Environmental impacts of RTB processing and life cycle assessment

Thierry TranRTB annual meeting, Entebbe, Uganda

30 / 09 / 2014thierry.tran@cirad.fr

Interactions between:• CIAT• IITA• CIRAD• NRI• Univalle (Colombia)• Kasetsart University, KMUTT (Thailand)

Framework

Complementary funding RTB Post-harvest 2013-2015

Twin post-doc IITA + CIRAD

RTBs are processed at large and small scales

A. de la Giraudière

Thailand 200t starch/day

Nigeria 2t HQCF/day

Tanzania 2t HQCF /day

Nigeria 0.3t gari/day

Colombia 2-3t starch/day

Vietnam 3-11t starch/day

Roots: 63% of costs279 USD/t starch

Energy: 20%89 USD/t starch

Total costs:443 USD/t HQCF

Demand to improve processing (1)

39%

7%

44%

6% 4%

Peak 1FD Black oil 2013

Raw material

Power

Energy (drying)

Labour

Packaging

63%11%

9%

10%7%

Peak 1FD + Nobex HEPalm Kernel Shells 2013

Raw material

Power

Energy (drying)

Labour

PackagingRoots: 39% of costs282 USD/t starch

Energy: 51%368 USD/t starch

Total costs:722 USD/t HQCF

Market price:

650 USD/t HQCF

NRI

Nigeria

Centrifuge456 kg/hour

Electricity: 36 kWh/t starch / 70

Water: 22 m3/t starch / 35

Demand to improve processing (2)

Colladora135 kg/hour

Electricity: 20 kWh/t starch / 55

Water: 34 m3/t starch / 48

Colombia

CIAT, Univalle, CIRAD

Flash dryer – Targets:2-4 t/day

Energy: < 2500 MJ/t starch

Surface: < 20 m2

Demand to improve processing (3)

Sun drying2 t/day

Energy: 0 MJ

Surface: 300-400 m2

Colombia

CIAT, Univalle, CIRAD

Re-engineer• Model the technical and economic

performance of current technologies• Optimization tools

How to improve: 2 steps

Benchmark• Production costs• Energy, water costs• Environmental impacts (LCA, Carbon footprint)

Benchmark: Roots are the main cost of production

81.24%

3.34%

7.48%

1.41%4.13%

0.45%

1.95%

Factory A

CIRAD

92.5%

1.5%1.2%

4.8%PA1

73%

7%

6%

6%8% Roots

Electricity

Natural gas

Labour

Other costs

63%11%

9%10%

7%

Raw mate-rial

Power

66%1%

15%

3%

15% Roots

Electricity

Labour

Consumabes,packaging, etc.Office &Marketing

Thailand Vietnam Cauca

Nigeria

CIRAD, CIAT, Univalle, NRI

126 127100

71

367 372

294

247

114107

82

126

106 70

51

32

0

200

400

600

800

1000

F1 F2 F3 F4

CO2e

q (k

g/t s

tarc

h 13

% m

c)

Wastewater treatmentBiogas productionElectricity - biogasElectricity - gridFuel oilOther factory inputsTransportationN2O emissionsOrganic fertilizerMineral fertilizersOther agricultural inputs

966

715

609

Units: kg CO2eq/t

starch

Benchmark: Grid electricity and methane emissions emit most CO2 at factory

910

Thailand - Biogas

CIRAD

126 127 100 71

367 372294

247

110219

74

121

267107

124154

389569

438346

0

200

400

600

800

1000

1200

1400

1600

F1 F2 F3 F4

CO2e

q (k

g/t s

tarc

h 13

% m

c)

Wastewater treatmentBiogas productionElectricity - biogasElectricity - gridFuel oilOther factory inputsTransportationN2O emissionsOrganic fertilizerMineral fertilizersOther agricultural inputs

1574

1183

1040

Units: kg CO2eq/t

starch

Renewables still better than fossil fuels

1410 Thailand - Fuel oil

CIRAD

Conclusions from benchmark

Energy is the second highest cost of production

Rasping and Drying use most energy

Focus on Rasping and Drying

is relevant for HQCF, gari, fufu, starch, etc.

Diversity of raspers - Colombia / Africa

Diversity of raspers - Vietnam

Diversity of raspers - Thailand / Brazil

Particle size matters

Influence on consumer

acceptance

Smaller particles release more starch

Starch yield increases below 300 µm

100 150 200 250 300 350 400 4500

10

20

30

40

50

60

70

Teneur en amidon (DSC)

teneur en fibres

modèle amidon

modèle fibres

Diamètre moyen des particules (µm)

% b

.s.

Diversity of flash dryers

Capacity Energy use Energy type

t/day MJ/t

Thailand 200 1500 - 2000 Biogas

Vietnam 2 5000 Coal

Nigeria 1 - 2 3000 - 10000 Oil / Biomass

Colombia (AdS) 50 2600 Natural gas

Colombia (Cauca) 2 - Sun drying

Can we make dryers at small scale with same energy efficiency as large scale?

Model predicts drying performance

Small-scale : 15 kg.h-1, Dp = 115 µm, Res.Time = 0.5 s

Large scale: 16 000 kg.h-1, Dp = 220 µm, Res.Time = 3.5 s

Air velocity is fixed

Residence time is shorter at small scale

Increase Air:Product ratio to dry

Increase heat losses

Re-design to minimize heat losses

Moisture

Moisture

Temperature

Temperature

Air-to-product ratio: 8

Air-to-product ratio: 16

Conclusions – Next steps

● Evidence of demand to improve Rasping and Drying

● Describe effect of rasping on product quality● Further experimental characterizations● Then improvements.

● Model and optimize drying● Build energy-efficient dryer at small scale

● Integrate socio-economic data to predict the effect on the value chain and gender.

● Tool for training and experience sharing: 5 MSc projects, 1 PhD, 2 post-docs.

Nanthiya HansupalakKlanarong SrirothArnaud ChapuisPalotai PiromkraipakPakhamas TamthiratSudarat LeeApisit Manitsorasak

Martin MorenoDominique DufourAndrès EscobarTimothée GallyArthur de la Giraudière

Contributors and donors

Adebayo AbassMarcelo Precoppe

Keith FahrneyCu Thi Le Thuy

Andy GraffhamDiego NaziriUli Kleih

Warinthorn SongkasiriKanchana Saengchan

Patrick Sébastian

Thank you