Residuos de caña de azucar

Energy and GHG emission reduction potential of power generation from
sugarcane residues in Thailand
Salakjai Jenjariyakosoln, Shabbir H. Gheewala ⁎, Boonrod Sajjakulnukit, Savitri Garivait
The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi, 126 Prachauthit Road, Bangmod, Tungkru, Bangkok 10140, Thailand
Center of Excellence on Energy Technology and Environment, Ministry of Education, Thailand
a b s t r a c ta r t i c l e i n f o
Article history:
Received 27 September 2013
Revised 15 July 2014
Accepted 15 July 2014
Available online 6 August 2014
Keywords:
Greenhouse gas
Sugarcane residues
Biomass supply chain
Advanced sugar mill
Electric power production using biomass residues from agricultural production using high efficiency electricity
generation technologies would reduce greenhouse gas (GHG) emissions and contribute to climate change miti-
gation. This study investigated the case of the sugar industry in Thailand and identified scenarios offering GHG
emissions reduction benefits. Electricity generation potential from using sugarcane residues and/or upgrading
power generation systems represent beneficial options. The largest potential of electricity export to the na-
tional grid can be achieved by upgrading boiler systems of all sugar mills to 103 bar and 515 °C. Using 19% of
the generated sugarcane tops and leaves along with bagasse can generate 9 TWh electricity and would re-
duce GHG emissions by 4.8 Mt CO2 equivalent a year. The economic analysis shows that using high steam
pressure boiler configurations for power generation results in substantial reduction in production cost
and increase in benefit.
© 2014 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
Introduction
Renewable energy technologies have an important role in miti-
gating climate change through reduced anthropogenic greenhouse
gas (GHG) emissions (IEA, 2011). Among the various renewable en-
ergy sources, biomass is currently gaining considerable interest
among energy policy makers (Bakos et al., 2008; WEC, 2013). Agri-
cultural residues, especially agro-industrial wastes, are widely used
as fuel for power generation. Due to the increase in power demand
and saturation of agro-waste utilization for power generation, the
increase in electricity efficiency along with the utilization of field
residues are receiving increased interest from researchers and policy
makers (Bocci et al., 2009; Deepchand 2001; Guzman and Valdes,
2000; Hassuani et al., 2005; Khatiwada et al., 2012; Larson et al.,
2001; Macedo et al., 2001; UNFCCC, 2014). In Thailand, electricity
generation, being highly dependent on fossil fuels (67% natural gas,
and 20% coal and lignite), is one of the major sectors contributing
to GHG emissions (EPPO, 2012). Thailand formulated the Power De-
velopment Plan (PDP) for the period of 2010–2030 for energy secu-
rity and adequacy by considering environment concerns, energy
efficiency, and renewable energy. Promotion of both renewable
energy and nuclear power was initially considered in this plan. How-
ever, following Japan's Fukushima incident in 2011, the revised plan
in 2012 promoted natural gas cogeneration systems in the initial
phase considering the existing infrastructure and domestic re-
sources. This was planned to replace the older gas-fired stations by
combined cycle power plants (US.EIA, 2013). Thailand is an agricul-
tural country, so biomass sources especially agro-industrial wastes
have been used as fuel for generating electricity for exporting to
the national grid through the Small Power Producers (SPP)
(10–90 MW) and Very Small Power Producers (VSPP) (b10 MW)
schemes. Some of the supporting schemes and incentives for SPP
and VSPP are the feed-in premium tariff, exemption of investment
tax scheme, soft loans for renewable energy, and fund provisions for
renewable energy investments (Asawachintachit, 2012; Sutabutr,
2010). An Alternative Energy Development Plan for 2012–2021
(AEDP 2012–2021) was also established by the Thai government to
target increasing the share of renewable and alternative energy to
25% of total energy demand within 10 years, particularly increasing
biomass power generation to 14,008 GWh, and reducing GHG emis-
sions by 76 Mt CO2 equivalent (CO2e) annually (DEDE, 2012).
Increased power generation from agro-waste e.g. woodchips, rice
husk, and bagasse has recently been achieved by upgrading technolo-
gies to increase electric efficiency. Many researchers have considered
the feasibility of improving electric efficiency by upgrading technolo-
gies and using additional fuel to meet the increased feedstock demand.
The surplus electricity production from sugarcane residues (bagasse
combined with tops and leaves) with increasing efficiency by upgrading
Energy for Sustainable Development 23 (2014) 32–45
⁎ Corresponding author at: The Joint Graduate School of Energy and Environment, King
Mongkut's University of Technology Thonburi, 126 Prachauthit Road, Bangmod, Tungkru,
Bangkok 10140, Thailand.
E-mail address: shabbir_g@jgsee.kmutt.ac.th (S.H. Gheewala).
http://dx.doi.org/10.1016/j.esd.2014.07.002
0973-0826/© 2014 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
Energy for Sustainable Development

power generation technologies has successfully been developed in
several countries e.g. Brazil, Cuba, India, Mauritius, and Thailand
(Bocci et al., 2009; Deepchand 2001; Guzman and Valdes, 2000;
Hassuani et al., 2005; Khatiwada et al., 2012; Larson et al., 2001;
Macedo et al., 2001; UNFCCC, 2014). Especially, the cogeneration
systems of sugar mills in Brazil had been developed from 22 bar
boiler with back pressure turbines to 105 bar with extraction con-
densing steam turbine and using tops and leaves as additional fuel
that increased surplus electricity export 16 folds (Khatiwada et al.,
2012).
Thailand is the world's fourth largest sugar producer, producing
98 Mt and 10.43 Mt of sugarcane and sugar in 2012 respectively. Also,
sugarcane production has been increasing 10% annually for the last
5 years (OAE, 2012; OCSM, 2013). The waste from sugarcane processing
i.e. bagasse, is already being used as the main fuel for heat and power
generation for sugar production with excess electricity being sold to
the national grid (Mendoza et al., 2002; OCSB, 2007b). The amount of
bagasse varies from 23% to 37% of the sugarcane (Deepchand, 2002),
with an average of 28% (Larson et al., 2001; PDTI, 2011). Another residue
that is interesting is the field residue from sugarcane cultivation i.e. tops
and leaves which varies between 17 and 30% of the sugarcane (DEDE,
2005; DEDP, 1992; Junginger et al., 2001; KMUTT, 2006). The available
amount of tops and leaves is approximately 74–98% of the total amount
generated, the rest being left in the field for incorporation into the soil as
organic fertilizer and weed control (DEDE, 2005; KMUTT, 2006;
Sajjakulnukit et al., 2005). Most of the tops and leaves are currently
open burnt in the field in order to facilitate sugarcane pre-harvesting,
and post-harvesting for land preparation (KMUTT, 2006; Yuttitham
et al., 2011). Utilizing tops and leaves as fuel for heat and power gener-
ation can help alleviate the open field burning problem, avoid GHG
emissions and contribute to reducing energy imports (Bocci et al.,
2009; DEDE, 2005, Gheewala et al., 2011; Guzman and Valdes, 2000;
Mendoza et al., 2002). The tops and leaves can be used as a secondary
fuel combined with bagasse in existing boilers, thereby avoiding
the need for storing excessive amounts of tops and leaves which
would be required if they are used as the primary fuel for the
whole year operation due to their seasonal availability limited
to 4–6 months annually in the harvesting season (Guzman and
Valdes, 2000; OCSM, 2013). However, the cost of the supply chain
process, including baling, field hauling and loading, truck transport,
shredding and storage stacking, of the low bulk density tops and
leaves is a major obstacle. Therefore this cost should be factored in
considerations for using this residue.
Currently, most sugar mills in Thailand operate low efficiency grate
boilers and back pressure steam turbines with steam pressure about
20 bar and temperature 350–360 °C; the plants produce energy for
their own needs (sugar milling) for the most part with only some excess
electricity being exported to the national grid (PDTI, 2011). The average
electricity export is only 14.5 kWh/ton sugarcane (tc) as compared to
70 kWh/tc and 158 kWh/tc that have been observed for the most ad-
vanced sugar mills in Thailand and Brazil respectively (Khatiwada
et al., 2012; Siemers, 2010). For new units recently equipped in the
more advanced sugar mills in Thailand with boilers that produce
steam at 103 bar and 515 °C, high amount of surplus electricity can be
produced for export to the grid; but additional fuel is required which
can possibly be provided by tops and leaves (ONEP, 2013). Siemers
(2010) evaluated the increasing surplus electricity generation and
GHG reduction using the best available boiler technology in Thailand.
However, this study was based on the existing technology in 2006 at
the highest boiler steam pressure of 70 bar and considering either
bagasse or tops and leaves as feedstock, but not a combination of
both. Hence, a need was perceived for updating the study with
more current technology (103 bar boiler pressure), using both resi-
dues (bagasse as well as tops and leaves) in combination accounting
for actual demand and availability, and considering actual data via
extensive site surveys.
The study aims to evaluate the electricity generated from sugar-
cane residues (tops and leaves, and bagasse) by upgrading the boiler
configurations in the existing power plants with high pressure steam
turbines. The associated GHG emissions from increasing surplus elec-
tricity are evaluated and compared to natural gas combined cycle
power plants that are expected to be constructed following the
Thailand PDP 2010 plan, and be the marginal power plants displaced
by the surplus electricity. The electricity export potential of different
production systems is also evaluated in terms of economic analysis to
encourage sugar mill owners or investors to consider exporting more
electricity. The cost models used are general and can be also applied
to other biomass feedstocks and locations.
Methodology
The methodology is organized into four parts. First, the tops and leaves availability was assessed followed by the estimation of the area
required for collecting these residues. This was followed by the assessment of surplus electricity potential from different scenarios depending
on different technologies of cogeneration systems at sugar mills. Sugar mills in Thailand were grouped roughly based on different levels of
boiler pressure and one sugar mill of each group was selected as the representative of that group for assessing energy balance (PDTI, 2011;
Siemers, 2010). After that, the overall GHG emissions assessment was carried out for the entire life cycle of power generation from bagasse
combined with tops and leaves. For tops and leaves, the supply chain (life cycle) includes collection, field hauling, road transport, shredding
and storage whereas for bagasse, only storage is required as it is generated in the sugar mill itself. The avoided GHG emissions from replacing
fossil-fuel power generation were also estimated. Finally, the cost assessment was conducted comprising the costs of tops and leaves supply chain
as well as different configurations of power plants.
Tops and leaves quantity and area estimation
The quantity of tops and leaves generated was evaluated based on the quantity of sugarcane delivered to the mill using residue to product ratio
(RPR) and surplus availability factor (SAF) as shown in Eq. (1) (Bhattacharya et al., 2005; Sajjakulnukit et al., 2005).
Tops and leaves available tð Þ ¼ Sugarcane amount tð Þ Â RPR Â SAF Â collection efficiency: ð1Þ
RPR varies with plant structure, seasonality, harvesting methods, irrigation practices, soil quality, moisture content, and various other
minor factors (Koopmans and Koppejan, 1998). The RPR of tops and leaves range between 17 and 30% as mentioned earlier. The average
value of 22% was selected for estimating tops and leaves generation. The amount of tops and leaves that has to be left in the field for agricultural
purposes (soil fertility and weeds control) depends on sugarcane variety, climate, soil, etc. (Hassuani et al., 2005). The SAF factor represents
the proportion of unused amount of tops and leaves divided by the annual total amount of tops and leaves generated. A significantly large
33S. Jenjariyakosoln et al. / Energy for Sustainable Development 23 (2014) 32–45

amount of tops and leaves is being open burned in the field, which could be considered as unused amount (DEDE, 2005). Collection efficiency
refers to the fraction of residues that can be collected from the field as compared to the total amount generated. The tops and leaves recovery
was calculated based on the collection efficiency of the baler of 70% (DEDE, 2005; EPPO, 2010a; Mendoza et al., 2002) and the surplus avail-
ability factor of 74% (DEDE, 2005).
The tops and leaves were assumed to have a constant distribution over the circular catchment area and distributed around sugar mills. The radius
of the catchment area was estimated from Eq. (2) which accounted for the winding nature of the road and the straight line distance for the radius by
multiplying with the square root of two (Delivand et al., 2011a; Rentizelas et al., 2009).
Radius kmð Þ ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Top sand leaves tð Þ
SAF Â Collection efficiency Â Farmland factor Â π Â Average yield of top sand leaves
t
km2

v
u
u
u
t
Â
ffiffiffi
2
p
: ð2Þ
The assumption was that 25% of the land is occupied by infrastructure (road, building, etc.), leaving 75% of the farm area to be actually cultivated
by sugarcane which indicates the farmland factor. These assumptions are based on Delivand et al. (2011a). The average yield (t/km2
) of tops and
leaves is based on sugarcane production in 2011/12 using the average RPR value above (OAE, 2012).
Electricity production in sugar mill
Description of power production from bagasse in Thailand
Sugar mills have generally used bagasse as fuel to cogenerate heat and power using boilers and turbines (back pressure or condensing turbines
with or without controlled extraction). In Thailand, the bagasse generated is about 25–31% of sugarcane with an average of 28% (PDTI, 2011).
Currently, some of the bagasse generated in the crushing season is stored to serve as fuel for the sugar re-melting process and power generation
in the off-season. A few advanced sugar mills equipped with high boiler steam pressures produce electricity in the off-season by using stored bagasse
combined with other biomass (e.g. wood bark). Moreover, the other byproduct of sugar i.e. molasses, is actually sold by the sugar mills to producers of
liquor and animal feed, but site survey and literature data revealed that at the advanced sugar mills with 70 bar and 103 bar cogeneration systems, the
molasses produced is used for ethanol production as an additional income source (ONEP, 2013). Therefore, these advanced sugar mills can sell part of
the surplus steam and electricity to the ethanol plant, the remaining being sold to the national grid.
Options for increasing energy output from sugar mills
The potential of electricity generation depends on type of boiler and turbine, and operating configuration (pressure and temperature) of the co-
generation systems (Bhatt and Rajkumar, 2001; Bocci et al., 2009; Khatiwada et al., 2012; Siemers, 2010). Review of local research revealed that the
sugar mills in Thailand use 20 bar, 30 bar, 40 bar, 70 bar, and 103 bar steam pressure boilers as shown in Table 1 (DEDE, 2008; OCSB, 2007a; ONEP,
2013; PDTI, 2011; Siemers, 2010; Tossanaitada, 2008). It must be noted that the 20 bar configuration, which is used in about half of the sugar mills,
actually represents a small range of boilers with pressures varying between 20 and 28 bars. The turbine technologies used for boilers with pressure
20, 30 and 40 bars are back pressure steam type whereas those with 70 and 103 bars are extraction condensing type.
In this study, based on the above review, three scenarios have been developed, S1 is the baseline scenario representing the current situation, S2 is
the scenario where the feedstock is increased to extend the number of operating days and S3 is the scenario where the power production technology
is upgraded to 103 bar pressure boiler and feedstock increased. The details of each scenario are described as follows.
Scenario S1 represents the prevailing conditions for sugar milling and power production in Thailand with steam generation pressures at 20 bar,
30 bar, 40 bar, 70 bar, and 103 bar and different turbine technologies mentioned above. The number of operating days varies between 120 and
240 days as shown in Table 2, with an average of 140 days.
Scenario S2 is similar to S1 but the operating time is increased to 300 days per year (140 days crushing season and 160 days off-season). The
additional fuel requirement due to the increased number of operating days is met by tops and leaves.
Scenario S3 assumes the upgrading of all power production systems to 103 bar boiler pressure combined with extraction condensing steam
turbine. The operating time is 300 days per year. Here also, the additional fuel requirement is met by tops and leaves.
To estimate the additional fuel for Scenarios S2 and S3, a sugar mill representative from each group of boilers (20, 30, 40, 70 and 103 bars) was
selected for conducting energy balance calculations. These representative mills were surveyed for the collection of primary data such as operating
time, crushed sugarcane amount, the average data per hour of bagasse consumption, heat and electricity requirements, and data related to steam
inlet and outlet. The surplus electricity (to be exported to the national grid) for each representative group was then calculated as kWh/tc. The
Table 1
Grouping of current cogeneration technologies used in sugar mills in Thailand.
Technologya
Sugar mill Sugarcane delivered in 2011/12b
Electricityc
export
Amount (t) % (kWh/tc)
Group 1: no export 4 2,732,300 2.79
Group 2: back pressure steam turbine, 20 bar, 360 °C 28 49,822,070 50.85 5.13
Group 5: extraction condensing steam turbine, 70 bar, 507 °C 3 8,566,992 8.74 69.34
Group 6: extraction condensing steam turbine, 103 bar, 515 °C 3 15,157,784 15.47 88.78
Total/average 46 97,979,690 100.00 26.67
a
Derived current technologies in used in Thailand from PDTI (2011), OCSB (2007a), Siemers (2010), Tossanaitada (2008), ONEP (2013) and a 5-site survey (2010–2012).
b
Delivered data of sugarcane delivered to sugar mills in Thailand in 2011/12 (OCSM, 2013).
c
Estimated based on the current excess electricity generated according to a 5-site survey.
34 S. Jenjariyakosoln et al. / Energy for Sustainable Development 23 (2014) 32–45

total electricity export potential for each group was then calculated by summing up the amount of electricity exported by each representative mill.
Finally, the results of surplus electricity were multiplied by the amount of sugarcane to obtain the total electricity exported for each group
(see amount of crushed sugarcane in each group in Table 1).
Life cycle GHG emissions estimation method
Goal and scope of the life cycle GHG emissions study
One goal of the study is the estimation of the life cycle GHG emissions from power production using sugar residues as fuel. The life cycle GHG
emissions for the different scenarios outlined in Options for increasing energy output from sugar mills section were compared with those from an
equivalent amount of electricity produced from natural gas combined cycle power plant which the former are supposed to substitute following
the Thailand PDP 2010 plan. The data of life cycle GHG emissions from conventional natural gas combined cycle power plant in Thailand were
sourced from Phumpradab et al. (2009). The GHGs included in the study are CO2, CH4 and N2O with 100 year-global warming potentials of 1, 25
and 298, respectively (IPCC, 2007). The additional data and assumptions used in the Life Cycle Assessment (LCA) are shown in Table 3.
The study covered 42 sugar mills which produce and sell electricity; these account for 97% of the national sugar production. Most of the existing
sugar mills in Thailand use bagasse as primary fuel for producing steam and electricity which are used in the sugar milling process as well as exported
outside the mill in case of excess (EPPO, 2010b; ONEP, 2013; Mendoza et al., 2002; OCSB, 2007b). GHG emissions for power production from bagasse
result only from the combustion in boilers for power production. Tops and leaves are used as additional fuel for Scenarios S2 and S3 to meet the target
of the scenarios. The GHG emissions for tops and leaves include those from the supply chain and combustion in boilers for power production. The
GHG emissions from the construction of sugar mills and associated power plants have not been included in the system boundary because the
large throughput and long lifetime make the impacts of their establishment and decommissioning per unit of product insigniﬁcant. The system
boundaries are shown in Fig. 1.
Table 2
Gross generated electricity and electricity production export for S1 and S2.
Items Unit S1 S2
Group 2 Group 3 Group 4 Group 5 Group 6 Group 2 Group 3 Group 4 Group 5 Group 6
20 bar 30 bar 40 bar 70 bar 103 bar 20 bar 30 bar 40 bar 70 bar 103 bar
Gross generated electricity MWh/y 33,745 89,288 246,671 394,092 385,081 61,585 119,875 309,725 503,549 534,900
Own consumption MWh/y 27,719 54,050 146,886 145,831 111,140 53,908 81,468 186,617 198,073 155,392
Electricity export into the grid MWh/y 6026 35,238 99,785 238,625 265,565 7677 38,407 123,108 295,840 371,204
Electricity export to the ethanol plant MWh/y 9636 8376 9636 8376
Steam export to the sugar mill t/y 60,752 347,446 691,508 937,793 1,044,155 79,344 378,697 853,140 1,038,061 1,223,262
Steam export to the ethanol plant t/y 125,611 109,182 125,611 109,182
Total bagasse demand t/y 329,216 607,134 1,237,849 963,591 837,561 329,216 607,134 1,237,849 963,591 837,561
Bagasse demand for electricity export
into the grid
t/y 30,381 150,405 341,028 542,139 536,806 26,590 133,090 354,683 525,957 540,177
Bagasse demand for electricity export for
ethanol plant
t/y 21,892 16,931 17,131 12,188
Total tops and leaves demand t/y 74,795 69,566 114,830 125,990 128,376
Tops and leaves demand for electricity
export into the national grid
t/y 6454 16,416 35,199 74,021 89,071
Tops and leaves demand for electricity
export for the ethanol plant
t/y 2411 2010
Operating time, (24 h/day) Days/y 123 216 241 194 183 300 300 300 300 300
Table 3
Factors for estimating GHG emissions from surplus electricity generation.
Item Value/assumptions Remark/references
Average sugarcane products
Sugarcane harvest per hectare 76.80 t sugarcane OAE (2012)
Bagasse produced 28% of sugarcane PDTI (2011)
Tops and leaves available 11.4% of sugarcane Estimated value in Tops and leaves quantity and area estimation
section
Sugar produced (raw sugar) 10.45% of sugarcane OCSM (2013)
Molasses produced 4.48% of sugarcane OCSM (2013)
Tops and leaves supply chain process (unit/t tops and leaves)
Baling 1.75 L diesel/t Calculated based on ASAEa
method
Loading 0.60 L diesel/t Pilot project data of a Thai advanced sugar mill
Truck transport 0.46–1.90 L diesel/t Adapted from Delivand et al. (2011a)
Stacking (storage) 0.60 L diesel/t Calculated based on the ASAE method
Shredding 21 kWh/t Pilot project data of a Thai advanced sugar mill
Average lower heating value (LHV) of bagasse 7.6 MJ/kg, around 50% moisture content (MC) 5 Sugar mills from site survey
Average LHV of tops and leaves 14.12 MJ/kg, around 10% MC A Thai advanced sugar mill; EFE (2006); Junginger et al. (2001)
Diesel combustion in vehicles 3.2 kg CO2e/L GEMIS 4.8 (2013)
Bagasse combustion in boilers 11.23 kg CO2e/MWh National Greenhouse Gas Inventory Committee (2007),
Australian Government Department
Tops and leaves combustion in boilers 20.78 kg CO2e/MWh National Greenhouse Gas Inventory Committee (2007),
Australian Government Department
Thai natural gas combined cycle power plant 539.46 kg CO2e/MWh Phumpradab et al. (2009)
a
Stands for American Society of Agricultural and Biological Engineers.

The GHG emissions of surplus electricity for the representative of each scenario were calculated using economic allocation with 1 MWh as the
functional unit following Eqs. (3) and (4) (Nguyen and Gheewala, 2008; Ramjeawon, 2008; Renouf et al., 2011).
AFEE ¼
MEE Â Pelectricity

ðMrawsugar Â Psugar þ
Xn
i¼1
ðMi Â PiÞ
ð3Þ
where AFEE is the allocation factor of the surplus electricity, MEE is the surplus electricity (MWh) per 100 tc, Pelectricity is the purchasing electricity
price (USD/MWh), Mraw sugar is the average raw sugar generated per 100 tc, Psugar is the price of raw sugar (USD/t raw sugar), Mi is the mass of
co-product i to n per 100 tc, Pi is the price of co-product i to n (USD per unit of co-product), and n is the number of co-products.
GHG emissions kg CO2e=MWhð Þ ¼
ð FB Â EFGHG Bð Þ þ FTL Â EFGHG TL þ EFGHG BTLð Þð Þ Â AFEE
MEE
ð4Þ
where FB (t) is the amount of bagasse consumption per 100 tc, EFGHG_B (kg CO2e/t) is the GHG emission factor of bagasse used as fuel, FTL (t) is
the amount of top and leaves consumption per 100 tc, EFGHG_TL (kg CO2e/t) is the GHG emission factor of top and leaves used as fuel, and EFGHG_BTL
(kg CO2e/t) is the GHG emission factor of biomass supply chain process.
Sugar
Milling
Sugar
Molasses
Steam
Electricity
generation
Ethanol
plant
Sugarcane
cultivation
Mill mud
Boiler ash
Tops and leaves
(collecting,
transportation,
shredding)
Bagasse
Electricity
to grid
Excess
steam
electricity
Steam
Electricity
Fig. 1. System boundary indicated by the dashed outline.
Power plant
(MW)
Cost
(USD/MW)
Selling steam to sugar mill
Sale
Selling bagasse
Grid
Own use
Tops and leaves
baling
Road
transportation
Cost
(USD/MW)
Pretreatment
size reduction
Machinery cost
Operating cost
Labor cost
Fuel cost
Fuel cost
Labor cost
OM cost
Contingency cost
Interest capital
cost
Ethanol
plant
Fig. 2. Scope of the project cost evaluation.

GHG emission reduction analysis
The estimated GHG emissions of electricity export in S1 and S2 for national grid were summed from the results of multiplying GHG emis-
sions (kg CO2e/MWh) of each group with the generated electricity export for the grid for that group. The capacities of sugar mills in Thailand are
between 2300 and 40,000 tcd; however, the trend is towards larger capacity mills. Hence, the largest scale mill was used for the GHG emission
calculations in S3. As the excess electricity generated will displace natural gas power generation as explained above, the GHG emission reductions
for S2 and S3 were calculated using Eq. (5).
GHG emission reductioni ¼ GHGSi– GHGS1ð Þ −DESi−S1 Â GHGNG ð5Þ
where GHG emission reductioni (kg CO2e) is the GHG emission reduction of scenario i (i = 2, 3), GHGSi (kg CO2e) is the GHG emissions of the elec-
tricity exported in scenario i (i = 2, 3), GHGS1 (kg CO2e) is the GHG emissions of the electricity exported in S1, ΔESi − S1 (MWh) is the difference of
electricity exported between scenario i (i = 2, 3) and S1, GHGNG (kg CO2e/MWh) is the GHG emissions per MWh of electricity generated from natural
gas combined cycle power plant (Phumpradab et al., 2009).
Life cycle economic analysis method
Another goal of the study is to evaluate the economic aspect of surplus electricity production of the different sugar mill conﬁgurations in the three
scenarios. The economic analysis considered costs and incomes of the entire processes related to excess electricity generation for export. The costs were
categorized into fuel costs and the costs of cogeneration system for electricity production. Fig. 2 shows the boundary of the project economic evaluation.
Table 4
Machinery and related costs.
Machinery and parameter Unit Value
Big rectangular baler
Purchasing cost of a new machinea
USD 57,258
Investment costb
USD 25,726
Insurance cost, 0.6%b
USD 2205
Repair and maintenance costb
USD 41,367
Tractor, PTOc
-84HP
Purchasing cost of a new machined
USD 35,472
Investment costb
USD 15,261
USD 2943
USD 35,756
Crab loader, PTO-70 HP (made in Thailand)
Purchasing cost of a new machinee
USD 30,390
Investment costb
USD 11,207
USD 2522
USD 30,634
Shredder
Purchasing cost of a new machinef
USD 31,990
Investment costb
USD 3915
USD 503
USD 14,715
a
The purchasing price of the big rectangular baler was derived from (Hassuani et al., 2005), the USD price is escalated to the base year 2013.
b
Calculations and parameters based on the ASAE approach and (Delivand et al., 2011a).
c
PTO (power take off) is the maximum power (PTO-hp) of the machine.
d
Kubota tractors models M8540.
e
The crab loader was made in Thailand (a sugarcane farmer, Kanchanaburi province).
f
The purchasing price of shredder was derived from a site survey.
Table 5
Major parameters of the agricultural machinery; ASAE standards (Adapted from Painter, 2011; Delivand et al., 2011a).
Machinery Estimated life (h)–years RFV a
(% of new costs) Repair factor
RF1b
RF2b
Four-wheel tractor (12,000)–15 22.92 0.007 2.0
Large square baler (3000)–10 28.37 0.10 1.8
Shredder (2000)–10 34.97 0.23 1.0
a
Remaining on farm value after their economic life time.
b
RF1 and RF2 refer to repair and maintenance factors — ASAE standards.
Table 6
Major assumptions for the activity duration time (h) in a round trip (Adapted from Delivand et al., 2011a).
Activity Unit Time
Stop time, loading and uploading of bales h 0.40
Baling time of the bale h/t 0.125
Hauling time and staking time h/t 0.166

Fuel costs
Bagasse alone is used as fuel for heat and power generation in the sugar mills in Scenario S1 whereas bagasse along with tops and leaves are
used in Scenarios S2 and S3. Fuel costs of bagasse and top and leaves are different because bagasse is a process based residue generated at the
sugar mill, while the tops and leaves are field based residues. As bagasse is generated at sugar mills, it was considered as a cost-free fuel for
sugar mills in the past. However, after the extensive use of bagasse for power production, some of which is exported to the grid, it has acquired
a substantial market price. In this study, the fuel cost of bagasse was assumed 8.0 USD/t (250 THB/t). Tops and leaves are used as the secondary
fuel for power generation. As they are field based residues, the cost of collecting, transportation, and pretreatment processes must be included.
Although tops and leaves are not traded at the commercial market in Thailand, the price of 1.6 USD/t (50 THB/t) of tops and leaves is assumed
from the experience of a pilot project that paid farmers at the field. The overall fuel costs of the tops and leaves are calculated as a sum of the
price paid to farmers at the field along with the subsequent cost of handling (baling and field hauling), transport, processing (shredding) and
storage (stacking) at the power plant.
The purchasing prices of most machinery such as tractors, flatbed trailers and crab loaders were obtained from companies in Thailand (Kubota, Hino,
local company) as shown in Table 4. Studies have shown the economic advantage of using large straw baling systems (Delivand et al., 2011a; Hassuani
et al., 2005); hence, large balers were assumed for collecting tops and leaves at the field. As these are currently not available in Thailand, the price
information was used from literature after adjusting for the base year 2013 (for July 2013; 1 USD = 31.26 THB) (Hassuani et al., 2005). The machinery
costs are divided into ownership costs (depreciation costs, insurance and interest rates) and operating costs (repair and maintenance, fuel and lubrica-
tion, and labor costs).
The calculation of ownership costs and operating costs of agricultural machinery was based on the machinery management data of the American
Society of Agricultural and Biological Engineers (ASAE) Standards (Delivand et al., 2011a; Painter, 2011). Based on the aforementioned assumptions
and calculations, the input data related to machinery are listed in Table 5. The transport distances were different depending on the radius of the catch-
ment area (see Tops and leaves quantity and area estimation section). The major assumptions in assessing the time duration of the trip for the pro-
jection model are summarized in Table 6. The used data and major assumptions for the road transportation assessment are presented in Table 7. It is
also assumed that the portion of the tops and leaves that need to be stored for use in the off season share the same facility as the bagasse. Thus there is
no additional cost for storage space, only some cost associated with stacking. On average, a wage of 2 USD/h was assumed for the drivers of tractors,
crab loaders, and shredders. The specific cost components of biomass supply chain of tops and leaves were calculated for delivering one ton of tops
and leaves from the field to preparing the residues as a ready fuel for sugar mills.
Cost of electricity exported to the grid
The cost consideration of electricity exported to the grid for the three scenarios is mainly composed of operating and fuel costs. The method of
calculating fuel costs is already shown in Fuel costs section. The Scenarios S1 and S2 do not include the capital costs because the mills generate
heat and electricity from the existing steam boilers and system configurations. In the third scenario (S3), all boilers were upgraded to the 103-bar
steam pressure for which the sugar mills required an investment for setting up a new power plant. Thus, the costs included the capital cost of
installing new boilers and turbines along with operating and fuel costs.
Capital costs. The costs of the boilers, turbines, and other related machinery were obtained from DEDE (2008), ONEP (2013), and PDTI (2011). The
cost of boilers and turbines also increase with the steam temperature (Bhatt and Rajkumar, 2001). The boiler and turbine costs of 392 USD/kW,
782 USD/kW, 1011 USD/kW, 1278 USD/kW, and 1393 USD/kW were assumed for 20 bar, 30 bar, 40 bar, 70 bar, and 103 bar models, respectively.
The total investment cost including equipment costs, and related costs of capital parameters is shown in Table 8. The major assumptions listed in
Table 8 were adapted from PDTI (2011). The costs of electricity transmission installation were also included in the investment cost. It was assumed
that around 70% of total investment cost will come from a bank loan over 10 years at an annual interest rate of 7%. The remaining 30% will be invested
by the owner in the first year of project.
Operating costs. Operating costs included the maintenance and repair costs, insurance costs, labor costs, and fuel costs.
Maintenance and repair, and insurance cost. The annual maintenance and repair costs of the power plants were 4% of equipment costs. An annual
insurance cost of 1.5% of equipment costs was also assumed in the financial analysis models.
Table 7
Transportation parameters (adapted from Delivand et al., 2011a).
Parameter and activities Unit Value
Purchasing of the trailera
USD 100,128
Salvage costb
10% of purchasing cost
Maximum volume capacity m3
100
Loading weightc
ton 21.2
Average travel speed km/h 40
Insurance and maintenance costb
Miscellaneous costb
Life time Years 7
Average annual traveled distanceb
km 80,000
Fuel consumptiond
L diesel/100 km 41.7
Diesel coste
USD/l 0.97
Driver hourly pay rate — 8 h shift USD/h 2.0
a
Obtained from a dealer of Hino Co. in Thailand (20.08.2013). Approximate dimensions: 2.5 × 2.5 × 14 (m); maximum loading capacity 37 t.
b
Adopted from Huisman et al. (1997), Delivand, et al. (2011a).
c
Based on the permitted volume capacity of the truck and number of bales (a bale dimension and weight is 0.8 × 0.87 × 1.9–295 kg) that can be loaded.
d
The value was obtained from IPCC (1996).
e
Average retail price of the first six months in 2013 for H-diesel (EPPO, 2013).

Labor cost. The number of laborers in the sugar mill for Scenario S2 did not increase despite the increase in number of operating days as they are
already employed all year round for maintenance, etc. For Scenario 3, the operator requirement for new power plant was estimated by Eq. (6)
(Delivand et al., 2011b). In addition, the number of laborers was allocated to excess electricity.
Number of laborers ¼ 13:761 MWeð Þ
0:4328
: ð6Þ
For this estimation, three shifts were considered along with the total annual labor and operator requirements, consisting of the numbers of
ordinary workers, skilled workers, engineers, supervisors, managers, and office staff. The average annual income and annual bonus per person
were assumed at 12,796 USD based on PDTI (2011).
Income conditions
In the three scenarios, income was generated in two ways, surplus electricity and surplus steam, with several different prices. The purchasing
price of 0.09 USD/kWh was assumed based on the firm electricity tariff regulations of the Thailand Energy Policy and Planning Office (EPPO) for
S3
-
20
40
60
80
100
120
140
S1 S2
20 bar
30 bar
40 bar
70 bar
103 bar
average
kWh/tc
ElectricityexportpotenƟal(kWh/tc)
Fig. 3. Comparison of electricity export per ton of sugarcane for S1, S2 and S3.
Table 8
Basic of calculations for financial analysis (adapted from PDTI, 2011).
Cost component Unit Value
Equipment cost
Cost of electric motorsa
106
USD SM: 3.07, L: 3.57
Boilers turbinesb
USD/kW
Economic parameters
Loan % 70
Equity % 30
Interest rate on loanc
% 7
Payback period of loan years 10
Capital parameters
Grid connection costd
Electricity export capacity:
≤20 MW 106
USD 1.22
≥20 MW 106
USD 6.77
Life time Years 20
Cost of engineering and civil work % 1.0% of equipment cost
Cost of construction time insurance % 0.5% of equipment cost
Contingency cost % 2.0% of equipment cost
Discount rate % 10
Operating parameters
Average annual incomes of a labor USD/person 12,796
Annual operating and maintenance cost % 4.0% of equipment cost
Annual insurance cost % 1.5% of equipment cost
Fuel cost
Bagasse USD/t 8
Tops and leaves e
Revenue parameters
Internal steam soldf
USD/t 2.06
Unit price of electricity sold of SPP-firmg
USD/kWh 0.09
Feed-in premium (adder) for first 7 yearsg
USD/kWh 0.01
a
S, M, and L are the sizes of sugar mill capacities: small size (b10,000 tcd), medium size (N10,000 tcd and b20,000 tcd), and large size (N20,000 tcd) (PDTI, 2011).
b
The costs were shown in Fuel costs section.
c
Based on the MLR interest rate of term loans for corporate customers of typical commercial banks.
d
Derived from literatures (DEDE, 2008; ONEP, 2013).
e
Tops and leaves fuel costs were shown in Power export potential from sugarcane residues to the national grid and GHG reduction potential of utilizing the surplus bagasse, and tops
and leaves for power production sections.
f
Derived from the ONEP, 2013.
g
Derived from Sutabutr, 2013.

all surplus electricity export. The adder premium of 0.01 USD/kWh for first 7 years was assumed for surplus electricity supplying into the national
grid (Sutabutr, 2013). The steam selling rate was assumed as 2.08 USD/t for all sugar mills and some ethanol plant (ONEP, 2013).
Evaluation of the economics of excess electricity production
Economic criteria for this analysis comprised production cost, NPV (net present value), and IRR (internal rate of return). The methods considered
the most general scheme, and have been frequently used in many technical-economic analyses (Delivand et al., 2011b; Moomaw et al., 2011;
Siemers, 2010). A discounted future stream of net incomes was used by applying 10% project-specific discount rate to evaluate the desirability of
the project fiscal measures. This is the highest rate including the risk premium reflecting a longer time perspective for investments from three
suggested values (i = 3, 7 and 10%) of the IPCC special report on renewable energy sources and climate change mitigation for the cost evaluation
of biomass power plants (Moomaw et al., 2011).
The production cost (USD/MWh) is the ratio of the total life time costs including the value of the capital, fuel costs, and operating costs to the
life-time net delivered electricity (to the grid) (Moomaw et al., 2011). The production cost model can be determined through following Eq. (7).
Production cost USD=MWhð Þ ¼
Xn
t¼0
Costt= 1 þ rð Þ
t
Xn
t¼0
Electricity export= 1 þ rð Þ
t
ð7Þ
where Costt (USD) is the cost in the year t, n (year) is the project life-time (20 years), and r is 10% discount rate.
Table 10
Quantities of products for S1: electricity export using pure surplus bagasse as fuel (from 100 tc), including the economic allocation factor.
Unit Bagasse power generating systems
Group 2 Group 3 Group 4 Group 5 Group 6
20 bar 30 bar 40 bar 70 bar 103 bar
Flow of bagasse within furnace
Bagassea
t 28.00 28.00 28.00 28.00 28.00
Quantities of products
Raw sugarb
t 10.45 10.45 10.45 10.45 10.45
Molasses (to animal feed)b
t 4.48 4.48 4.48
Molasses (to ethanol)b
t 4.48 4.48
Electricity exporta
MWh 0.51 1.63 2.26 6.93 8.88
Electricity export (to ethanol)a
MWh 0.28 0.28
LP steam to ethanola
t 3.65 3.65
Economic allocation factors
Raw sugarc
% 92.32 90.83 90.00 83.91 81.78
Molasses (to animal feed)c
% 6.92 6.81 6.75
Molasses (to ethanol)c
% 6.29 6.13
Electricity exportc
% 0.76 2.36 3.25 9.68 11.98
LP steam to ethanolc
% 0.11 0.11
GHG emission
Electricity export kg CO2e/MWh 4.64 4.57 4.53 4.22 4.11
Electricity exportd
tCO2e 1186 841 1061 2507 5531
a
Based on the energy balance of each representative sugar mill.
b
Derived from the average of sugar and molasses products of sugarcane production statistics in 2011/12 (OCSM, 2013).
c
Based on the average economic values in 2012–2013: raw sugar 533.86 USD/t (OAE, 2012), molasses 93.35 USD/t (BOT, 2012), electricity price 0.09 USD/kWh (Sutabutr, 2013), and
internal steam price 2.06 USD/t (ONEP, 2013).
d
For each group, the GHG emissions from electricity export are calculated for all the sugar mills within the group.
Table 9
Gross generated electricity and electricity production export for S3.
Items Unit S3
103 bar (2300 tcd) 103 bar (40,000 tcd)
Gross generated electricity MWh/y 57,162 941,538
Own consumption MWh/y 16,596 273,416
Electricity export into the grid MWh/y 39,671 653,451
Electricity export to the ethanol plant MWh/y 895 14,745
Steam export to the sugar mill t/y 132,212 2,163,426
Steam export to the ethanol plant t/y 11,669 192,213
Total bagasse demand t/y 83,194 1,370,346
Bagasse demand for electricity export into the grid t/y 57,737 951,032
Bagasse demand for electricity export for ethanol plant t/y 1303 21,460
Total tops and leaves demand t/y 13,721 226,001
Tops and leaves demand for electricity export into the national grid t/y 9521 156,828
Tops and leaves demand for electricity export for the ethanol plant t/y 215 3539
Operating time, (24 h/day) Days/y 300 300

The Thailand Board of Investment (BOI) provides a policy to support the renewable energy investment by offering the application of a privilege
corporate income tax exemption. Based on that, the tax payment was assumed as follows: 0% tax rate for first 8 years, 15% for the next 5 years, and
30% for the remaining years (Asawachintachit, 2012).
NPV is the sum of the initial investment and the present value of all future cash flows at a particular discount rate, and it is calculated through
following equation (Chau et al., 2009).
NPV ¼ −I0 þ
Xn
t¼1
Ct
1 þ rð Þn ð8Þ
where I0 (USD) is the initial investment (new constructing investment of power plant), Ct (USD) is the cash flow in period t (year), n (year) is the
project life-time (20 years), and r is discount rate of 10%.
Table 11
Quantities of products for S2: electricity export after expanding operating times and using tops and leaves as a secondary fuel (from 100 tc), including the economic allocation factor.
Unit Power generating systems of sugarcane residues
Group 2 Group 3 Group 4 Group 5 Group 6
20 bar 30 bar 40 bar 70 bar 103 bar
Flow of sugarcane residues within furnace
Bagassea
t 28.00 28.00 28.00 28.00 28.00
Tops and leavesa
t 6.03 3.29 2.60 3.66 4.29
Raw sugarb
t 10.45 10.45 10.45 10.45 10.45
Molasses (to animal feed)b
t 4.48 4.48 4.48
t 4.48 4.48
Electricity exporta
MWh 0.65 1.77 2.78 8.60 12.31
Electricity export (to ethanol) MWh 0.28 0.28
LP steam to ethanol t 3.65 3.65
Raw sugarc
% 92.12 90.62 89.30 82.01 78.18
Molasses (to animal feed)c
% 6.91 6.79 6.69
% 6.15 5.86
Electricity exportc
% 0.97 2.59 4.01 11.73 15.96
% 0.11 0.11
GHG emission
Tops and leaves supply chain processd
kg CO2e/t 12.53 12.53 12.53 12.53 12.53
Electricity export kg CO2e/MWh 7.81 6.15 5.77 5.77 5.76
Electricity exporte
tCO2e 2543 1233 1668 4249 10,739
a
Own calculation based on the energy balance of each representative sugar mill.
b
c
Based on the average economic values of the references in Table 10.
d
Based on the estimated GHG emission amount of tops and leaves supply chains process of the average daily sugar mill capacity in Thailand (16,200 tcd).
e
For each group, the GHG emissions from electricity export are calculated for all the sugar mills within the group.
Table 12
Quantities of products from minimum and maximum sugar mill capacity (tcd) of S3: upgrading power generation configurations as 103-bar and 515 °C and using tops and leaves as the
secondary fuel (from 100 tc), including the economic allocation factor.
Unit Power generating systems of sugarcane residues
(2300 tcd) (40,000 tcd)
Flow of sugarcane residues within furnace
Bagassea
t 28.00 28.00
Tops and leavesa
t 4.29 4.29
Raw sugarb
t 10.45 10.45
t 4.48 4.48
Electricity exporta
MWh 12.31 12.31
Electricity export (to ethanol) MWh 0.28 0.28
LP steam to ethanol t 3.65 3.65
Raw sugarc
% 78.26 78.26
% 5.87 5.87
Electricity exportc
% 15.76 15.76
% 0.11 0.11
GHG emission
Tops and leaves supply chain processd
kg CO2e/t 10.06 14.75
Electricity export kg CO2e/MWh 5.59 5.85
Electricity export tCO2e – 68,535
a
Own calculation based on the energy balance of each representative sugar mill.
b
c
Based on the average economic values of the references in Table 10.
d
Based on the estimated GHG emission amount of tops and leaves supply chain process of smallest and largest scales of daily sugar mill capacities.

IRR was used to decide whether or not a project would be feasible for investment towards a new power plant. Project IRR is frequently used for
making investment decisions; it is a specific rate calculated by the sum of cash flows after tax in which the NPV (net present value) of the project is
zero. A project can be economically feasible when the IRR is higher than the accepted required rate of return for investors. The accepted IRR should be
above 11% as the minimum required rate of return for private investors in the case of Thailand as suggested by the Department of Alternative Energy
Development and Efficiency (Delivand et al., 2011b).
Results and discussion
Power export potential from sugarcane residues to the national grid
Scenario 1
Table 1 provides the summary of cogeneration technologies in
Thailand. The data show a significant increase in export potential of
surplus electricity generated under the new technology of extraction
condensing turbines. Even with the older technology, increasing pres-
sure and temperature contributes to an increased export potential.
The electricity export and operating time of each group in S1 are
shown in Table 2.
The operating times of the first three configurations i.e. sugar mill
models of 20 bar, 30 bar, and 40 bar were based on their current opera-
tion using bagasse fuel. The electricity from sugar mills in Thailand is
currently being generated mainly from bagasse fuel (EPPO, 2010b).
The numbers of operating days of sugar mill models of 70 bar, and
103 bar were estimated based on their bagasse availability with the ac-
tual conditions of heat and power generation and consumption. The
electricity export of each sugar mill model in each group is shown in
Table 1. The 28 sugar mills with 20 bar boilers, covering 51% of the sug-
arcane processing, could deliver approximately 5 kWh/tc of electricity
to the grid. The mills with 30 and 40 bar boilers, comprising 22% of sug-
arcane processing, could deliver 16–23 kWh/tc of electricity to the grid.
The high electricity export potential group of 70 bar and 103 bar steam
pressure representing 24% of sugarcane processing was 69–89 kWh/tc.
The average electricity exported by sugar mills in Thailand increased
from 14.5 kWh/tc in 2006 to approximately 26.67 kWh/tc in 2012.
Scenario 2
The different increases of electricity export potential for each boiler
pressure in S2 are shown in Table 2 and Fig. 3. S2, which increases elec-
tricity export by expanding operating time to 300 operating days and
using tops and leaves as the secondary fuel, could export about
34.86 kWh/tc, or 3416 GWh/y. About 23% of the generated tops and
leaves were sufficient as the secondary fuel to generate heat and
power generation for sugar mill use, and for electricity export. The elec-
tricity export of Thailand was increased by approximately 802 GWh/y
or 31% compared to the former systems characterized by less efficient
low pressure boilers with back pressure turbines.
Scenario 3
The electricity export potentials of the two sugar mill capacities
(2300 and 40,000 tcd) in S3 are shown in Table 9. Fig. 3 confirms that
the electricity exported in S3 at as high as 123 kWh/tc could be achieved
via an improvement in power generation potential from upgrading all
boiler and system configurations to 103 bar and 515 °C with extraction
condensing steam turbine. The tops and leaves fuel required for heat
and power generation of sugar mills in this case is only 19% of
the generated amount. The electricity export was approximately
11,715 GWh/y, increasing by 9102 GWh/y from the current
situation.
GHG reduction potential of utilizing the surplus bagasse, and tops and
leaves for power production
The co-products of surplus electricity, and molasses generated in
sugar mills are significant in economic value (Ramjeawon, 2008). The
issue of surplus steam and electricity from the power plant being used
at the ethanol production plant in sugar mills of 70 bar and 103 bar
models in Thailand as aforementioned in Description of power
production from bagasse in Thailand section was also investigated.
The GHG emissions of the biomass supply chain for tops and leaves for
S2 were based on the average sugarcane crushing capacity of
16,200 tcd whereas for S3 were based on 2300 tcd and 40,000 tcd. The
amount of feedstock requirement for increasing electricity export by
expanding operating time was evaluated by electric efficiency, and the
average lower heating value (LHV) of bagasse, and tops and leaves
(shown in Table 3). The quantities of generated products and the results
of the economic allocation factors of each of the groups in each scenario
are shown in Tables 10, 11, and 12. The life cycle GHG emissions for
purely bagasse-derived electricity are estimated at 4.11–4.64 kg CO2e/
MWh whereas those from mixed residues (bagasse combined with
tops and leaves) are 5.75–7.81 kg CO2e/MWh (Table 11). On the other
hand, GHG emissions from natural gas combined cycle power plants
are about 540 kg CO2e/MWh (Phumpradab et al., 2009), two orders of
magnitude higher than those from electricity generation from sugar-
cane residues.
The results of GHG emission reduction from the electricity export
generated by sugarcane residues replacing the electricity generated by
natural gas combined cycle power plants in S2 and S3 are 423 ktCO2e,
and 4853 ktCO2e respectively. Obviously, the more biomass-based
power generated, the greater the reduction in GHG emissions as
compared to conventional fossil-based power.
Economic analysis results
The cost consideration was separated into two categories i.e. costs of
fuel and power production using improved technology. The fuel cost is
the cost of feedstock and of biomass supply chain process varying
with the amount of biomass in that area and distance of transportation,
specific for tops and leaves. For bagasse, which is generated in the mill
itself, only the cost of feedstock is considered. The difference in the
cost of power production is not only from the cost of main equipment
(boiler and turbine) but also the cost of operation and fuel.
Table 13
Estimated machinery ownership and operating costs for handling tops and leaves.
Machine Ownership operating cost Fuel and lubrication cost Labor cost Twine cost Total
(USD/t) (USD/t) (USD/t) (USD/t) (USD/t)
Big rectangular baler 4.60 0.55 5.15
Tractor, PTO 84 hp 0.85 1.94 0.25 3.04
Crab loader, PTO 70 hp (hauling and loading) 0.47 0.65 0.17 1.29
Crab loader, PTO 70 hp (stacking 50% of tops and leaves fuel) 0.24 0.33 0.08 0.65
Shredder 3.99 0.40 4.39

Fuel costs
The fuel cost of tops and leaves included a feedstock price of
1.6 USD/t at the field which is paid to the farmers and the costs of bio-
mass supply chain process. The biomass supply chain model was devel-
oped for delivering sugarcane to sugar mills across the entire range from
2300 to 40,000 tcd. This range is the current smallest and largest actual
daily crushed sugarcane amount at sugar mills in Thailand during 2011/
12. The results of estimation based on Tables 4–7 and methodology of
ASAE standards are shown in Table 13. The costs included specific own-
ership and operating costs, and twine cost of baler for handling tops and
leaves in the field, at the storage site, and for shredding. The specific cost
of biomass supply chain excluding the transportation cost would be
14.5 USD/t. The cost was mainly from big rectangular baler (35%), and
shredder (30%). Moreover, the increase of tops and leaves fuel cost
shown in Fig. 4 was from a minimum of 18.8 USD/t for 2300 tcd to
21.5 USD/t for 40,000 tcd of sugar mill capacity. The reason for the
cost variations in the two capacities of sugar mills was the variation of
the estimated round radius distance from 15 km for the smallest scale
of sugar mill capacity to a maximum of 61 km for the largest scale. If
we assume daily sugarcane capacity of 5000, 10,000, 20,000, and
40,000 tcd, the specific biomass supply chain costs would be 19.2,
19.7, 20.5, and 21.5 USD/t, respectively. The economic analysis shows
that increasing the system capacity from 2300 tcd to 40,000 tcd, a 17.4
fold increase, resulted in an increase of the specific tops and leaves
fuel costs by 14.6%.
Technology improvement
Based on the financial evaluation of the configurations in S1 and S2,
the economic criteria including the production cost, and NPV over the
life-time of the systems have been computed, and the results are
shown in Table 14. Site survey revealed that sugar mill capacities of
9600, 13,000, 24,000, 22,000 and 22,000 tcd were equipped with boilers
having pressures 20 bar, 30 bar, 40 bar, 70 bar and 103 bar, respectively.
From the estimated tops and leaves fuel cost varying in terms of sugar
mill capacities in Fuel costs section, the tops and leaves costs of 19.70,
20.00, 20.60, 20.50 and 20.50 USD/t were assumed for models of
20 bar and 30 bar, 40 bar, 70 bar and 103 bar respectively. Table 14
shows the production costs including operating costs and fuel costs of
each group in S1 and S2. The fuel costs of the groups in S2 were higher
than those in S1. However, the operation cost per unit of electricity
(USD/MWh) of each group in S2 was significantly lower than that in
S1. The range varied from 19 to 59%, (note 59% reduction of 20-bar
17.00
17.50
18.00
18.50
19.00
19.50
20.00
20.50
21.00
21.50
22.00
2,300 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
Costsoftopsandleaves(USD/t)
Sugarmill capacities (ton sugarcane per day)
21 km
37km
48 km
61 km
15 km
30 km
43km
53km 57 km
Fig. 4. Trend in tops and leaves fuel costs with various sugar mill capacities and travel-distances.
Table 14
Specific production costs and NPV values for S1 and S2.
Items Unit S1 S2
Group 2 Group 3 Group 4 Group 5 Group 6 Group 2 Group 3 Group 4 Group 5 Group 6
20 bar 30 bar 40 bar 70 bar 103 bar 20 bar 30 bar 40 bar 70 bar 103 bar
1. Operating cost
Operating and maintenance cost USD/MWh 5.31 6.04 6.99 11.33 13.04 2.18 4.35 5.60 7.38 8.00
Insurance cost USD/MWh 1.99 2.26 2.62 4.25 4.89 0.82 1.63 2.10 2.77 3.00
Total operating cost 7.30 8.30 9.61 15.58 17.93 3.00 5.98 7.70 10.14 11.00
2. Fuel cost USD/MWh
Bagasse fuel cost USD/MWh 40.32 34.14 27.33 18.17 16.17 27.70 27.71 23.04 14.22 11.64
Hypothetical price of tops and leaves
Fuel cost (@ 1.6 USD/t of feedstock) USD/MWh 16.49 8.51 5.9 5.13 4.91
3. Production cost USD/MWh 47.62 43.34 36.94 33.75 34.10
@ 1.6 USD/t of feedstock USD/MWh 47.19 42.20 36.64 29.49 27.55
4. NPV value 106
USD 3.00 18.62 52.23 125.40 137.56
@ 1.6 USD/t of feedstock 106
USD 3.84 20.03 64.78 161.55 204.54
USD 3.77 19.83 64.40 160.68 203.49
USD 3.55 19.26 63.18 158.06 200.38

sugar mill configuration, 39% of 103 bar, and 35% of 70 bar). The results
should convince investors of the benefits of expanding operating time
to 300 days. The advantages achievable through reduction in produc-
tion costs and increasing positive values of NPV for the groups in S2
without capital investment for newer technology must be taken note of.
Assuming that a market of tops and leaves is established, it can be
expected that the price paid to the farmers may also increase. Apart
from the modeled base price of 1.6 USD/t, calculations were also done
for a 3.2 USD/t as well as 8 USD/t (equalling the current assumed
price of bagasse following the experience of rice straw in Thailand).
The increase from 1.6 to 8 USD/t resulted in an increase of only 4–11%
of the production costs and decrease in NPV by 2–8% (Table 14).
Investment analysis results for S3
The systems in S3 were examined for the effects of the different
scales on new investment of equipment and installation. For S3, the
specific production costs varied from a minimum of approximately
63 USD/MWh for the large capacity sugar mill to a maximum of
80 USD/MWh for the small scale. The production cost of biggest sugar
mill (40,000 tcd) was around 22% lower than the smallest one
(2300 tcd) (Table 15). The change in market prices of tops and leaves as-
sumed earlier generates a 2–3% increase in production costs, and 4–14%
decrease in NPV values (Table 15). These figures confirm the financial
viability of the interventions.
The IRR values for the power plants associated with the 2300 and
40,000 tcd sugar mills were 17% and 27% respectively which, being
higher than 11%, are highly favorable. If the feed in tariff of
0.01 USD/kWh (0.3 THB/kWh) offered by the government is included
in the calculations, the IRR values further increase to 20% and 31% for
the small and large systems respectively. These values clearly show
the rationalization for investments.
The sensitivity analysis of investment appraisal condition on IRR
≥ 11% was considered i.e. in case of 40,000 tcd capacity of 103-bar
sugar mill, even if fuel price, selling price of electricity, capital cost,
and the plant factor were changed by +2.35 folds (26.79 USD/t of ba-
gasse fuel cost, and 72.07 USD/t of tops and leaves fuel cost), −43.80%
(0.051 USD/kWh), +1.15 folds (308 Million USD) and −49.72%
(3620 h/y), respectively, investment will still remain appealing
(see Table 16). The larger scale sugar mill capacity is financially more
robust and is less sensitive to all factors compared to the smaller one.
Conclusions
Increasing the number of operating days for electricity generation in
existing sugar mills using top and leaves as the supplementary fuel
could provide a potential gain of 31% in surplus electricity (8.2 kWh/tc).
Additionally, using upgraded technology of boiler steam pressure of 103
bar and 515 °C and extraction condensing steam turbine technology
showed a potential of a 3.5 fold increase in surplus electricity generation
for export as compared to the current situation. For this case, the electric-
ity export to the grid is 123 kWh/tc and the secondary fuel used is only
19% of generated tops and leaves. The electricity export from sugar
mills with state-of-the-art technology and use of bagasse and tops and
leaves as fuel would be 9 TWh or 65% of biomass power target of the
AEDP 2012–2021 with 4.8 Mt CO2e of GHG emission reduction or 6% of
the AEDP 2012–2021 target. The electricity generation costs and NPV
values show that the high boiler steam pressure configuration and the
reduction in production cost can lead to higher benefits than the current
situation. The results confirmed that despite the large investments for the
state-of-the-art technology, the IRR was still higher than 11% (the mini-
mum pre-requisite rate of return). Therefore, these provide an attractive
investment option, especially for large sugar mill capacities. The result in
cost analysis of tops and leaves supply chains in different scales of sugar
mill capacities (2300–40,000 tcd) also showed less sensitivity to the dif-
ferent scales of sugar capacities and different transportation distances.
Table 16
Percentage variation of the individual variables to yield at IRR = 11% for S3.
Variable (Δ% of base values) S3
103 bar (2300 tcd) 103 bar (40,000 tcd)
Fuel price, Δ% +135 +235
Electricity selling price, Δ% −24.2 −43.8
Capital cost, Δ% +49.5 +115
Plant factor, Δ% −27.2 −49.7
Table 15
Production costs and NPV values for S3.
Items Unit S3
103 bar (2300 tcd) 103 bar (40,000 tcd)
1. Capital cost USD/MWh 42.26 33.03
2. Operating cost
Operating and maintenance cost USD/MWh 9.44 7.89
Labor cost USD/MWh 8.52 1.88
Insurance cost USD/MWh 3.54 2.96
Total operating cost USD/MWh 21.49 12.72
3. Fuel cost
Bagasse USD/MWh 11.64 11.64
Hypothetical price of tops and leaves
Fuel cost (@ 1.6 USD/t of feedstock) USD/MWh 4.51 5.16
4. Production cost
@ 1.6 USD/t of feedstock USD/MWh 79.90 62.55
5. NPV value
USD 6.94 196.23
USD 6.81 194.37
USD 5.98 188.90
6. IRR value
@ 1.6 USD/t of feedstock % 20.00 31.26

Acknowledgments
The authors would like to thank Dr. Bundit Fungtammasan for his
technical comments and suggestions. This Ph.D. work was financially
supported by the Joint Graduate School of Energy and Environment
(JGSEE), King Mongkut’s University of Technology Thonburi (KMUTT),
Bangkok, Thailand.
References
Asawachintachit D. Thailand, a perfect place for your business to grow. Thailand: Thailand
Board of Investment; 2012 [Retrieved January 21, 2014, from: http://www.boi.go.th/
upload/content/Tax%20seminar_Feb%202012_53851.pdf].
Bakos GC, Tsioliaridou E, Potolias C. Technoeconomic assessment and strategic analysis of
heat and power co-generation (CHP) from biomass in Greece. Biomass Bioenergy
2008;32:558–67.
Bhatt MS, Rajkumar N. Mapping of combined heat and power systems in cane sugar
industry. Appl Therm Eng 2001;21:1707–19.
Bhattacharya SC, Abdul Salam P, Runqing H, Somashekar HI, Racelis DA, Rathnasiri PG,
et al. An assessment of the potential for non-plantation biomass resources in selected
Asian countries for 2010. Biomass Bioenergy 2005;29:153–66.
Bocci E, Di Carlo A, Marcelo D. Power plant perspectives for sugarcane mills. Energy 2009;34:
689–98.
BOT. Molasses price in Thailand in 2012. Bank of Thailand; 2012 [Retrieved September 01,
2013, from: http://www.bot.or.th/Thai/EconomicConditions/Thai/Northeast/com-
modities/Doclib_CommodityMonthly/Ethanol%20Monthly–01-56.pdf].
Chau J, Sowlati T, Sokhansanj S, Preto F, Melin S, Bi X. Techno-economic analysis of wood
biomass boilers for the greenhouse industry. Appl Energ 2009;86:364–71.
DEDE. The assessment of agricultural residues availability for energy resources in Thailand.
Department of Alternative Energy Development and Efficiency; 2005 [Retrieved
May 20, 2013, from: http://e-lib.dede.go.th/mm-data/Bib10630-1.pdf].
DEDE. Co-generation biomass. Department of Alternative Energy Development
and Efficiency; 2008 [Retrieved May 20, 2013, from: http://e-lib.dede.go.th/
mm-data/Bib11329.pdf].
DEDE. The Renewable and Alternative Energy Development Plan for 25 percent in
10 years (AEDP 2012–2021). Thailand: Department of Alternative Energy Develop-
ment and Efficiency; 2012 [Retrieved December 05, 2012, from: http://www.
dede.go.th/dede/images/stories/dede_aedp_2012_2021.pdf].
DEDP. The study of behavior and patterns of energy use in plantation: summary report.
Thailand: Department of Energy Development and Promotion; 1992.
Deepchand K. Commercial scale cogeneration of bagasse energy in Mauritius. Energy Sus-
tain Dev 2001;1:15–21.
Deepchand K. Promoting equity in large-scale renewable energy development: the case
of Mauritius. Energy Policy 2002;30:1129–42.
Delivand MK, Barz M, Gheewala SH. Logistics cost analysis of rice straw for biomass
power generation in Thailand. Energy 2011a;36:1435–41.
Delivand MK, Barz M, Gheewala SH, Sajjakulnukit B. Economic feasibility assessment of
rice straw utilization for electricity generating through combustion in Thailand.
Appl Energ 2011b;88:3651–8.
EFE. Biomass analysis values. Bangkok, Thailand: Energy for Environment Foundation;
2006 [Retrieved November 11, 2007, from: http://www.efe.or.th].
EPPO. Study of a small-scale biomass power plant for rural communities (Phase II).
Thailand: Energy Planning and Policy Office, Ministry of Energy; 2010a.
EPPO. Power Plant Community Development Fund: statistics of monthly fee of biomass
power producers paid into the fund based on the amount of monthly electricity ex-
port into the grid. Thailand: Energy Planning and Policy Office, Ministry of Energy;
2010b [Retrieved January 11, 2013, from: http://www.eppo.go.th/cdf/Document/
Fund%20Income.xls].
EPPO. Power generation by type of fuel. Thailand: Energy Planning and Policy Office, Min-
istry of Energy; 2012 [Retrieved August 18, 2013, from http://www.eppo.go.th/info/
5electricity_stat.htm].
EPPO. H-diesel price statistics of January–July 2013. Thailand: Energy Planning and Policy
Office, Ministry of Energy; 2013 [Retrieved August 18, 2013, from: http://www.eppo.
go.th/index-E.html].
GEMIS 4.8. Global Emission Model Integrated System Version 4.8. 2013. Oko-Institute for
applied Ecology; 2013 [Retrieved August 18, 2013, from: http://www.iinas.org/
gemis.html].
Gheewala SH, Bonnet S, Prueksakorn K, Nilsalab P. Sustainability assessment of a bio
refinery complex in Thailand. Sustainability 2011;3:518–30.
Guzman PL, Valdes A. Heat and power cogeneration at a Cuban sugar mill based on
bagasse and trash as fuel: the “Hector Molina” project. Energy Sustain Dev
2000;4:90–2.
Hassuani SJ, Leal MRLV, Macedo IC. Biomass power generation — sugarcane bagasse and
trash; 2005. p. 57–63 [Brazil].
Huisman W, Venturi P, Molenaar J. Cost of supply chains of Miscanthus giganteus. Ind Crop
Prod 1997;6:353–66.
IEA. Renewable energy policy considerations for deploying renewable. Paris: Organization
for Economic Co-operation and Development/International Energy Agency; 2011.
IPCC. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. The Inter-
governmental Panel on Climate Change; 1996.
IPCC. IPCC fourth assessment report (AR4) — climate change 2007. Intergovernmental
Panel on Climate Change; 2007.
Junginger M, Faaij A, Broek VDR, Koopmans A, Hulscher W. Fuel supply strategies for
large-scale bio-energy projects in developing countries. Electricity generation from
agricultural and forest residues in Northeastern Thailand. Biomass Bioenergy 2001;
21:259–75.
Khatiwada D, Seabra J, Silveira S, Walter A. Power generation from sugarcane biomass
— a complementary option to hydroelectricity in Nepal and Brazil. Energy 2012:
1–14.
KMUTT. Assessment of potential amount solid-biomass from agricultural residues and
agro-residues from sawed timber, furniture and short rotation tree for heat and
power generation in Thailand. Thailand: King Mongkut's University of Technology
Thonburi; 2006. p. 2–42.
Koopmans A, Koppejan J. Agricultural and forest residues-generation, utilization and
availability. FAO; 1998 [Retrieved November 22, 2012, http://wgbis.ces.iisc.ernet.
in/energy/HC270799/RWEDP/acrobat/p_residues.pdf accessed November 10,
2010].
Larson ED, Williams RH, Leal MRLV. A review of biomass integrated-gasifier/gas turbine
combined cycle technology and its application in sugarcane industries, with an analysis
for Cuba. Energy Sustain Dev 2001;1:54–76.
Macedo IC, Leal MRLV, Hassuani SJ. Sugar cane residues for power generation in the
sugar/ethanol mills in Brazil. Energy Sustainable Dev 2001;1:77–82.
Mendoza TC, Samson R, Elepano AR. Renewable biomass fuel as “Green Power” alterna-
tive for sugarcane milling the Philippines. Philipp J Crop Sci 2002;27:23–39.
Moomaw W, Burgherr P, Heath G, Lenzen M, Nyboer J, Verbruggen A. Annex II:
methodology. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Seyboth K,
Matschoss P, Kadner S, Zwickel T, Eickemeier P, Hansen G, Schlömer S, von
Stechow C, editors. IPCC Special Report on Renewable Energy Sources and
Climate Change Mitigation. United Kingdom and New York, NY, USA:
Cambridge University Press, Cambridge; 2011.
National Greenhouse Gas Inventory Committee. Australian methodology for the estima-
tion of greenhouse gas emissions and sinks 2006. Canberra: Energy (stationary
sources) Australian Government Department of Climate Change; 2007.
Nguyen TLT, Gheewala SH. Life cycle assessment of fuel ethanol from cane molasses in
Thailand. Int J Life Cycle Assess 2008;13:301–11.
OAE. Basic information of agricultural crops in Thailand in 2009/10–2011/12.
Thailand: Office of Agricultural Economics, Ministry for Agricultural and Cooper-
atives; 2012.
OCSB. In-depth study energy efficiency improvement for sugar industries. Faculty of
Engineering Chulalongkorn University, Bangkok: Office of the Cane and Sugar
Board, Ministry of Industry, Thailand; 2007a.
OCSB. The value added for sugarcane and sugar industry. Thailand: Office of the Cane and
Sugar Board. Ministry of Industry; 2007b.
OCSM. Statistics of sugarcane production in 2002/03–2012/13. Thailand: Office of the
Cane and Sugar Management. Ministry of Industry; 2013.
ONEP. Environmental impact assessment reports. Thailand: Office of Natural Resources
and Environmental Policy and Planning; 2013.
Painter K. Costs of owning and operating farm machinery in the Pacific Northwest. U.S.: A
Pacific Northwest Extension Publication University of Idaho, Washington State
University, Oregon State University; 2011
PDTI. Assessment of energy efficiency development from heat and power gener-
ation in sugar factories in Thailand. Thailand: The Pilot Plant Development
and Training Institute (PDTI), King Mongkut's University of Technology
Thonburi; 2011.
Phumpradab K, Gheewala SH, Sagisaka M. Life cycle assessment of natural gas power
plants in Thailand. Int J Life Cycle Assess 2009;14:354–63.
Ramjeawon T. Life cycle assessment of electricity generation from bagasse in Mauritius. J
Clean Prod 2008;16:1727–34.
Renouf MA, Pagan RJ, Wegener MK. Life cycle assessment of Australian sugarcane
products with a focus on cane processing. Int J Life Cycle Assess 2011;16:
125–37.
Rentizelas AA, Tolis AJ, Tatsiopoulos IP. Logistics issues of biomass: the storage problem
and the multi-biomass supply chain. Renew Sust Energ Rev 2009;13:887–94.
Sajjakulnukit B, Yingyuad R, Maneekhao V, Pongnarintasut V, Bhattacharya SC, Abdul
Salam P. Assessment of sustainable energy potential of non-plantation biomass
resources in Thailand. Biomass Bioenergy 2005;29:214–24.
Siemers W. Greenhouse gas balance for electricity production from biomass resources in
Thailand. Sust Energ Environ 2010;1:65–70.
Sutabutr T. Business opportunities in Thailand's renewable energy, ASEAN clean energy
trade, technology and investment forum. Manila, The Philippines: USTDA-ASEAN-
BCIU; 2010. p. 19–21 [April].
Sutabutr T. Updated renewable energy policies: case of Thailand. 2013 International Con-
ference on Alternative Energy in Development Countries and Emerging Economies.
Bangkok, Thailand: AEDCEE; 2013. [May].
Tossanaitada W. Cogeneration efficiency enhancement in sugar mills [MD Dissertation]
Bangkok, Thailand: King Mongkut's University of Technology Thonburi; 2008.
UNFCCC. CDM Project documents, 2014. United Nations Framework Convention on
Climate Change. Retrieved June 13, 2014, from: http://cdm.unfccc.int/Projects/
projsearch.html.
US.EIA. Overview Thailand is a net importer of oil and natural gas, although the country is
a grower producer of natural gas. Retrieved February 25, 2014 http://www.eia.gov/
countries/analysisbriefs/Thailand/thailand.pdf, 2013.
WEC. World energy resources. Retrieved June 12, 2014 http://www.worldenergy.
org/wp-content/uploads/2013/10/WEC_Resources_summary-final_180314_TT.
pdf, 2013.
Yuttitham M, Gheewala SH, Chidthaisong A. Carbon footprint of sugarcane produced from
sugarcane in eastern Thailand. J Clean Prod 2011;19:2119–27.

Residuos de caña de azucar

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Similar to Residuos de caña de azucar

Similar to Residuos de caña de azucar (20)

Recently uploaded

Recently uploaded (20)

Residuos de caña de azucar