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AI-assisted Microplastics Removal
Summary
This review explored how artificial intelligence is being used to improve the detection and removal of microplastics from water and the environment. Researchers found that machine learning techniques can enhance the identification of microplastic particles and optimize treatment processes like filtration and coagulation. The study suggests that AI-driven approaches could overcome many of the efficiency and cost limitations of conventional microplastic removal methods.
Microplastics (MPs) pose significant risks to aquatic ecosystems and human health due to their widespread distribution, persistence, and toxicity. Conventional methods for MPs detection and removal such as membrane filtration, adsorption, and coagulation, face lots of challenges including inefficiency, high costs, and secondary pollution. Artificial intelligence (AI) has emerged as a transformative tool and offers new solutions for optimizing detection and removal processes. By integrating machine learning (ML) techniques, AI enhances the identification of MPs, improves predictive modeling for filtration systems, and facilitates the optimization of operational parameters to maximize efficiency. This review provides an overview of AI applications in MPs detection, filtration, and performance prediction, highlighting the potential of AI to address current limitations and pave the way for innovative, environmentally sustainable solutions.