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Performance of polyvinyl alcohol graphene oxide membrane for microplastic removal in wastewater with an IoT based monitoring approach
Summary
Researchers developed a polyvinyl alcohol-graphene oxide membrane combined with IoT-based real-time monitoring to improve microplastic removal from wastewater treatment plants. Conventional treatment plants in the study achieved only about 84% microplastic removal, leaving significant amounts entering natural water bodies. The membrane filtration system coupled with continuous monitoring showed promise for improving microplastic capture rates in wastewater treatment.
In this study, microplastics (MPs) source, movement and final disposition in wastewater treatment plants (WWTPs) and its ultimate release into natural water bodies are investigated. It seeks to exploit this to devise a systematic procedure for identifying the presence of MPs, the primary and secondary sources of MPs, and their behavior in wastewater. It also discusses pre-treatment processes, analytical methods, multiple sampling methods, among others, for quantifying microplastics in WWTPs. Real time operation-based experimental results indicate that the average removal efficiency of microplastics in the evaluated WWTPs was around 84%. Real time monitoring and analysis of the WWTPs was conducted in Erode where WWTPs were present in study. In this, we have implemented an IoT based system, for monitoring water quality using Data Aggregator Server in the cloud running task applications. The findings suggest that WWTPs are effective in removing a large proportion of microplastics, however smaller particles with low density are harder to remove through traditional treatment process means and end up in the environment. Therefore, membrane filtration and advanced oxidation processes may be utilized to overcome microplastic removal, and cost-effective solutions are important. Also, the research explores modification of existing polymer-based membranes, we modified PSF and GO membranes incorporating PVA or GO for improved efficiency with good economics. Real-time experimental outcomes were closely matched by the data analysis of the cloud-based virtual model for removal efficiency.