The grading of equipment (e.g., mobile phone, laptops, desktops, monitors etc) is an important step in our process to establish the condition prior to sale. The grade of the equipment is based on both function and form/condition.

Objectives:

TES would like to explore the possibility of automating whole or part of the current process. Some of the questions to be posed include:

1. Can 3D image capture and AI be used to grade function and form?
2. Can individual item grading be achieved at scale?
3. Can technologies go beyond grading to determine the quantum of repair or refurbishment required?
4. Could the image include barcode scan to capture key info such as age, specification and functionality as well as the cosmetic condition (perfect, cracked screen, major scratch, minor scratch, etc. ?

Currently TES receives a large amount of non-homogenous lithium-ion mobile phone batteries that need to be discharged prior to shredding. Non-discharged lithium-ion batteries represent a significant combustion risk if they are damaged or tampered. A damaged battery cell can go into thermal runaway, producing enough heat to cause adjacent battery cells to also go into thermal runaway. This produces a fire that repeatedly flares up as each battery cell in turn ruptures and releases its contents. These types of fires are extremely dangerous, life-threatening, and difficult to extinguish.

The broad range of non-homogenous lithium-ion batteries received also makes it difficult to define a standard process.

Objectives:

TES would like to explore the possibility of automating this process.

There are efficiency and risk mitigation benefits to be gained by establishing a timely, low risk and automated process. Some of the questions to be posed include:

1. Can a lithium-ion battery be efficiently and effectively discharged using a chemical process?
2. Can a shredder be designed to safely accommodate thermal events during shredding of charged lithium-ion batteries?
3. Could individual batteries be discharged by connecting to terminals? Would need AI vision to identify shape of battery, find terminals and start discharge. However, we do process thousands of these type of batteries per day, so the process has to be fast.
4. With an efficient discharge process could the energy be captured to be put back into the grid.

Currently, TES receives a large amount of electrical and electronic equipment in their original packing in pallets or boxes. These pallets need to be unpacked and sorted into unique waste streams.

Some unique products will then have to be further de-manufactured, disassembled or dismantled into their individual components materials which may involve the removal of casings, screws, etc.

The current process is manual and labour intensive, and therefore time consuming, costly and error prone. The effectiveness and efficiency are also currently heavily dependent on the competence and capability of individuals.

Objectives:

TES would like to explore automating whole or part of the current process to improve efficiency. As there is a broad range of non-homogenous packaging, materials and products received, they are looking to define a standard process for this.

TES has identified some potential solutions to this problem.

1. Can the unpacking process use robotics to unpack and de-box?
2. Can the unpacked materials and products be automatically sorted based on material type, and value? Consulting up to date pricing of the materials would increase value recovery.
3. Can robotics and artificial intelligence be used during the de-manufacture process?
4. Can we also use robotics to manage sortation of the packaging materials?
5. Since removing screws from cases and interior is one of the most common de-manufacturing tasks, could a robotic system be developed that identifies the screws and removes them?