Aan Priyanto, Dian Ahmad Hapidin, Dhewa Edikresnha, Khairurrijal Khairurrijal
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. © The Author(s). The University of Tehran Press.
Research Group of Physics and Technology of Advanced Materials, Department of Physics, Institut Teknologi Bandung, Jalan Ganesa No. 10, Jawa Barat, Bandung, 40132, Indonesia; Department of Physics, Faculty of Science, Institut Teknologi Sumatera, Jalan Terusan Ryacudu, Lampung Selatan, Lampung, 35365, Indonesia