Circular manufacturing ecosystems: Automotive printed circuit boards recycling as an enabler of the economic development

Figures & data

Table 1. Composition of WPCBs. Adapted by IMDS.

	Small WPCBs	Medium-small WPCBs	Medium-big WPCBs	Big WPCBs
Silver (Ag)	0.09%	0.00%	0.00%	0.00%
Gold (Au)	0.42%	0.20%	0.24%	0.09%
Copper (Cu)	18.84%	24.19%	14.52%	16.30%
Iron (Fe)	0.18%	0.17%	0.19%	0.10%
Nichel (Ni)	0.69%	0.43%	1.13%	0.89%
Tin (Sn)	1.81%	1.46%	1.23%	1.56%
Tantalum (Ta)	0.08%	0.00%	0.00%	0.00%
Lead (Pb)	0.71%	1.13%	0.40%	0.27%
Sum of metals	22.82%	27.58%	17.71%	19.21%
Epoxy resin	15.81%	3.73%	12.73%	13.60%
Glass fibre	19.34%	35.56%	35.53%	36.72%
Delta materials	39.40%	30.50%	32.80%	29.00%
Other materials	2.63%	2.63%	1.23%	1.47%

Table 2. Metal prices. Adapted by Heraeus, KME, and Investing.

Metals	Average price (€/Kg)	Maximum price (€/Kg)	Minimum price (€/Kg)
Silver (Ag)	647.6	771.4	561.4
Gold (Au)	51,876	59,340	46,540
Copper (Cu)	8.5	9.9	7.0
Iron (Fe)	0.13	0.18	0.09
Nichel (Ni)	22.0	44.0	15.3
Tin (Sn)	33.9	45.9	23.5
Tantalum (Ta)	326.6	365.7	295.9
Lead (Pb)	2.0	2.3	1.8

Figure 1. Metal extraction process.

Table

$\mathrm{Q}$: amount of ECU	${\mathrm{Q}}_{\mathrm{m}}$: amount of metal dusts
${\mathrm{Q}}_{\mathrm{s}}$: amount of WPCB	${\mathrm{p}}_{\mathrm{p}\mathrm{r}}$: percentage of dust lost in refining
${\mathrm{p}}_{\mathrm{i}\mathrm{n}}$: weight percentage of aluminum casings	${\mathrm{Q}}_{\mathrm{p}\mathrm{r}}$: amount of dust lost in refining
${\mathrm{Q}}_{\mathrm{i}\mathrm{n}}$: amount of aluminum casings	${\mathrm{Q}}_{\mathrm{m}\mathrm{r}}$: amount of refined powders
${\mathrm{Q}}_{\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{x}}$: amount of toxic material in the casings	${\mathrm{p}}_{\mathrm{m}\mathrm{t}\mathrm{o}\mathrm{x}}$: percentage of toxic metals
${\mathrm{p}}_{\mathrm{e}\mathrm{d}}$: amount of toxic material in enclosures	${\mathrm{Q}}_{\mathrm{m}\mathrm{t}\mathrm{o}\mathrm{x}}$: amount of toxic metals
${\mathrm{Q}}_{\mathrm{p}\mathrm{t}}$: amount of dust lost in shredding	${\mathrm{p}}_{\mathrm{m}\mathrm{m}}$: percentage of metals destined for the market
${\mathrm{p}}_{\mathrm{t}}$: percentage of dust lost in shredding	${\mathrm{Q}}_{\mathrm{m}\mathrm{m}}$: amount of metals intended for market
${\mathrm{Q}}_{\mathrm{t}}$: amount of dust	${\mathrm{p}}_{\mathrm{S}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{s}}$: percentage of all metals in the WPCB
${\mathrm{p}}_{\mathrm{n}\mathrm{m}}$: percentage of non-metal dust	$\mathrm{j}$: general metal intended for market
${\mathrm{Q}}_{\mathrm{n}\mathrm{m}}$: amount of non-metallic powders	${\mathrm{p}}_{\mathrm{j}}$: percentage of generic metal for market
${\mathrm{p}}_{\mathrm{p}\mathrm{u}\mathrm{r}}$: percentage of metal purity for market	${\mathrm{Q}}_{\mathrm{j}}$: amount of generic metal for market
${\mathrm{p}}_{\mathrm{P}\mathrm{b}}$: percentage of Pb	${\mathrm{p}}_{\mathrm{o}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}}$: percentage of other materials

Table

${\mathrm{F}}_{\mathrm{A}\mathrm{T}\mathrm{T}}$: discounted cash flow	${\mathrm{F}}_{\mathrm{d}\mathrm{e}\mathrm{b}}$: loan interest rate
k: time	${\mathrm{F}}_{\mathrm{a}\mathrm{q}}$: purchase cost
${\mathrm{F}}_{\mathrm{N}\mathrm{E}\mathrm{T}}$: net cash flow	${\mathrm{F}}_{\mathrm{s}}$: material disposal cost
r: opportunity cost of capital	${\mathrm{F}}_{\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{x}}$: toxic material disposal cost
F: cash flow	${\mathrm{F}}_{\mathrm{e}}$: electricity cost
${\mathrm{F}}_{\mathrm{I}\mathrm{N}}$: cash inflow	${\mathrm{F}}_{\mathrm{l}}$: labour cost
${\mathrm{F}}_{\mathrm{O}\mathrm{U}\mathrm{T}}$: cash outflow	${\mathrm{F}}_{\mathrm{a}\mathrm{s}\mathrm{s}}$: insurance cost
tax: value of taxes	${\mathrm{F}}_{\mathrm{m}\mathrm{a}\mathrm{n}}$: maintenance cost
j: metal	${\mathrm{F}}_{\mathrm{r}}$: reagent cost
${\mathrm{P}}_{\mathrm{m}}$: price metal	${\mathrm{F}}_{\mathrm{i}\mathrm{n}\mathrm{v}}$: total investment cost
${\mathrm{p}}_{\mathrm{p}\mathrm{u}\mathrm{r}}$: level of metal purity	N: lifetime

Table 3. Input data. Adapted by (Ardente et al., 2014; Cucchiella, D’adamo, Rosa, et al., 2016; D’adamo et al., 2019; Kamberović et al., 2011; Zeng et al., 2015).

Variable	Value
$\mathrm{i}$: type of electronic board	Small, Medium-small, Medium-big, Big
${\mathrm{C}}_{\mathrm{a},\mathrm{i}}$: purchase cost of electronic boards	4000 €/t; 3000 €/t; 3200 €/t; 2500 €/t
${\mathrm{C}}_{\mathrm{s}}$: disposal cost of materials	110 €/t
${\mathrm{C}}_{\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{x}}$: disposal cost of toxic materials	400 €/t
${\mathrm{C}}_{\mathrm{e}}$: cost of electricity	0.14 €/kWh
${\mathrm{C}}_{\mathrm{i}\mathrm{n}\mathrm{v}}$: initial investment	1,100,000 €
${\mathrm{n}}_{\mathrm{i}\mathrm{n}\mathrm{v}}$: number of years needed to pay off the debt	5 years
${\mathrm{r}}_{\mathrm{d}\mathrm{e}\mathrm{b}}$: interest rate of the loan	3%
${\mathrm{p}}_{\mathrm{i}\mathrm{n}\mathrm{v}2}$: percentage of the investment for pre-treatment	50%
${\mathrm{p}}_{\mathrm{i}\mathrm{n}\mathrm{v}3}$: percentage of investment for pre-processing and end of process	50%
${\mathrm{C}}_{\mathrm{l}\mathrm{a}}$: annual labour cost	45,000 €/workers
${\mathrm{C}}_{\mathrm{l}\mathrm{g}}$: daily labour cost	180 €/worker
${\mathrm{n}}_{\mathrm{g}}$: number of working hours in one year	250 days
$\mathrm{N}$: lifetime	10 years
${\mathrm{p}}_{\mathrm{a}\mathrm{s}\mathrm{s}}$: percentage of insurance expenditure to investment	2.5%
${\mathrm{p}}_{\mathrm{t}\mathrm{a}\mathrm{x}}$: percentage of cash flow taxed	40%
${\mathrm{C}}_{\mathrm{r}}$: cost of reagents	900 €/t
${\mathrm{e}}_2$: energy consumed by pre-treatment machinery	400 kWh/t
${\mathrm{e}}_3$: energy consumed by machinery for pre-processing and end-of-processing	9500 kWh/t
$\mathrm{i}\mathrm{n}\mathrm{f}$: annual inflation rate	2%
${\mathrm{n}}_{\mathrm{o}\mathrm{p}1}$: number of operators per pre-selection	3
${\mathrm{n}}_{\mathrm{o}\mathrm{p}2}$: number of operators per pre-processing	5
${\mathrm{n}}_{\mathrm{o}\mathrm{p}3}$: number of operators for pre-processing and end of process	5
${\mathrm{p}}_{\mathrm{m}\mathrm{a}\mathrm{n}2}$: annual maintenance percentage for pre-processing	30%
${\mathrm{p}}_{\mathrm{m}\mathrm{a}\mathrm{n}3}$: annual maintenance percentage for pre-processing and end of process	10%

Table 4. Economic indicators – Baseline scenario.

Indicator	Small WPCBs	Medium-small WPCBs	Medium-big WPCBs	Big WPCBs
DPBT (y)	1	1	1	1
NPV (M€)	303.8	163.0	196.3	68.3
NPV/QS (€/t)	607,621	325,922	392,696	136,570

Figure 2. NPV (million €) - Baseline scenario.

Figure 3. Breakdown of materials in terms of composition and economic value.

Table 5. NPV (million €) and its percentage decrease – Sensitivity analysis.

	Small WPCBs		Medium-small WPCBs		Medium-big WPCBs		Big WPCBs
	NPV (M€)	% Decrease	NPV (M€)	% Decrease	NPV (M€)	% Decrease	NPV (M€)	% Decrease
Purchase cost	302.5	0.4%	161.7	0.8%	195.1	0.7%	67.0	1.9%
Labour cost	303.2	0.2%	162.4	0.4%	195.8	0.3%	67.7	0.9%
Maintenance cost	303.5	0.1%	162.7	0.2%	196.1	0.1%	68.0	0.4%
Gold price	271.3	10.7%	145.1	11.0%	174.9	10.9%	60.3	11.8%
Purity level 85%	270.0	11.1%	144.2	11.5%	174.1	11.3%	59.7	12.6%
Purity level 75%	236.2	22.3%	125.5	23.0%	151.8	22.7%	51.1	25.2%
Purity level 65%	202.4	33.4%	106.8	34.5%	129.6	34.0%	42.5	37.8%
Plant saturation 90%	272.9	10.2%	146.2	10.3%	176.2	10.3%	61.0	10.7%
Plant saturation 80%	242.1	20.3%	129.4	20.6%	156.1	20.5%	53.6	21.5%
Plant saturation 70%	211.2	30.5%	112.6	30.9%	135.9	30.8%	46.3	32.2%
Plant saturation 60%	180.3	40.7%	95.8	41.2%	115.8	41.0%	39.0	42.9%
Opportunity Cost 7.5%	270.1	11.1%	144.9	11.1%	174.5	11.1%	60.7	11.1%
Opportunity Cost 10%	241.8	20.4%	129.7	20.4%	156.2	20.4%	54.3	20.4%

Table 6. NPV (million €) and its percentage decrease – Sensitivity analysis.

	Small WPCBs		Medium-small WPCBs		Medium-big WPCBs		Big WPCBs
	NPV (M€)	% Decrease	NPV (M€)	% Decrease	NPV (M€)	% Decrease	NPV (M€)	% Decrease
revenues	270.3	11.0%	144.2	11.5%	174.2	11.3%	59.5	12.9%
costs	301.6	0.7%	160.8	1.3%	194.2	1.1%	66.1	3.2%
revenues+costs	268.2	11.7%	142.0	12.9%	172.1	12.4%	57.3	16.1%
purity level+ revenues	164.3	45.9%	85.5	47.5%	104.5	46.8%	32.6	52.2%
purity level+ costs	183.3	39.7%	95.2	41.6%	116.3	40.8%	36.1	47.2%
purity level+ revenues+costs	162.1	46.6%	83.4	48.8%	102.3	47.9%	30.5	55.4%
plant saturation+ revenues	160.2	47.3%	84.5	48.1%	102.5	47.8%	33.7	50.7%
plant saturation+ costs	178.6	41.2%	94.1	42.2%	114.2	41.9%	37.3	45.4%
plant saturation+ revenues+costs	158.5	47.8%	82.9	49.2%	100.9	48.6%	32.0	53.1%
purity level+ plant saturation+ revenues	96.6	68.2%	49.3	69.7%	60.7	69.1%	17.6	74.3%
purity level+ plant saturation+ costs	107.6	64.6%	54.8	66.4%	67.4	65.7%	19.3	71.8%
purity level+ plant saturation+ revenues+costs	94.9	68.8%	47.7	70.8%	59.0	69.9%	15.9	76.7%

Figure 4. NPV (million €) - Risk analysis.

Figure 5. Gold content within WPCB – BEP analysis.

Figure 6. Trend and estimated weight of ELVs in Europe.

Table 7. Results of the estimation on the recycling of electronic boards of European ELVs.

	2019	2022	2030
Annual weight of ELVs (kt)	6889	7860	11,171
Annual weight of WPCBs (kt)	8.52	9.72	13.81
Economic pessimistic forecast (G€)	1.16	1.33	1.89
Economic optimistic forecast (G€)	5.17	5.91	8.39

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