RECYCLING: AUTOMATING THE SORTING AND
RECYCLING: AUTOMATING THESORTING AND SEPARATION OF E-WASTEINDUSTRIAL, CONSUMER, ENERGY
RECYCLING: AUTOMATING THE SORTING AND SEPARATION OF E-WASTEWHITE PAPEREXECUTIVE SUMMARYConverting e-waste back to raw materials offers a cost-efficient alternativeto mining virgin materials, particularly as they become harder and moreexpensive to source. Resource bottlenecks drive up raw material pricesand can inhibit innovation cycles.In this paper we focus on the biggest challenges the e-wasteprocessing industry is facing. We examine how thesechallenges could be overcome by leveraging technologiessuch as machine vision, machine learning, augmented realityand robotic systems. We also present a vision for how thesetechnologies will be brought to the market through a stepwiseprogression.01
WHITE PAPER1.RECYCLING: AUTOMATING THE SORTING AND SEPARATION OF E-WASTEINTRODUCTIONThe availability of critical, virgin materials for the fabricationof new high-tech products is a major supply chain issue, asEuropean and North American manufacturing is dependenton imports of virgin materials to fuel their economies. Rareearths, for example, are either mined in politically unstablecountries, or in countries that have sporadically applied exportrestrictions1. Consequently, commodity price fluctuations putadditional commercial pressures on OEMs and reduce theirmargins. The conversion of waste streams back into rawmaterials offers an opportunity for OEMs to radically reducethis dependency, while extracting additional value from theirsold products that have reached end-of-life (EoL). To do so,technological innovation and optimisation of the recyclingvalue-chain is needed.Overall the e-waste recycling value-chain consists of threemajor stages, see Figure 1: E-waste collection Mechanical pre-processing (device dismantling and/orshredding, sorting of e-waste fractions)COLLECTION End-processing/Refinement (refining of e-waste fractionsinto raw materials)The key commercial challenge therecycling industry is facing is that valuabletarget materials are lost into side-streamsduring mechanical pre-processing, andcan’t be recovered during end-processing,significantly reducing financial returns.To maximise recycling efficiencies and consequent rawmaterial recovery, mutual optimisation across this logisticchain is needed. For example, the reduction of EoL devicesinto material fractions: printed circuit boards (PCBs), wiring,ferrous metals, plastics, glass etc. is key to effective recoveryof the embedded precious and/or critical materials. InefficientPRE-PROCESSINGEND-PROCESSING 5%30-80%50%PROCESS NOWNFIGURE 1: Embedded material lost during e-waste processing2,502
RECYCLING: AUTOMATING THE SORTING AND SEPARATION OF E-WASTEseparation causes loss of material fractions into side streamsfrom which they cannot be recovered, for example, plasticsfragments contaminating the purity of a collated PCB streamand vice versa.This raises the question, what can bedone to improve processing efficiencies?To put this into context, across all e-waste categories 48%of the monetary value is embedded in the PCBs fraction,yet PCBs account for only 8% of the overall e-waste mass3,see Figure 2. For IT and Telecommunications equipment, orconsumer electronics, this PCB mass fraction is even higher,typically 13% – 14%4. PCB fraction recovery rates can varygreatly. While 100% recovery can be achieved through labourintensive manual disassembly and separation, mechanicalshredding or crushing, coupled with automated flake sorting,results in poor recovery – typically 30% – 80%5. About 20%of precious metal content is lost to non-recoverable outputside-streams such as plastics, process residues or saleablemetals6 when e-waste items are mechanically pre-processed.COLLECTIONPer ton of PCB mix, end-processors pay pre-processorsonly the net value that is the precious metal content7 minustreatment costs for sampling, assaying, smelting and refining.For a tonne of typical PCB mix, the net value can vary between 2,500 – 5,9008,9.We will now look at some of the current challenges that preprocessors are facing, new emerging technologies and howthese technologies could be integrated into current systemsto fully unlock the potential that circular economy provides.Light, ferrous metalPCBsPlasticsWiringOther componentsGlassBatteriesDisplays0%10%20%BY VALUE40%50%60%BY WEIGHTEND-PROCESSING DEVICE DISASSEMBLY / DEVICE SHREDDING SORTING INTO E-WASTE FRACTIONS GRINDING INTO BULK POWDERSHIPPINGPAYMENT30%FIGURE 2: Value of e-waste fractions across all typical categories10PRE-PROCESSING WEEE COLLECTION DEVICE LEVEL SORTINGWHITE PAPER ANALYSIS OF BULK POWDER PROCESSING OF BULK POWDER CONVERT TO RAW MATERIALSSHIPPINGPAYMENTFIGURE 3: The e-waste processing chain03
WHITE PAPER2.RECYCLING: AUTOMATING THE SORTING AND SEPARATION OF E-WASTEPROCESS CHALLENGESWe have identified six challenges that pre-processors arelikely to face:DATA SECURITY: Concerns over data security stop consumers from handing over their end-of-lifedevices for recycling. A recent survey, conducted in September 2017, suggested that 69% ofconsumers had concerns over data security. Up to a third would keep hold of old devices, effectivelywithholding them from recycling and metal recovery11.PRODUCT COMPLEXITY: Each generation of electronic devices delivers greater functionality12 whichis normally achieved through more integrated design at reduced cost13. Bulk shredding of thesefully integrated electronic devices creates a homogenous mix of waste fragments14. Sorting thesegenerated small waste fragments into groups such as ferrous and non-ferrous metals, plastics,PCBs etc. is becoming increasingly difficult. For example, the purity of the plastic output wastestream is 95%15. Consequently, only high value items are manually pre-processed.HUMAN TOUCH POINTS: To complete repetitive but variable tasks, such as sorting or disassembly,e-waste collectors and/or pre-processors rely on manual labour. Staff availability and retention is amajor challenge due to the monotonous nature of the tasks. To put this into context, the estimatedraw material value of an iPhone 6 is 0.7316, while the UK minimum wage is 7.83 per hour –manual pre-processing is not a commercially viable option.THROUGHPUT: An increasing number of electronic devices are being sold. At the same time, recyclingtargets are being increased. This puts significant pressure on the e-waste recycling industry toupscale and increase throughput. The overall requirement is that by 2019 65% of all new e-wasteitems sold should be reclaimed for recycling17 in the UK. With 2015 UK reclamation rates of37.5%18 processing capacity has to almost double to achieve this.PURER OUTPUT STREAMS: Most low value e-waste items are currently shredded in bulk and ground intoa fine powder19 During the conversion of this powder into new materials, only some of the embeddedcritical materials can be recovered20. To improve recycling efficiencies, components fabricatedwith similar base materials, such as aluminium housings or printed circuit boards, require targetedconversion. This is a challenging task even for human operators.UNKNOWN VALUE: Insufficient information about the actual composition of the selected e-wastefragments, such as PCBs, that are to be converted back into raw materials makes physical samplingand analysis necessary. As the raw value per ton of PCBs can vary from 2,500 to 10,00021 for‘low grade’ and ‘high grade’ PCBs respectively, pre-processors have very little financial planningsecurity.Although these challenges are apparent for e-waste processing, they are not exclusiveto e-waste processing only. Such technological developments can have multipledeployments in the recycling industry to enable even faster return on investment.04
RECYCLING: AUTOMATING THE SORTING AND SEPARATION OF E-WASTE3.RELEVANT TECHNOLOGIESOverall, the waste management industry has a track recordof being entrepreneurial, developing highly bespoke firstof-its-kind solutions to tackle new process challenges.Traditionally the equipment design has been undertaken byspecialist suppliers who are heavily dominated by classic,heavy duty mechanical engineering. More recently, earlyWHITE PAPERadopters have begun to unlock the potential that new digitaltechnologies provide22.In the context of digital transformation, we believe that fourtechnologies – machine vision, machine learning & artificialintelligence, augmented reality and energy efficient roboticsystems – will have a big impact on the waste managementbusiness in the coming decade.MACHINE VISION: Machine Vision (MV) has become a key technology in the area of manufacturingand quality control. For automatic inspection, cameras have been mounted over processing lines inorder to capture digital images and to inspect features to predefined tolerance. Alternatively, roboticsystems can be controlled to perform process operations without human intervention. In essence,the machine vision system consists of a number of cameras and control units that interpret andsignal individual operating instructions.For example: Tracking and analysing the size of chicken nuggets during manufacturing; instructing amachine to reject the nuggets that don’t fit acceptable criteria.MACHINE LEARNING & ARTIFICIAL INTELLIGENCE: Rather than building a control system that worksto rigid tolerances, pre-defined by human analysis, Machine Learning (ML) could be used todefine tolerances for industrial control systems with little data. In addition, ML could be used toprogressively improve the performance of a specific task using an ever-increasing amount of datacaptured through MV. Machines will be able to ‘intelligently’ identify and disassemble e-waste itemsand sort sub-assemblies and components into categories without human intervention.For example: Control systems will be able to identify devices, laptops and their state of damage orcomponents on a PCB and their estimated material value.AUGMENTED REALITY: With the aid of Augmented Reality (AR) glasses that use cameras, motion anddepth sensors, additional operating information can be overlaid onto the real-world environmentduring work. Operators will be instructed in real-time on how to de-manufacture end-of-life items,while training times will be reduced. Ultimately the operator will be able to see the location ofscrews, prying points and suggested critical parts to be retrieved, while receiving instructions onhow to disassemble a specific device.ENERGY EFFICIENT ROBOTICS: Recent advances in robotic systems have pushed down maintenanceintervals and energy requirements further. As robotic systems can work 24 hours, 7 days a week,the operational cost is pushed beneath that of human labour. At a reported average picking rateof 65 picks per minute a robot can already handle the workload of two manual picking stations23.These four digital technologies have the potential to transform how e-waste,and in the wider sense all waste streams, will be cost effectively processedin the coming decade.05
WHITE PAPER4.RECYCLING: AUTOMATING THE SORTING AND SEPARATION OF E-WASTEPROCESS IMPROVEMENTSThe process steps of disassembly and sorting are likely to betransformed first. At a later stage, full analysis and valuationof the waste stream will become the gold standard.Not only will efficiency improvements occur in real-time, butthe full potential of circular economy will be unlocked and newvalue streams created in the process.4.1DISASSEMBLYTasks that require a degree of decision taking, such as theability to disassemble a device, were once only possible forhumans. As high-end CPUs and GPUs become availableat lower costs, robotic systems will be able to performcomplicated tasks.The ability to disassemble a device relies on skilfulness andknowledge24. Skilfulness can be described as a set of repeatableactions used to perform a task, such as a single disassemblyoperation. Knowledge is the information required to plan suchoperations as taking a specific device apart. In real life, bothskilfulness and knowledge are acquired and refined throughdemonstration (training) and practice (learning).Like humans, a robotic system can, however, be trained tolearn a disassembly operation. This remains challenging andrelies on a dedicated physical training environment to recordthe manual operating procedures and later translate theminto robotic control instructions. Once developed the systemcan be improved with every operating procedure captured.Unlike with manual labour, this skilfulness and knowledge ispermanently stored and instantly sharable across the globalworkforce.4.2SORTINGObjects are not always easy to differentiate, with even humansmaking mistakes. At the same time, a robot can pick items attwice the speed of a human, as mentioned earlier.Machine vision has been successfully deployed to identifydifferent types of fruit in the agricultural industry. Paired withlightweight actuators, requiring far less energy to operate, thehourly operating cost can drop below the equivalent minimumwage at higher picking rates. Our work in agricultural roboticshas demonstrated that intelligent robotic systems performmore accurate and repeatable sorting operations. Transferringthis knowledge to e-waste pre-processing would make pureroutput waste streams possible.064.3VALUE ANALYSISAt present, little information about the composition andmaterial value of the produced waste output streams iscaptured during pre-processing. Therefore, the actualcomposition remains unknown. This limits the opportunitiesfor meaningful commercial negotiation. As a result, both thepre-processors and end-processors rely on sampling andphysical analysis to determine the shipment value.During automated disassembly and sorting, valuableinformation about the waste output streams can be gatheredand recorded. This data can be further analysed and the valuepredicted in real time. At present, however, the actual valueof a recycled PCB is estimated by an experienced operator.Recent research has proven that it is possible to analyse howa PCB is populated by means of Machine Vision25. Combinedwith additional information, such as overall PCB dimensions,track layout and connector quantity, the intrinsic “embedded”material value can be predicted accurately and repeatedly.The predicted value can be exchanged in real-time betweenpre-processors and end-processors to optimise forecasting,refining and to speed up payment.4.4RE-USE & RE-MANUFACTURINGAt the end-of-life of a product not all components or devicemodules disposed of are necessarily waste. Components suchas heat sinks may be perfectly fine for reuse. In fact, it ismore energy efficient to salvage an aluminium heat sink froma de-manufactured device and re-use it, than it is to fabricatea new one from virgin materials. However, this treatment iscurrently only reserved for high value items, as targeted demanufacturing is not cost efficient for small devices.As technology matures and facilities leverage the potentialof flexible de-manufacturing lines, new value streamsand business models will be unlocked. As second-handcomponents are typically cheaper than new ones, usingsalvaged components and modules to repair devices can bringdown the cost of product ownership.In the near future, e-waste recycling facility operatorsare likely to become skilled device de-manufacturing andre-manufacturing service providers. Flexible automaticdisassembly and sorting systems are a first step to realisingthis change.
RECYCLING: AUTOMATING THE SORTING AND SEPARATION OF E-WASTE5.ROUTE TO MARKETAt Cambridge Consultants we work with clients in markets thatundergo rapid technological transformation, such as logistics,agriculture or surgical robotics. Having developed breakthroughrobotic systems for these clients, we have an appreciation ofthe technical and commercial challenges involved. Any systemneeds to be accurate, reliable and robust, easy to maintainand with low energy consumption, while also providing a fastreturn on investment. In short, any new product or equipmentneeds to be both commercially viable and technically feasible.When working with our clients to help them improve theirbusiness, we focus on areas that offer rapid returns byimproving processing efficiencies and reducing operationalcosts, using technologies that have recently become readilyavailable. In the case of e-waste processing we anticipatea stepwise transformation in order to establish what istechnologically feasible and to focus on the areas that offerimmediate returns.WHITE PAPERFULLY-AUTOMATIC DISASSEMBLY: With sufficient disassembly skillsand knowledge copied from humans to ‘train up’ a roboticsystem, full automation becomes feasible. Initially with humanoversight, increasingly phased out until the process is fullyautonomous.Automating the way e-waste is pre-processed offers rapidcommercial gains, nonetheless successful machinery developmentwill involve competent management of commercial and technicalrisks.6.CONCLUSIONIn the developed world the e-waste and waste managementindustry is under pressure to improve recycling efficiencies.At the same time, end-of-life (EoL) devices are not effectivelyconverted back into raw materials. Fully exploiting the ‘value’of waste, while being under external pressure, will force theindustry to radically innovate in order to survive.We anticipate the following stepwise transformation:WASTE STREAM ANALYSIS: Capturing the manual sorting processwith dedicated camera systems, Machine Vision and MachineLearning will be leveraged to train a system to automaticallyidentify and value the components and sub-assemblies.FIGURE 4: PCB with estimated high precious metal content; total value 10,000 /tonne. Classified with algorithm developed in-house.SEMI-AUTOMATIC DISASSEMBLY: Augmented reality will beleveraged to provide workers with disassembly instructions ina dedicated environment, speeding up manual operations inthe process and reducing training cost.TRAIN ROBOTIC SYSTEMS: At the same time vision systems can beleveraged to record how the operator is actually disassemblingan item. While workers disassemble products in a dedicatedenvironment, unknowns such as device specific disassemblysteps can be recorded and used to improve the cognitiveability of a robotic system.For waste management to beeconomically viable in the future it mustembrace recent technology advancesand become part of a circular economy.This in turn will help ensure that rawmaterials remain available to fuel futurecycles of rapid innovation.Recent technological advances unlock the potential toautomate complex tasks that were previously undertakenby humans. This makes flexible automatic disassembly ofe-waste to create purer output waste streams both technicallyfeasible and commercially viable. Fully analysing the wastestream and valuing it in real time will improve cash flow andlogistic planning. Overall these advances unlock the potentialthat circular economy provides.To keep commercial risk low, the industry will be transformedthrough stepwise transitions as technology matures. At firsthumans will be assisted with operating instructions to speedup the process. Disassembly operations will be capturedthrough demonstration and recorded, to fully automate theprocess using robotic systems. The captured images will beused to analyse the waste stream and predict its embeddedmaterial value.07
RECYCLING: AUTOMATING THE SORTING AND SEPARATION OF -PROCESSINGGRINDINGCHALLENGESPROCESS STEPWHITE PAPERHUMAN TOUCH POINTSTHROUGHPUTUNKNOWN VALUEPURER OUTPUT STREAMSRELEVANT TECHNOLOGIESPRODUCT COMPLEXITYMACHINE VISIONARTIFICIAL INTELLIGENCEAUGMENTED REALITYENERGY EFFICIENT r-economybac
Converting e-waste back to raw materials offers a cost-efficient alternative to mining virgin materials, particularly as they become harder and more expensive to source. Resource bottlenecks drive up raw material prices and can inhibit innovation cycles. In this paper we focus on the bigg
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