Date post: | 19-Jun-2015 |
Category: |
Science |
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Environmental impacts of RTB processing and life cycle assessment
Thierry TranRTB annual meeting, Entebbe, Uganda
30 / 09 / [email protected]
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