Activated recovery of PVC from contaminated waste extension cord-cable using a weak acid. (September 2022)

• Record Type: Journal Article
• Authors: Jia, Chunmiao; Das, Pallab; Zeng, Qiang; Gabriel, Jean-Christophe P.; Tay, Chor Yong; Lee, Jong-Min
• Is Part Of: Chemosphere. Volume 303:Part 1(2022)
• Publication Date: 2022-09
• Subjects: Toxic metals -- Microwave -- Extraction -- Machine learning -- Lead -- Polyvinyl chloride -- Plastic recovery; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2022.134878
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Waste electronic and electrical equipment are complex mixtures of valuable and/or toxic materials, which pose serious challenges in their recycling or disposal, for example, electrical transmission wires insulated in polyvinyl chloride materials. These materials are frequently found contaminated with toxic chemical elements, such as Pb, Hg, Cr, or Cd, and are discarded without decontamination. To resolve this problem, we developed a microwave-assisted extraction process to remove toxic metals from plastic e-waste. We processed diluted (30 wt%) citric acid at 210 °C for 1 h inside a pressurized vessel heated by microwave, and found it was suitable not only for the extraction of the toxic metals (∼100%) but also for a significant plastic recovery (>50 wt%). To predict an optimized process window, the support vector regression machine learning algorithm was applied, which reduced the amount of experimentation required while still giving accurate results. Conditions optimized for the reference sample also led to maximum extraction of toxic metals from real-life extension cord waste. We also report that the recovered plastic's properties remained intact after the extraction. Graphical abstract: Image 1 Highlights: High level of toxic metal (lead) was found in waste extension cord-cable samples. Microwave-assisted extraction was demonstrated to completely remove toxic metal. Diluted citric acid was found to be more effective than strong acids. Accurate process optimization prediction was generated using machine learning. More than 50 wt% plastic recovery was achieved.