Real-Time Water Quality Monitoring via Impedance Spectroscopy and Machine Learning

Aybar, Oğuzhan; Kara, Zeynep; Yücel, Meriç; Üstündağ, Burak Berk

doi:10.1109/agro-geoinformatics262780.2024.10660726

2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 2024

DOI: 10.1109/agro-geoinformatics262780.2024.10660726

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Real-Time Water Quality Monitoring via Impedance Spectroscopy and Machine Learning

Oğuzhan Aybar,

Zeynep Kara,

Meriç Yücel

et al.

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Determination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy

Ma,

Fischerauer,

Haacke

et al. 2024

Sensors

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Molding sand mixtures in the foundry industry are typically composed of fresh and reclaimed sands, water, and additives such as bentonite. Optimizing the control of these mixtures and the recycling of used sand after casting requires an efficient in-line monitoring method, which is currently unavailable. This study explores the potential of an AI-enhanced electrical impedance spectroscopy (EIS) system as a solution. To establish a fundamental dataset, we characterized various sand mixtures containing quartz sand, bentonite, and deionized water using EIS in the frequency range from 20 Hz to 1 MHz under laboratory conditions and also measured the water content and density of samples. Principal component analysis was applied to the EIS data to extract relevant features as input data for machine learning models. These features, combined with water content and density, were used to train regression models based on fully connected neural networks to estimate the bentonite content in the mixtures. This led to a high prediction accuracy (R2 = 0.94). These results demonstrate that AI-enhanced EIS has promising potential for the in-line monitoring of bulk material in the foundry industry, paving the way for optimized process control and efficient sand recycling.

show abstract

Determination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy

Ma,

Fischerauer,

Haacke

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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Real-Time Water Quality Monitoring via Impedance Spectroscopy and Machine Learning

Cited by 1 publication

References 25 publications

Determination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy

Determination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy

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