| Title | Exploration of material recovery framework from waste - A revolutionary move towards clean environment |
|---|---|
| ID_Doc | 15570 |
| Authors | Arun, M; Barik, D; Chandran, SSR |
| Title | Exploration of material recovery framework from waste - A revolutionary move towards clean environment |
| Year | 2024 |
| Published | |
| Abstract | Reduce environmental impacts and guarantee a steady supply of critical chemicals by practising sustainable waste management and chemical production. By advancing circular economy ideas and decreasing dependency on finite resources, this research has the potential to alter the industrial landscape radically. The technological, economic, regulatory, and social barriers to waste material recovery and chemical production are explored in this paper. The key to resolving these issues is the identification of solutions that are both economically viable and environmentally benign. This paper introduces the sustainable Chemical Production and Waste Material Recovery Framework (CP&WMRF), which incorporates innovative recycling and upcycling methods, innovative chemical manufacturing processes, and the incorporation of digital technologies like artificial intelligence (AI) and machine learning (ML) to maximize the efficiency with which resources are employed. It is possible to reduce waste and energy use in the production of Interfaces with the help of CP&WMRF. Chemicals can be manufactured using sustainable feedstocks as an alternative to fossil fuels. The system standardizes how e-waste can be recycled and recovered metals and materials can be used. To prove the viability and efficiency of these methods, they require innovative simulation and modeling tools. The assessments help decision-makers understand the benefits and drawbacks of the proposed technologies in terms of their performance, environmental effect, and economic viability. When pitted against AI-ML, which achieved 94.2 %, CP&WMRF's 96.2 % result reveals a significant edge. AI-ML is less efficient, with a score of 93.8 %. The field of sustainability analysis, with a score of 95.2 %, is higher than AI-ML's decent lower score of 93.2 %. The impressive 97.5 % score of CP&WMRF in terms of resource efficiency substantially surpasses the 92.8 % score ascribed to AI-ML. The remarkable success of CP&WMRF in optimizing waste recovery, with a score of 98.7 %, higher than the 91.5 % associated with AI-ML. The present research establishes the framework for a revolutionary move toward circular and green chemistry by integrating innovative methods, all-encompassing applications, and rigorous simulation analysis. |
| https://doi.org/10.1016/j.ceja.2024.100589 |
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