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Priyanto, Aan, Hapidin, Dian, Edikresnha, Dhewa, Khairurrijal, Khairurrijal. (1405). Enhancing Microplastic Removal Efficiency Through Fe-based Coagulation: Insights from Response Surface Methodology and Machine Learning. , 12(2), 533-544. doi: 10.22059/poll.2026.406372.3196
Aan Priyanto; Dian Ahmad Hapidin; Dhewa Edikresnha; Khairurrijal Khairurrijal. "Enhancing Microplastic Removal Efficiency Through Fe-based Coagulation: Insights from Response Surface Methodology and Machine Learning". , 12, 2, 1405, 533-544. doi: 10.22059/poll.2026.406372.3196
Priyanto, Aan, Hapidin, Dian, Edikresnha, Dhewa, Khairurrijal, Khairurrijal. (1405). 'Enhancing Microplastic Removal Efficiency Through Fe-based Coagulation: Insights from Response Surface Methodology and Machine Learning', , 12(2), pp. 533-544. doi: 10.22059/poll.2026.406372.3196
Priyanto, Aan, Hapidin, Dian, Edikresnha, Dhewa, Khairurrijal, Khairurrijal. Enhancing Microplastic Removal Efficiency Through Fe-based Coagulation: Insights from Response Surface Methodology and Machine Learning. , 1405; 12(2): 533-544. doi: 10.22059/poll.2026.406372.3196

Enhancing Microplastic Removal Efficiency Through Fe-based Coagulation: Insights from Response Surface Methodology and Machine Learning

Pollution
دوره 12، شماره 2، مرداد 2026، صفحه 533-544    اصل مقاله (1.74 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22059/poll.2026.406372.3196
نویسندگان
Aan Priyanto1؛ Dian Ahmad Hapidin* 2؛ Dhewa Edikresnha1؛ Khairurrijal Khairurrijal1
1Research 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
2Department of Physics, Faculty of Science, Institut Teknologi Sumatera, Jalan Terusan Ryacudu, Lampung Selatan, Lampung 35365, Indonesia
چکیده
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.
کلیدواژه‌ها
Microplastics؛ Coagulation؛ Iron-Based Coagulant؛ Response Surface Methodology؛ Machine Learning؛ Removal Efficiency
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