Wastewater Treatment with Carbon-Based NanomaterialsEnhanced by Real-Time Machine ‎Learning Based Monitoring

  • Authors

    • Srinivasa Chanakya Muramshetti Senior Software Engineer (Photon InfoTech Inc), Irving, Texas 750392
    • Kishore Konel Professor of Business Analytics, Loyola Institute of Business Administration, Chennai, Tamil Nadu, India
    • R. Murugadoss Professor, Department of Artificial Intelligence and Data Science, V.S.B. College of Engineering, Technical Campus, Coimbatore, ‎Tamil Nadu, India
    • Vairavel Madeshwaren Department of Agriculture Engineering, Dhanalakshmi Srinivasan College of Engineering, Coimbatore, Tamil Nadu, India
    https://doi.org/10.14419/drek9d11

    Received date: June 27, 2025

    Accepted date: August 6, 2025

    Published date: August 16, 2025

  • Carbon Nanomaterials; Wastewater Treatment; Anomaly Detection; Machine Learning; TCN; Random Forest

  • Abstract

    Intelligent adaptive control is required for the effective treatment of wastewater using carbon-based nanomaterials due to the dynamic and ‎complex nature of pollutant removal mechanisms. This study improves anomaly detection and predictive monitoring in treatment systems ‎that use carbon derivatives such as graphene oxide (GO) and graphite activated carbon (AC) by applying machine learning (ML) ‎techniques. A treatment facility in Maharashtra supplied operational data from 2023 to 2025, including 18,250 daily records by combining ‎real-time sensor data with laboratory analysis during that time. The zeta potential of the heavy metals BOD and COD, the dosage of the ‎nanomaterials, the pH, contact time, and the turbidity were all significant factors. Measurement tools like ICP-OES, UV-VIS ‎spectrophotometers, and SCADA-GIS interface turbidity meters were used to guarantee high temporal and spatial granularity. Temporal ‎patterns and operational anomalies were recorded using machine learning models called Random Forest Regressor, Autoencoders, and ‎Temporal Convolutional Networks (TCNs). Autoencoders identified deviations such as membrane fouling. Random Forests identified ‎significant factors influencing treatment efficacy, and TCNs effectively modeled trends in pollutant concentrations. An ensemble modeling ‎approach was applied to improve prediction robustness. MAPE, NRMSE, and precision-recall metrics were used to assess the model. The ‎results demonstrated increased operational reliability, more accurate fault detection, and more informed dosage selections. Ultimately, ‎complementary techniques like optical microscopy and scanning electron microscopy (SEM) were used to describe these nanomaterials in ‎order to confirm their surface morphology, elemental composition, and functional integrity. Thus, the machine learning-integrated framework ‎promotes data-driven, environmentally friendly wastewater treatment using cutting-edge carbon nanomaterials‎.

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  • How to Cite

    Muramshetti, S. C. ., Konel, K. ., Murugadoss , R. ., & Madeshwaren, V. (2025). Wastewater Treatment with Carbon-Based NanomaterialsEnhanced by Real-Time Machine ‎Learning Based Monitoring. International Journal of Basic and Applied Sciences, 14(4), 488-498. https://doi.org/10.14419/drek9d11