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We live in a world in which the generation and accumulation of trash is commonplace. Yet it’s clear that taking, making, using and discarding is an unsustainable economic strategy. This pattern devours finite natural resources and blights our health and environment.
In this collection, we gather research into engineering approaches to address post consumer waste, that is, consumer product waste once it has reached the end of use. We focus on chemical, materials, mechanical and other engineering advances tackling solid waste streams, such as food, paper and textiles, electronics and plastics.
Specific areas of interest include (but are not limited to):
Recyclable, renewable and degradable materials and device design
Advances in mechanical, chemical, enzymatic or other treatment of waste streams towards useful products (recycling, upcycling, engineered re-purposing.)
Approaches to device, materials or elemental recovery from waste streams
Methods for assessing impacts of interventions
Reverse logistics and infrastructure engineering for waste flow management
Xin Xiong and colleagues estimate end-of life mass predictions of aerospace and rail vehicles in China and predict waste accumulation between 2000 and 2050. The study will aid in developing and managing effective technological solutions for a circular economy as well as formulating plans for governance.
Kevin Wyss and colleagues report the flash synthesis of graphene from end-of-life vehicle plastic waste. A polyurethane/flash graphene composite is also re-flashed back into more graphene. A life cycle assessment suggests environmental benefits compared to other graphene synthetic routes.
Jiaqi Lu et al. apply an asynchronous-parallel recurrent neural network to predict the yield of separating copper and poly(vinyl chloride) components from cable waste by ball milling. The modelling approach could guide the process design to maximise material recovery.
Modelling waste biorefining processes can be difficult due to variability of feedstocks, making process optimization challenging. To address these uncertainties, Ji Gao and colleagues introduced a reinforcement learning-based framework, using a model anaerobic digestion process to demonstrate their control methods.