Wastewater Treatment with Carbon-Based Nanomaterials Enhanced by Real-Time Machine Learning Based Monitoring
Year: 2025
Type: article
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.
DOI: 10.14419/drek9d11
Language: English
Subfield: Waste Management and Disposal
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413340886