The Knowledge Base Today

Transcription

1 The Knowledge Base Today This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No

2 What the data from the EU-UMKDP tells us about the urban mine End-of-life vehicles Maria Ljunggren Söderman Batteries Johanna Emmerich Mining wastes Anders Hallberg WEEE Jaco Huisman This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No

3 Introduction BATT, ELV and WEEE data How much data did ProSUM manipulate? 3

4 ProSUM Secondary Raw Materials Data

5 A big step forward... Definitions and languages Scope Classifications Data quality Meta-data All latest available data from >800 sources Automatization of Uncertainty and error propagation

6 Data availability/coverage Colour is coverage Part of data model BATT ELV EEE Products placed on market (POM) Product Average Weight Lifespan information Compositions Stock/ Waste Generation Waste collected and reported Other whereabouts Scavenging 19,364 records: 29% of data points are considered original. Gaps related to national data and years before records based on market statistics and samplings 1,248 records, 51% based on age determination of collected waste batteries 272 records Data for 17 electrochemical systems (subkeys), not differentiating time and regions 2x 26,000 computed records, plus measured data for some countries and years on stocks of batteries in pieces per household 1,356 records in tonnes per country, year and battery family (key) Some data on percentage of batteries in residual household waste (%), no data on other whereabouts 9,702 reported records plus 4,158 computed projected records 26,460 actual records (stock information) and computed records 26,460 actual records (stock information) and computed records 1,764 records Data for 28 elements over 63 vehicle keys 23,000 of which are computed 450 records (28+2 countries and 15 years) 600 records (28+2 countries and 20 years) 43,507 records: 33% of data points are considered original. Plus external sources 2,750 records: compared with product register data based on millions of data points 108 records: based on extensive multi-variate stock and flow modelling Cat I, C&F : 4,680 records Cat II, Screens: 5,460 records Cat III Lamps: 3,016 records Cat IV LHA: 10,868 records Cat V SHA: 14,820 records Cat VI 4,662 records 2x 54 M records: computed 1,080 records ( , 6 collectoin categories, 30 countries) 6,630 records: Based on 665 original data points/ sampling

7 What the data from the EU-UMKDP tells us about the urban mine Vehicles ProSUM Final Information Network Event, 10 November 2017 Maria Ljunggren Söderman (Chalmers), Amund N. Løvik (EMPA), Duncan Kushnir (Chalmers) et al. This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No

8 14 million new vehicles 260 million vehicles in the fleet 6 million vehicles recycled

9 These are preliminary results and, as such, may be subject to change. They should not be used nor cited. Total mass of vehicles, 2014 [million tonnes] New on the market 18 Imported used vehicles 0.2 Simplified Sankey tbc Reported fleet 300 Registered recycled ELV 8 Exported used vehicles 1 Unknown whereabouts 6 >70% Germany, Italy, France, UK, Spain and Poland

10 These are preliminary results and, as such, may be subject to change. They should not be used nor cited. New vehicles by drivetrain [number] Historic Projected

11 These are preliminary results and, as such, may be subject to change. They should not be used nor cited. Reported ELV by drivetrain [number] Historic Projected

12 These are preliminary results and, as such, may be subject to change. They should not be used nor cited. Vehicle element composition 63 vehicle keys type/weight range/motor energy type/cylinder volume range (~segment) 28 elements Ag, Al, Au, Ce, Co, Cu, Dy, Fe, Ga, Gd, In, La, Li, Mg, Mn, Mo, Nb, Nd, Pb, Pd, Pr, Pt, Rh, Si, Sm, Ta, Tb, Y Materials Steel and iron (7) Aluminium (9) Copper Magnesium (4) Components Catalytic converter (5) EV motor magnet (3) Battery cell (6) Systems Electric and electronic system (16) 1 7 Differs over vehicle key POM year

13 These are preliminary results and, as such, may be subject to change. They should not be used nor cited. Content 2015 (batteries excluded)

14 These are preliminary results and, as such, may be subject to change. They should not be used nor cited. Conclusions Uncertainty estimates: lowest -5%<Fe<5% highest -90%<Ga, Gd, In, Sm, Tb, Y<900% Prio for future work : Element composition per vehicle key and over time Vehicles of unknown whereabouts Significant source of ProSUM elements despite few EVs Examples: Fe, Al, Mg, Mo, Nb, Pt, Au, Ag, Nd Significance likely to increase for most ProSUM elements

15 What the data from the EU-UMKDP tells us about the urban mine Batteries ProSUM Final Information Network Event, 10 November 2017 Perrine Chancerel (TU Berlin), Claude Chanson, Peter Binnemans, Johanna Emmerich, Paul Mählitz et al. This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No

16 Overview /Sankey

17 Battery, flow placed on market Batteries POM, EU28, in tonnes of cells * * Projection Data sources: Avicenne, extracted by RECHARGE, Eucobat, EPTA, CONEBI, ICCT, EV 17

18 Battery, flow placed on market Lithium-ion batteries POM, EU28, in tonnes of cells * Projection Data sources: Avicenne, extracted by RECHARGE, Eucobat, EPTA, CONEBI, ICCT, EV * 18

19 Battery, flow placed on market Lithium-ion batteries POM, EU28, projection for 2017, in tonnes, by application Data sources: Avicenne, extracted by RECHARGE, EPTA, CONEBI, ICCT, EV 19

20 mass fraction (median) mass fraction (median) Battery, composition elements in products (e-p) Elements in Lithium batteries Lithium batteries LiSO2 LiSOCl2 LiMnO2 LiCFx LiCoO2 LiNMC LiFePO4 LiMn Al C* Cd Ce Co Cr Cu F Fe La Li Mn Nd Ni P Pb Pr S Sb Zn C* = graphite Carbon Elements in Zn, NiMH, NiCd, Pb batteries Zn-, NiMH-, NiCd-, Pb-batteries NiMH - sealed NiMH - vented NiCd - sealed NiCd - vented Pb - sealed Pb - vented Zn batteries NiMH batteries NiCd batteries Pb battereies Al C* Cd Ce Co Cr Cu F Fe La Li Mn Nd Ni P Pb Pr S Sb Zn Data sources: Batteries Material Safety Data Sheets, Handbook of batteries, other published data from the industry

21 Battery, stocks Batteries in stocks according to the stocks and flows modelling, excluding Pbbatteries - For EU 28, in tonnes of cells Matches experimental data in pieces Data sources for the modelling: POM: Avicenne, extracted by RECHARGE, Eucobat, EPTA, CONEBI, ICCT, EV; residence times: Eucobat per household 21

22 Battery, stocks Lithium-ion batteries in stocks according to the stocks and flows modelling For EU 28, in tonnes of cells Data sources for the modelling: POM: Avicenne, extracted by RECHARGE, Eucobat, EPTA, CONEBI, ICCT, EV; residence times: Eucobat, RECHARGE 22

23 Battery, stocks Lithium-ion batteries in stocks according to the stocks and flows modelling For EU 28, in pieces per inhabitant Data sources for the modelling: POM: Avicenne, extracted by RECHARGE, Eucobat, EPTA, CONEBI, ICCT, EV; residence times: Eucobat, RECHARGE; population: Eurostat 23

24 Gap: Waste Generated - Waste Collected Data sources : Waste Collection: Eurostat, EPBA, Eucobat; Modelled Generation: Eucobat 24

25 Gap: Waste Generated - Waste Collected Data sources : Waste Collection: Eurostat, EPBA, Eucobat; Modelled Generation: Eucobat 25

26 Stocks and Flows of critical raw materials? Data sources: Eucobat, RECHARGE * USGS, European Commission 26

27 Challenges for batteries Periodic update of the data is challenging due to the complex data sourcing All records have an uncertainty (5-50%) which should never be forgotten! Data need to be harmonised and improved, e.g.: Industrial batteries Complementary flows Lifespan distribution (better consideration of hoarding, time and geographical area) Changes over time of composition and average weights Go beyond collection and provide data on recycling/ recyclability of the CRM contained in the waste batteries?

28 What the data from the EU-UMKDP tells us about the urban mine Mining Waste Anders Hallberg, Geological Survey of Sweden EGS, BRGM, CGS etc This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No

29 Mining Waste MIN (MET ) WEE E BATT ELV No recycling industry No EU-legislation No Eurostat statistics Short term flow Very large but low grade Secondary resource Kankberg open pit, Photo: Boliden 29

30 ProSUM Mining Waste & Minerals4EU Mining waste from mining waste rock Mining waste from mineral processing tailings 30

31 Mining Waste - Mine and waste rock Stollberg mine, Sweden c

32 Mining Waste - Tailings Silvhyttan tailing dam, Sweden Photo: Helge Reginiussen, SGU 32

33 Mining Waste Data collection Location Amount Composition Quality Deliverable 4.2 Deliverable B: Methods for estimating the composition of mining waste 1 Drilling or trenching 1. 1 Detailed sampling and analysis Sampling by drilling or trenching with a sample density and analytical quality sufficient for a geostatistical estimation to calculate the amount of material for a resource estimate acceptable for international reporting (i.e., the JORC code, NI , etc.) 1. 2 Sampling 1. Random sampling 3 2 Production statistics 33 Random drilling or trenching with a sample density and analytical quality not sufficient for a proper geostatistical analysis. One or very few locations sampled and analysed 2. 1 Production statistics Calculations from known composition of produced waste rock, produced tailings, ore input to concentrator minus quantity of produced concentrate or similar. 2. Ore production Estimates from amount and composition of

34 Mining Waste Data collection digestion aqua-regia Li-borate, 4-acid analysis ICP-MS ICP-OES, MS MnO_% 9,79 S_% 1,23 1,42 Ag_ppm 12 14,6 As_ppm 2050 >250 Cd_ppm 27,7 25,7 Cu_ppm In_ppm 1,74 1,83 Pb_% 1,08 1,09 Sb_ppm 21,6 21,6 Zn_% 1,205 1,24 34

35 Mining Waste On the web 35

36 Mine Mining Waste Cu_% 0,07 Zn_% 2,37 Pb_% 3,24 Ag_ppm 80,83 Cu_% 0,05 Zn_% 0,31 Pb_% 0,74 As_% 0,98 n=49 In (ppm) In aquaregia digestion Li-borate, 4-acid analysis ICP-MS ICP-OES, MS Cu_ppm Zn_% 1,21 1,24 Pb_% 1,08 1,09 As_ppm 2050 >250 Cd_ppm 27,7 25,7 Ag_ppm 12 14,6 In_ppm 1,74 1,83 Sb_ppm 21,6 21,6 In Zn (%) 36

37 Mining Waste - MIN Photo: Magnus Ripa, SGU 37

38 What the data from the EU-UMKDP tells us about the urban mine WEEE Jaco Huisman, UNU Jaco Huisman, Hina Habib, Michelle Wagner (all UNU) This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No

39 Example results Example - Screens case study Jaco Huisman 39

40 Flows of screens products/ elements in reported collection

41 Product sales and waste data 41

42 Components, materials and elements 42

43 Printed Circuit Board % in product (t) 43

44 Components, materials and elements 44

45 The influence of collection quality 150% 125% 100% 75% The presence of the UNU key in the Collected Stream on a weight basis, Over-represented Under-represented 0303 Portable PC 50% 25% 0% Relative presence of products in the Screens Category (II) 0308 CRT Monitors 0309 LCD Monitors 0407 CRT TVs 0408 LCD TVs 45

46 The influence of collection quality 150% 125% 100% 75% The presence of the UNU key in the Collected Stream on a weight basis, Over-represented Under-represented 0303 Portable PC 50% 25% 0% Relative presence of products in the Screens Category (II) 0308 CRT Monitors 0309 LCD Monitors 0407 CRT TVs 0408 LCD TVs 46

47 Sankey diagram - Screens 47

48 Conclusions Static: There is a substantial Urban Mine! Dynamic: EEE Products are changing rapidly! Periodic update of composition data is challenging! All records have an uncertainty (5-100%)! Some elements are structurally underestimated. Some specific elements amounts are based on only few data sources Reporting on Unknown Whereabouts needs to be improved more transparency on metal scrap trade and exports