Development of a soft sensor for fouling prediction in pipe fittings using the example of particulate deposition from suspension flow

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@Article{jarmatz:2024:fbp,

• author = "Niklas Jarmatz and Wolfgang Augustin and Stephan Scholl and Alberto Tonda and Guillaume Delaplace",
• title = "Development of a soft sensor for fouling prediction in pipe fittings using the example of particulate deposition from suspension flow",
• journal = "Food and Bioproducts Processing",
• year = "2024",
• volume = "145",
• pages = "116--127",
• keywords = "genetic algorithms, genetic programming, PySR, Food processing, Fouling, Cleaning, Sustainability, Machine learning, Dimensional analysis",
• ISSN = "0960-3085",
• URL = "https://www.sciencedirect.com/science/article/pii/S0960308524000245",
• DOI = "doi:10.1016/j.fbp.2024.02.009",
• code_url = "https://github.com/albertotonda/soft-sensor-pipe-fouling",
• abstract = "Fouling is the unwanted accumulation of material on a processing surface which is an especially problematic issue in the food industry. Characterising or predicting fouling through traditional methods or models is a challenge due to the complexity of fouling mechanisms. Machine learning (ML) techniques can overcome this challenge by creating models for prediction directly from experimental data. Unfortunately, the results can be hard to interpret depending on the algorithm. Here, a soft sensor is generated from an extensive data set to predict the fouling of a model particle material system. This is performed inside two different pipe fittings, an inaccessible and accessible fitting (e.g., for sensor measurements). Additionally, dimensional analysis (DA) is conducted to identify the correlations responsible for fouling while keeping descriptors with physical meaning. The resulting dimensionless numbers (DNs) are further processed by three ML algorithms: linear regression (LR), symbolic regression (SR), and random forest (RF). The soft sensor generated using a RF outperformed the other two regressors for the dimensional (Q2=0.90 p/m 0.08) and for the dimensionless data (Q2=0.88 p/m 0.09). The parameter time and particle mass fraction were determined to be most influential. Furthermore, seven DNs were obtained allowing a reduced experimental design.",
• notes = "also known as \cite{JARMATZ2024116}",

}

Genetic Programming entries for Niklas Jarmatz Wolfgang Augustin Stephan Scholl Alberto Tonda Guillaume Delaplace

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