Machine-Learning-Assisted Development of Polymer-Biochar Composite Adsorbents for the Removal of Heavy Metals from Gomti River Water

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Year : 2026 | Volume : 14 | 04 | Page :
    By

    Nidhi Singh,

  • Smita Tung,

  1. Research Scholar, Department of Civil Engineering, GLA University, Mathura, Uttar Pradesh,
  2. Assistant Professor, Department of Civil Engineering, GLA University, Mathura, Uttar Pradesh, India

Abstract

Rapid urbanization, industrial discharge, and agricultural runoff pose a significant threat to freshwater sustainability and public health. Within these ecosystems, polymer pollutants—such as microplastics, nanoplastics, synthetic fibres, and additive residues—have emerged as persistent vectors capable of adsorbing and transporting toxic heavy metals. Because these polymeric contaminants dynamically interact with conventional aquatic parameters to alter pollutant mobility and ecological risk profiles, there is an urgent need to transition from passive environmental monitoring to active, materials-driven remediation. To address this challenge, this study presents the development and machine-learning-assisted performance evaluation of a novel polymer-biochar composite adsorbent designed for the removal of heavy metals from complex aquatic matrices. The Gomti River Basin was utilized as a real-world environmental testbed. A dataset comprising 100 water samples collected from five representative locations, characterized by 18 physicochemical and heavy-metal parameters, served as the competing ionic background matrix. To predict the composite’s adsorption efficiency under varying, non-linear riverine conditions, a comparative assessment of machine learning (ML) models—Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)—was conducted. A standardized 70:30 train–test split combined with nested 10-fold cross-validation was employed to ensure robust model development. Among the investigated models, the ANN demonstrated superior predictive capability for evaluating the composite’s performance against matrix interferences, achieving a testing R² of 0.99, RMSE of 0.05, and 92.5% of predictions falling within ±20% of observed values. From a physical chemistry perspective, the ML framework identified that elevated Electrical Conductivity (EC), Total Dissolved Solids (TDS), and sulfate concentrations act as the dominant variables influencing the electrical double layer, surface complexation, and biosorption capacity of the polymeric material. Ultimately, these findings demonstrate that integrating green polymer-composite design with data-driven ML modelling provides a highly effective pathway for optimizing nanoparticle-enabled water-treatment technologies, thereby advancing the goals of circular polymer science and sustainable aquatic management.

Keywords: Polymeric contaminants, Micro plastics, Nano plastics, Water Quality Index, Machine Learning; Gomti River Basin, Biopolymers; Nanoparticles; Biosorption; Green composites; River water quality.

How to cite this article:
Nidhi Singh, Smita Tung. Machine-Learning-Assisted Development of Polymer-Biochar Composite Adsorbents for the Removal of Heavy Metals from Gomti River Water. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL:
Nidhi Singh, Smita Tung. Machine-Learning-Assisted Development of Polymer-Biochar Composite Adsorbents for the Removal of Heavy Metals from Gomti River Water. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=248262


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Ahead of Print Subscription Original Research
Volume 14
04
Received 26/06/2026
Accepted 30/06/2026
Published 30/06/2026
Publication Time 4 Days
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