author Affiliations
Iqbal IM: Technische Universität Dresden, Process-to-Order Group, Chair of Process Control Systems, Dresden, Germany
Wang X: Technische Universität Dresden, Process-to-Order Group, Chair of Process Control Systems, Dresden, Germany
Viedt I: Technische Universität Dresden, Process-to-Order Group, Chair of Process Control Systems, Dresden, Germany; Technische Universität Dresden, Process-to-Order Group, Process Systems Engineering Group, Dresden, Germany
Urbas L: Technische Universität Dresden, Process-to-Order Group, Chair of Process Control Systems, Dresden, Germany; Technische Universität Dresden, Process-to-Order Group, Process Systems Engineering Group, Dresden, Germany
Wang X: Technische Universität Dresden, Process-to-Order Group, Chair of Process Control Systems, Dresden, Germany
Viedt I: Technische Universität Dresden, Process-to-Order Group, Chair of Process Control Systems, Dresden, Germany; Technische Universität Dresden, Process-to-Order Group, Process Systems Engineering Group, Dresden, Germany
Urbas L: Technische Universität Dresden, Process-to-Order Group, Chair of Process Control Systems, Dresden, Germany; Technische Universität Dresden, Process-to-Order Group, Process Systems Engineering Group, Dresden, Germany
Journal Name
Systems and Control Transactions
Volume
4
First Page
1688
Last Page
1693
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 1688-1693-1227-SCT-4-2025, Publication Type: Journal Article
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LAPSE:2025.0423
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https://doi.org/10.69997/sct.130911
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[v1] (Original Submission)
Jun 27, 2025
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References Cited
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