Enhancing Microplastic Removal Efficiency Through Fe-based Coagulation: Insights from Response Surface Methodology and Machine Learning | ||
| Pollution | ||
| دوره 12، شماره 2، تابستان 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 | ||
| مراجع | ||
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