Disassembly Without a Playbook

Individual technologies exist for e-waste component liberation — thermal desoldering (96% w/w efficiency), froth flotation (76% metal yield at 95% recovery), crushing, acid leaching — but no optimization-based conceptual design framework integrates these into complete processing pathways. Operators must choose between manual disassembly (expensive but preserves reusable components) and automated crushing (cost-effective but generates metal dust losses and destroys reuse potential), with no systematic method for determining the optimal sequence for a given waste stream. Process systems engineering studies on waste printed circuit board (WPCB) recovery are described as "scarce" despite commercial operations processing 80+ kilotonnes per year.

The gap between individual unit operations and integrated processing pathways means that commercial e-waste recyclers operate ad hoc, optimizing individual steps rather than the full recovery chain. This results in suboptimal overall recovery — metals that could be captured by one pathway are lost because an earlier step in the current sequence made them unrecoverable. As electronics become more complex (multi-layer PCBs, system-in-package designs), the number of possible disassembly and processing sequences grows combinatorially, making intuition-based optimization increasingly inadequate.

Chemical desoldering generates secondary pollution (waste acids, alkaline liquids, sludge). Particle size optimization is determined empirically: 2–5mm is optimal for component liberation, but further reduction diminishes recovery — yet no predictive model links particle size to recovery yield across different waste stream compositions. Digital product information that could guide disassembly is "often lacking at End-of-Life," making fit-to-resource disassembly instructions labor-intensive to generate for each batch. Of 65 publications on Design for Disassembly, the greatest knowledge gaps are in disassembly process optimization (versus design principles), indicating that the academic community has focused on designing for future disassembly rather than optimizing the disassembly of today's products.

Process systems engineering approaches — superstructure optimization, process synthesis algorithms — already used in chemical plant design could be adapted for e-waste processing pathway optimization. Digital product passports recording material composition and assembly sequence would enable automated disassembly planning. Machine vision-guided robotic disassembly trained on common electronic component types could bridge the gap between manual precision and automated throughput.

A team could model a specific e-waste stream (e.g., smartphones or laptop PCBs) as a process synthesis problem, using published unit operation data to optimize the disassembly-and-recovery sequence for maximum total metal recovery at minimum cost. A robotics team could prototype a vision-guided system that identifies and separates specific components (batteries, capacitors, connectors) from a mixed PCB waste stream. Relevant disciplines: chemical engineering, process systems engineering, robotics, computer vision.