AI-Enabled Sensors and Analytical Chemistry for Resource Recovery: Trends and Frontiers in the Circular Economy
DOI:
https://doi.org/10.64229/96wxng32Keywords:
Artificial intelligence, Sensors, Analytical chemistry, Resource recovery, Circular economy, Digitalization, Sustainable technologiesAbstract
Shifting to a circular economy requires new ways to recover materials from waste. Waste must be reimagined as a source of value. The core of this change is analytical chemistry, sensor technologies, which detect, quantify and characterise critical resources in a variety of waste streams. Artificial intelligence or AI has become a transformative enabler recently to provide sophisticated solutions for data-driven modelling, predictive analytics, and process optimisation. This review describes the AI-based sensor and analytical chemistry techniques applications in water and wastewater treatment, energy recovery, recovery of critical materials, plastics recycling and biomass valorization. Focus on spectroscopy, chromatography, electroanalysis, and hybrid techniques boosted by machine learning. AI-based sensors development for real-time monitoring and classification also will be discussed and showcased. In addition, the authors point out that digitalization, automation and smart platforms (i.e., IoT integration, cloud-based analytics, digital twins) can lead to fully autonomous recovery systems. It critically examines data quality, reproducibility and standardization, and AI’s energy demand sustainability paradox. In the future, scientists will have next-generation AI models, autonomous laboratories, and quantum computing that will speed up analytical chemistry for resource recovery. This work integrates chemistry, engineering, data science, and policy to support the idea that AI can facilitate a circular economy as well as sustainable resource use and global environmental accountability.
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