Enhancing microplastic removal efficiency through Fe-based coagulation: insights from response surface methodology and machine learning

Articles in Press

Document Type : Original Research Paper

Authors

1 Research Group of Physics and Technology of Advanced Materials, Department of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung Jawa Barat 40132, Indonesia

2 Research Group of Physics and Technology of Advanced Materials, Departement of Physics, Bandung Institute of Technology, Indonesia.

10.22059/poll.2026.406372.3196

Abstract

Microplastic pollution poses a major global environmental threat, demanding effective removal strategies. Coagulation is among the most practical methods due to its cost efficiency, simplicity, and high performance, with iron-based (Fe-based) coagulants showing particular environmental and operational advantages. However, integrated approaches combining statistical and machine learning optimization for different microplastic types and sizes remain limited. This study applied a hybrid Response Surface Methodology (RSM) and machine learning framework to optimize Fe-based coagulation for polyethylene terephthalate (PET), polyethylene (PE), and polypropylene (PP) microplastics of various sizes. A Box–Behnken design (15 runs per polymer) was used, totaling 135 experiments. Removal efficiency was quantified gravimetrically after floc separation and drying. The optimized process achieved a maximum removal efficiency of (94.9 ± 0.2)%, comparable to many previous reports. RSM yielded the lowest mean prediction error (1.80%), surpassing Linear Regression (2.74%) and Artificial Neural Network (5.02%) models trained using k-fold cross-validation to mitigate overfitting. Coagulant dose was identified as the most influential variable, followed by polyacrylamide (PAM) dose and pH. These findings provide a robust, data-driven framework for optimizing microplastic coagulation and highlight key operational factors governing efficient removal.

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Main Subjects

Pollution

Articles in Press, Accepted Manuscript
Available Online from 19 April 2026

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