Presentation at the RTB Annual Review and Planning Meeting (Entebbe, Uganda, 29 Sep-3 Oct 2014)

1. Environmental impacts of RTB
processing and life cycle assessment
Thierry Tran
RTB annual meeting, Entebbe, Uganda
30 / 09 / 2014
thierry.tran@cirad.fr

2. Framework
Complementary funding RTB Post-harvest 2013-2015
Twin post-doc IITA + CIRAD
Interactions between:
• CIAT
• IITA
• CIRAD
• NRI
• Univalle (Colombia)
• Kasetsart University, KMUTT (Thailand)

3. 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

4. Demand to improve processing (1)
9%
Peak 1FD + Nobex HE
Palm Kernel Shells 2013
10%
7%
Roots: 63% of costs
279 USD/t starch
Energy: 20%
89 USD/t starch
Total costs:
443 USD/t HQCF
Peak 1FD Black oil 2013
39%
7%
44%
6% 4%
Raw material
Power
Energy (drying)
Labour
Packaging
11% 63%
Raw material
Power
Energy (drying)
Labour
Packaging
Roots: 39% of costs
282 USD/t starch
Energy: 51%
368 USD/t starch
Total costs:
722 USD/t HQCF
Market
price:
650
USD/t
HQCF
Nigeria
NRI

5. Demand to improve processing (2)
Centrifuge
456 kg/hour
Electricity: 36 kWh/t starch / 70
Water: 22 m3/t starch / 35
Colladora
135 kg/hour
Electricity: 20 kWh/t starch / 55
Water: 34 m3/t starch / 48
Colombia
CIAT, Univalle, CIRAD

6. Demand to improve processing (3)
Flash dryer – Targets:
2-4 t/day
Energy: < 2500 MJ/t starch
Surface: < 20 m2
Sun drying
2 t/day
Energy: 0 MJ
Surface: 300-400 m2
Colombia
CIAT, Univalle, CIRAD

7. How to improve: 2 steps
Benchmark
• Production costs
• Energy, water costs
• Environmental impacts (LCA, Carbon footprint)
Re-engineer
• Model the technical and economic
performance of current technologies
• Optimization tools

8. Benchmark: Roots are the main cost
of production
Thailand Vietnam Cauca
81.24%
1.41%
7.48%
3.34%
4.13%
0.45%
1.95%
Factory A
CIRAD
92.5%
1.5%
1.2%
4.8%
PA1
73%
3%
15%
6%
7%
6%
8% Roots
Electricity
Natural gas
Labour
Other costs
9%
10%
7%
11% 63%
Raw
material
Power
66%
1%
15% Roots
Electricity
Labour
Consumabes,
packaging, etc.
Office &
Marketing
Nigeria
CIRAD, CIAT, Univalle, NRI

9. 126 127
100
71
367 372
294
247
114
107
82
126
106 70
51
32
1000
800
600
400
200
0
F1 F2 F3 F4
CO2eq (kg/t starch 13% mc)
Wastewater treatment
Biogas production
Electricity - biogas
Electricity - grid
Fuel oil
Other factory inputs
Transportation
N2O emissions
Organic fertilizer
Mineral fertilizers
Other 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

10. 126 127 100
71
367 372
294
247
110
219
74
121
267
107
124
154
389
569
438
346
1600
1400
1200
1000
800
600
400
200
0
F1 F2 F3 F4
CO2eq (kg/t starch 13% mc)
Wastewater treatment
Biogas production
Electricity - biogas
Electricity - grid
Fuel oil
Other factory inputs
Transportation
N2O emissions
Organic fertilizer
Mineral fertilizers
Other agricultural inputs
1574
1183
1040
Units: kg
CO2eq/t
starch
Renewables still better than fossil fuels
1410 Thailand -
Fuel oil
CIRAD

11. 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.

12. Diversity of raspers - Colombia / Africa

13. Diversity of raspers - Vietnam

14. Diversity of raspers - Thailand / Brazil

15. Particle size matters
Influence on
consumer
acceptance

16. Smaller particles release more starch
70
60
50
40
30
20
10
0
100 150 200 250 300 350 400 450
Starch yield increases below 300 μm
% b.s.
Diamètre moyen des particules (μm)
Teneur en amidon
(DSC)
teneur en fibres
modèle amidon
modèle fibres

17. 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?

18. Model predicts drying performance
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Small-scale : 15 kg.h-1, Dp = 115 μm, Res.Time = 0.5 s
Air-to-product ratio: 16
Large scale: 16 000 kg.h-1, Dp = 220 μm, Res.Time = 3.5 s
X: 57.7
Y: 0.0665
0 10 20 30 40 50 60
Position along the pipe (m)
Moisture content d.b.
0 2 4 6 8 10
Position along the pipe (m)
Moisture content d.b.
180
160
140
120
100
80
60
40
20
0 2 4 6 8 10
Position along the pipe (m)
Temperature in ?C
200
180
160
140
120
100
80
60
40
20
X: 57.45
Y: 51.41
0 10 20 30 40 50 60
Position along the pipe (m)
Temperature in ?C
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

19. 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.

20. Contributors and donors
Nanthiya Hansupalak
Klanarong Sriroth
Arnaud Chapuis
Palotai Piromkraipak
Pakhamas Tamthirat
Sudarat Lee
Apisit Manitsorasak
Martin Moreno
Dominique Dufour
Andrès Escobar
Timothée Gally
Arthur de la Giraudière
Adebayo Abass
Marcelo Precoppe
Keith Fahrney
Cu Thi Le Thuy
Andy Graffham
Diego Naziri
Uli Kleih
Warinthorn Songkasiri
Kanchana Saengchan
Patrick Sébastian

21. Thank you