MI-NiDIA: A scalable framework for modeling flocculation kinetics and floc evolution in water treatment

This paper presents a scalable framework for modeling floc evolution and flocculation kinetics in water treatment. Unlike the existing methods that subjects Non-intrusive Dynamic Image Analysis (NiDIA) data to complex mathematical concepts, the proposed software devised a scaling concept for NiDIA d...

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Bibliographic Details
Published in:Software impacts Vol. 20; p. 100662
Main Authors: Bankole, Abayomi O., Moruzzi, Rodrigo, Negri, Rogério G., Oishi, Cassio M., Bankole, Afolashade R., James, Abraham O.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2024
Subjects:
Flocculation kinetics
Machine learning
Non-intrusive image analysis
Smart water treatment
Non-intrusive image analysis
Smart water treatment
Flocculation kinetics
Machine learning
ISSN:2665-9638, 2665-9638
Online Access:Get full text
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Summary:This paper presents a scalable framework for modeling floc evolution and flocculation kinetics in water treatment. Unlike the existing methods that subjects Non-intrusive Dynamic Image Analysis (NiDIA) data to complex mathematical concepts, the proposed software devised a scaling concept for NiDIA data and designed an effective algorithm with the capability to predict varying floc lengths and the underlying kinetics under a broad flocculation conditions (Gf and Tf). Technically, the designed machine-intelligence framework (MI-NiDIA) involves data preprocessing, automatic parameter selection, validation and prediction of floc length evolution with metrics. For instance, MI-NiDIA-MLP recorded R2 of 0.95–1.0 for varying floc length at Gf60s−1. •Proposed algorithm models floc evolution and flocculation kinetics with time-series.•Algorithm scales limited non-intrusive dynamic image analysis flocculation dataset.•Source code utilizes basic and well-supported Python modules.•The framework is compatible with other neural network algorithms. [Display omitted]
ISSN:2665-9638
2665-9638
DOI:10.1016/j.simpa.2024.100662