Random Forest and Autoencoder Data-Driven Models for Prediction of Dispersed-Phase Holdup and Drop Size in Rotating Disc Contactors

Random Forest and Autoencoder Data-Driven Models for Prediction of Dispersed-Phase Holdup and Drop Size in Rotating Disc Contactors

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Veröffentlicht in:Industrial & engineering chemistry research 2021-01, Vol.60 (1), p.425-435
Hauptverfasser: Saraswathi K., Swetha, Bhosale, Hrushikesh, Ovhal, Prasad, Parlikkad Rajan, Naren, Valadi, Jayaraman Krishnamoorthy
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container_title Industrial & engineering chemistry research
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creator Saraswathi K., Swetha
Bhosale, Hrushikesh
Ovhal, Prasad
Parlikkad Rajan, Naren
Valadi, Jayaraman Krishnamoorthy
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doi_str_mv 10.1021/acs.iecr.0c04149
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title Random Forest and Autoencoder Data-Driven Models for Prediction of Dispersed-Phase Holdup and Drop Size in Rotating Disc Contactors
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