DOI: 10.3303/CET2188188

Paper Received: 17 June 2021; Revised: 5 July 2021; Accepted: 1 October 2021
Please cite this article as: Ng W.P., Ong C.T., Lim C.H., How B.S., Lam H.L., 2021, An Optimum Biomass Supply Network Synthesis Using the
Elemental Targeting Approach, Chemical Engineering Transactions, 88, 1129-1134 DOI:10.3303/CET2188188

CHEMICAL ENGINEERING TRANSACTIONS

VOL. 88, 2021

A publication of

The Italian Association
of Chemical Engineering
Online at www.cetjournal.it

Guest Editors: Petar S. Varbanov, Yee Van Fan, Jiří J. Klemeš

ISBN 978-88-95608-86-0; ISSN 2283-9216

An Optimum Biomass Supply Network Synthesis Using the
Elemental Targeting Approach

Wendy P. Q. Nga,b*, Chin Tye Ongb, Chun Hsion Limc, Bing Shen Howd, Hon Loong
Lame
a Universiti Teknologi Brunei, Jalan Tungku Link Gadong, BE1410 Brunei Darussalam
b Curtin University Malaysia, CDT 250, 98000 Miri, Sarawak, Malaysia
c Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras 43000 Kajang, Selangor, Malaysia
d Swinburne University of Technology Sarawak Campus, Jalan Simpang Tiga, 93350 Kuching, Sarawak, Malaysia
e University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor, Malaysia

wendyngpq@gmail.com

Malaysia has been relying on fossil fuels as the major source of energy despite the global trend in transitioning
towards using renewable energy. It is crucial for the country to execute energy transformation to reduce its
reliance on fossil fuels. Malaysia with its agriculture industries as one key economy sector, generates abundant
biomass from the agriculture activities. The biomass has been underutilised despite the country’s policy to
promote waste-to-wealth strategy. This work aims to trigger the industrial energy transition in the country and
tackling climate protection through the adoption of local and underutilised biomass energy. In this work, an
optimal biomass fuel formulation is developed using the elemental targeting approach. The modelling approach
blends multiple species of underutilised biomass in the country for biofuel production to achieve feedstock
security while maintaining acceptable biofuel properties. A conceptual biofuel product which could be fed into
the existing energy extraction technology for consumption is targeted. The underutilized biomass wastes in
Malaysia are first reviewed and their respective element characteristics are studied. Then, the underutilised
biomass is integrated into the biomass supply chain and an optimum biofuel formulation is evaluated using the
element targeting approach. The modelling result indicates that hemicellulose, cellulose and volatile matter are
the major limiting characteristics that constrain the supply of biomass to the gasification plants and biomass of
low cost is preferred in the biomass supply network.

1. Introduction

1.1 Biomass as fuel

In Malaysia, approximately 168 Mt of biomass waste is being generated annually, which mainly constitutes of
oil palm waste, wood residues, rice husks, and coconut trunk. Stich et al. (2017) estimated the available biomass
energy in Malaysia as shown in Table 1.

Table 1. Available energy by solid waste residues in Malaysia (Stich et al., 2017)

Biomass Total available energy (GWh) Biomass Total available energy (GWh)
Paddy waste 6,295.17 Coconut 1,224.42
Corn waste 182.99 Coffee 39.58
Sugarcane 140.49 Groundnut 3.04
Oil palm waste 33,578.52 Forestry residues 17,372.47
Cassava 13.82

Pelletization technology has been used for a long time and is gaining popularity among Asian countries. The
increasing demand for wood pellet has inspired the research toward alternative biomasses that have been
overlooked in the past. However, the variation of quantity and quality of biomass supplied is difficult to predict
due to various external factors, such as seasonal availability, weather, soil condition, etc. The fluctuation of

1129

biomass feedstock supply would directly affect the quality of biofuel product as well as creating inconsistent
energy supply to downstream biofuel users. This poses challenge to biomass utilisation.
Biomass generally requires the pre-treatment and densification processes to remove impurities, minerals, and
moisture before it is ready to be converted. The challenges faced in biomass energy plant are the high capital
and operating costs for biomass drying, chemical pre-treatment, grinding, pelletizing and wastewater treatment.
Typically, plants with higher production capacity benefit from lower capital cost. However, inconsistent supply
of biomass and high transportation cost has limited the establishment of higher capacity bioenergy plant.
Diversification of biomass feedstock is one solution to reduce the risk of supply disruption as more biomass
feedstock are integrated into the supply chain. Additional feedstock choices do not only provide flexibility in
biomass pellet production but also creates a buffering effect when there is fluctuation in biomass supply. The
blending of different biomasses has the potential to reduce transportation cost through the integration of local
underutilized biomass in the supply chain. The cost-saving from the integration of local biomass into the supply
chain is especially remarkable for large scale pellet producers considering local underutilized biomass is
cheaper and has lower transportation costs.

1.2 Integrated biomass fuel

The growing interest for cheap yet effective biomass pellet as biofuel has created market demands for
alternatives sources of biomass pellet. However, the supply and quality of biomass feedstock have been
fluctuating due to weather, cultivation, harvesting method and soil condition (Yancey et al., 2013). Integrating
multiple species of biomass feedstock into the biomass pellet supply chain may even out the inconsistency of
biomass supply and characteristics. Through this strategy, the desirable characteristics of different biomass are
combined to result in a feedstock that is more suitable for biofuel production. Biomass with a longer period of
seasonal harvest or waste generated during replantation of crops requires larger storage capacity to
accommodate the huge quantity at once. Through the diversification of biomass input, the storage size can be
reduced. This could add versatility and economic advantage to high capacity biofuel processing plant and allow
unpopular biomass to be blended into the supply chain. Wattana et al. (2017) studied the characteristics of
mixed biomass pellet made from oil palm frond (OPF) and rubber tree residues and observed an improvement
in the density of pellet from 935 kg/m3 to 1,023 kg/m3. Various researchers have conducted elemental analyses
on different biomass wastes by performing proximate analysis to study the moisture content, ash content, volatile
matter, and fixed carbon of biomass as well as ultimate analyses to investigate the carbon, hydrogen, nitrogen,
oxygen and sulphur contents in the biomass. Yancey et al. (2013) demonstrated the benefits of mixed biomass
by using biomass pellet made of switchgrass, eucalyptus, corn stover and lodgepole pine.

1.3 Elemental targeting approach

The Elemental Targeting Approach was first introduced by Lim and Lam (2014a) as a tool to predict the
applicability of biofuel made of different biomass species in commercial application. Through this method,
biomasses are categorised according to their elemental characteristics. Lim et al. (2016) found a linear
relationship between the feedstock element characteristics with the element characteristics of the pyrolysis
product which becomes the basis of the elemental targeting approach to predict the element characteristics of
mixed biomass feedstock. The elements such as ash content, fixed carbon (FC), volatile matter (VM), moisture
(MC), heating value (HV), cellulose (Cel), hemicellulose (Hcell) and lignin (Lig) were found to be the key
elements in determining the quality of biofuel products (Lim and Lam, 2016). This work aims to integrate as
much as possible the different types of local underutilized biomass into the biomass supply chain to maximize
resource utilization using the elemental targeting approach. There were existing studies which integrate different
types of biomass into the biomass supply chain to optimise the supply chain performance. However, most of
the studies considered only the economic and sustainability performances of the supply chain and less focus
has been made on the practicability of the integration and the technological constraint to accommodate the
change of feedstock in the biomass supply chain. In this work, the technological feasibility which is constrained
by the biomass’s elemental composition is considered as one main element for biomass supply chain synthesis.
An optimum biofuel formulation made of multiple biomass feedstock is targeted in this work, which potentially
and conceptually forms the basis for biofuel composition design using local and underutilised biomass.

2. Model formulation and case study

The integrated biofuel product can be made of a combination of biomass, such as palm kernel shell (PKS), palm
mesocarp fiber (PMF), rice husk (RH), rice straw (RS), oil palm trunk (OPT), oil palm frond (OPF), wood residue
(Wood) and empty fruit bunch (EFB). The compositions of biomass feedstock are then determined from the
model using the element acceptance range (EAR). The following objective function and equations are developed
to constrain the model.

1130

The typical mass balance formulation applies to constrain the biomass availability and mass flow from its source
point to the pre-treatment facilities and along the biomass supply chain. These general equations can be found
elsewhere in the published documents such as Ng et al. (2015) for biomass allocation and cost calculations.
The amount of biomass’s element that enters the pre-treatment facility (Elemassm,e,p

bm ) is equal to the product of the
mass of biomass (Mm,pbm,in) and the biomass’s elemental composition (Em,ebm ).

Elemassm,e,p
bm

= Mm,p
bm,in × Em,e

bm
∀ m ∈ M, e ∈ E, p ∈ P (1)

where m is the types of biomass, e is the characteristics of the biomass and p is the pre-treatment facility.
Biomass requires pre-treatment such as leaching and drying. During the process, biomass experiences change
in elemental composition. Eq.2 accounts for the elemental composition changes and mass loss by using yield
factor. The element mass flow of processed biomass (Elemassm,e,p

pbm ) is calculated as the product of the element
mass flow of raw biomass and their respective yield factor (Yieldm,e

PT ) listed in Table 3.

𝐸𝑙𝑒𝑚𝑎𝑠𝑠𝑚,𝑒,𝑝
𝑝𝑏𝑚

= 𝐸𝑙𝑒𝑚𝑎𝑠𝑠𝑚,𝑒,𝑝
𝑏𝑚 × 𝑌𝑖𝑒𝑙𝑑𝑚,𝑒

𝑃𝑇 ∀ 𝑚 ∈ 𝑀, 𝑒 ∈ 𝐸, 𝑝 ∈ 𝑃 (2)

In the processing hub, the yield factors used for biomass pellet and syngas production (𝑀𝑗
𝑝𝑟𝑜𝑑𝑢𝑐𝑡) are 1 t/t feed

and 1,940 Nm3/t feed.

𝑀𝑗,𝑝
𝑝𝑟𝑜𝑑𝑢𝑐𝑡

= 𝑀𝑗,𝑝
𝐹𝑒𝑒𝑑 × 𝑌𝑖𝑒𝑙𝑑𝑗,𝑝

𝑃 𝑗 ∈ 𝐽, 𝑝 ∈ 𝑃 (3)

where j is the types of biomass products in the processing plant.
The pellet and syngas produced are used for electricity generation. The capacity of the power plant is limited at
the working capacity of 90 %. The costs involved in pre-treatment, processing technology vary for different
biomass and pre-treatment processes. The processing cost of biomass consists of the installation cost of the
power plant, the annual fixed cost and variable cost. The total processing cost equals to the sum of processing
cost in each processing hubs.
The total profit of the system which involves the raw material cost (Ctotc

rawmat), pre-treatment cost (Ctotc
pretreat),

processing cost (Ctotc
process) and transportation cost (Ctotc

Trans) is to be maximised.

Max. Profit = ∑(Revenuej − Ctotj
rawmat

− Ctotj
pretreat

− Ctotj
process

− Ctotj
Trans

)

j =1

(4)

A case study was performed to integrate the major biomass in one of the states in Malaysia to investigate the
biomass integration potential using the element targeting approach. A Mixed Integer Linear Program (MILP)
model was constructed to conceptually formulate the composition of an integrated biomass fuel produced using
local underutilized biomasses. The model generated is solved using General Algebraic Modelling System
(GAMS). As shown in Figure 1, the system model consists of 5 major sections: biomass originated from a
resource point is transferred into pre-treatment facilities. After pre-treatment, biomass is evaluated based on
their element characteristics that is capable to fit into the biofuel consumption technology. The integrated
biomass fuel with composition that satisfies the element acceptance of the corresponding processing technology
is produced for electricity generation in the biomass power plant. The model aims to obtain an optimal
composition of biomass to produce solid biofuel from pelletization process and syngas from the gasification
process. To conduct this modelling, parameters such as production cost, technology conversion yield and
biomass element characteristics (Table 2) were obtained from the literature.

Figure 1: The model structure for integrated biofuel production

Mainstream
Biomass 1

Resource

Combustion
EAR

Pellet
Mixture 1

Element
Acceptance

Range (EAR)

Gasification
EAR

Biofuel

Product

Mainstream
Biomass 2

Mainstream
Biomass 3

Underutilise
d Biomass 4

Underutilise
d Biomass 5

Pellet
Mixture 2

Syngas 3

Syngas 4

Pre-

treatment

Pre-treatment 1

Pre-treatment 2

Electricity
Output

Biomass

Plant

1131

Table 2: Element characteristics (weight fraction) of different types of biomass

Material Cel Hcell Lig MC Ash VM FC HV
(MJ/kg)

Reference

PKS 0.2092 0.2293 0.5123 0.0613 0.0173 0.7216 0.1998 20.09 Soh et al. (2019)
PMF 0.389 0.1936 0.3311 0.0678 0.0303 0.6959 0.2061 19.06 Soh et al. (2019)
EFB 0.3907 0.3527 0.2284 0.0785 0.0221 0.7167 0.1826 18.88 Soh et al. (2019)
OPF 0.304 0.404 0.217 0.1600 0.0109 0.7014 0.1277 17.28 Guangul et al. (2012)
RH 0.286 0.286 0.243 0.1171 0.1162 0.6381 0.1286 16.56 Lim et al. (2016)
RS 0.32 0.357 0.223 0.0740 0.1699 0.6945 0.0616 14.7 Calvo et al. (2012)
OPT 0.4581 0.1774 0.2449 0.0869 0.0323 0.6810 0.1998 17.47 Soh et al. (2019)
Wood 0.4 0.275 0.285 0.2000 0.0080 0.6560 0.1360 20.48 McKendry. (2002)

Lim and Lam (2014b) constructed the element acceptance range based on the key elements of the processing
technology as a constraint in developing the mixed biomass feedstock. These key elements are used as the
determining factors to constrain the quality of biofuel.

(a) (b)

Figure 2: (a) EAR of combustion (Lim and Lam, 2014b) and (b) EAR of gasification (Lim and Lam, 2014b)

Upon entering the processing hubs, the biomass undergoes pretreatment processes such as grinding, leaching
and drying. The cost of raw biomass and elemental yield (in terms of fraction of its original value) of the
pretreatment process are tabulated in Table 3.

Table 3: Elemental yield of biomass pre-treatment process and raw biomass cost

MC Ash VM FC HV Reference Cost (MYR/t) Reference

PKS 1.00 1.00 1.00 1.00 1.00 - 130 AIM (2013)
PMF 1.00 1.00 1.00 1.00 1.00 - 40 AIM (2013)
EFB 0.9425 0.22 0.9425 0.9425 0.9425 Chin et al. (2015) 36 Malek et al. (2017)
OPF 1.00 1.00 1.00 1.00 1.00 - 60 AIM (2013)
RH 0.8 0.1 0.8 0.8 0.8 Bazargan et al. (2015) 400 MiGHT (2013)
RS 0.95 0.1 0.95 0.95 0.95 Liu et al. (2013) 62.67 Shafie et al. (2013)
OPT 0.991 0.6 0.991 0.991 0.991 Chin et al. (2015) 13.5 MiGHT (2013)
Wood 0.6 1 1 1 1 - 50 Malek et al. (2017)

In this case study, a total of 26 resource points of oil palm and paddy biomass and 18 resource points of woody
biomass in Sabah, Malaysia are considered to supply the biomass feedstock. The planted area of oil palm and
paddy in different districts in 2016 was obtained from Department of Agriculture Sabah (2016) and the annual
volume of logs production from timber industry was obtained from the Sabah Forestry Department (2017). These
values form the biomass source points and availabilities of the case study. The model is solved to look for the
supply of biomass required to produce the biomass pellet and syngas that fulfils the EAR of the biomass fuel
consumption technologies. The biofuel is to be consumed in four pre-selected power plants (P1-P4) in the
district. P1 and P3 are combustion plants, whilst P2 and P4 are gasification plants.

100
Ash (wt%)

FC (wt%)

VM (wt%)

MC (wt%)

HV (wt%)

Cell (wt%)

Hcell (wt%)

Lig (wt%)

Upper Boundary Lower Boundary

100
Ash (wt%)

FC (wt%)

VM (wt%)

MC (wt%)

HV (wt%)

Cell (wt%)

Hcell
(wt%)

Lig (wt%)

Upper Boundary Lower Boundary

1132

3. Result and discussion

Through element targeting approach, the element characteristics of the mixed biomass feedstock of plants P1,
P2, P3 and P4 were calculated by the model as shown in Figure 3a-3d (green lines). Comparing the EAR of the
combustion plant and gasification plant, the gasification plant has a more stringent requirement on the biomass
element characteristics. The biofuel compositions in terms of element characteristics that are specific to each
of the processing plant are developed as shown in Figure 3a-3d. As shown in Figure 3b and Figure 3d, the
hemicellulose content of mixed biomass lies on the lower boundary of the EAR, while the cellulose content of
the mixed biomass lies on the upper boundary of element acceptance range. This shows that the hemicellulose,
cellulose and volatile matter are the major limiting characteristics for the gasification plant in this case study.

(a) (b)

characteristics of feed in P3, and (d) element characteristics of feed in P4

Table 4 shows the biomass feedstock requirement in the power plants. The results indicated that the biomass
selectivity were significantly affected by the market value of the biomass. Conventionally, palm kernel shell, rice
husk, and wood residues are the popular biomass feedstock for biomass power plant. However, in this study,
the utilization of these biomasses are low due to their higher costs. The model tends to select the cheaper
underutilized biomass to maximize profit as long as their element characteristics fit the EAR of the technologies.

Table 4: Biomass received in power plants

P1 P2 P3 P4
PKS (t/d) 8.00
PMF (t/d) 20.88
EFB (t/d) 66.06 39.279 20.14 51.10
OPF (t/d) 298.67 170.56 290.20
OPT (t/d) 252.92 86.164 45.41 83.63
Wood (t/d) 44.14
Total (t/d) 318.98 424.11 309.13 424.93
Profit (MYR/d) 33,315.86 13,181.50 20,556.78 12,969.01

100
Ash (wt%)

FC (wt%)

VM (wt%)

MC (wt%)

HV (wt%)

Cell (wt%)

Hcell
(wt%)

Lig (wt%)

Upper Boundary
Lower Boundary
Element Characteristics of Biomass Feed

100
Ash (wt%)

FC (wt%)

VM (wt%)

MC (wt%)

HV (wt%)

Cell (wt%)

Hcell
(wt%)

Lig (wt%)

Upper Boundary
Lower Boundary
Element Characteristics of Biomass Feed

100
Ash (wt%)

FC (wt%)

VM (wt%)

MC (wt%)

HV (wt%)

Cell (wt%)

Hcell
(wt%)

Lig (wt%)

Upper Boundary
Lower Boundary
Element Characteristics of Biomass Feed

0
20
40
60
80
Ash (wt%)

FC (wt%)

VM (wt%)

MC (wt%)

HV (wt%)

Cell (wt%)

Hcell
(wt%)

Lig (wt%)

Upper Boundary
Lower Boundary
Element Characteristics of Biomass Feed

1133

4. Conclusions

The element targeting approach was introduced to integrate to underutilized biomass for combustion plant and
gasification plant. The element acceptance range comprises cellulose, hemicellulose, lignin, moisture, fixed
carbon content, volatile matter and heating value of biomass. The model integrates low cost and underutilized
biomass in the biomass supply chain instead selecting the mainstream biomasses, as long as the element
characteristics of the mixed biomass feedstock fulfil the EAR of the technologies. The approach can be applied
to other regional scenario that biomass product is to be produced without undergoing chemical reaction. Future
work will investigate the effect of the approach on the seasonal availability of biomass feedstock. More studies
could be performed to analyse the energy cost and energy density of the biomass that may affect the selection
of the biomass in the system.

References

AIM, 2013. National Biomass Strategy 2020: New wealth creation for Malaysia’s biomass industry,
<https://renewable-carbon.eu/news/media/news-images/20111121-
08/National_Biomass_Strategy_Nov_2011_FINAL.pdf>, accessed 15 May 2021.

Bazargan A., Bazargan M., McKay G., 2015. Optimization of rice husk pretreatment for energy production.
Renewable Energy, 77, 512–520.

Calvo L.F., Gil M.V., Otero M., Morán A., García A.I., 2012. Gasification of rice straw in a fluidized-bed gasifier
for syngas application in close-coupled boiler-gasifier systems. Bioresource Technology, 109, 206-214.

Chin K.L., H’ng P.S., Paridah M.T., Szymona K., Maminski M., Lee S.H., Lum W.C., Nurliyana M.Y., Chow M.J.,
Go W.Z., 2015. Reducing ash related operation problems of fast growing timber species and oil palm
biomass for combustion applications using leaching techniques. Energy, 90, 622–630.

Department of Agriculture Sabah, 2016. Hectareage of Industrial Crops by Districts and Types of Crops, Sabah
2016, 36–38, Jabatan Pertanian Sabah.

Guangul F.M., Sulaiman S.A., Ramli A., 2012. Gasifier selection, design and gasification of oil palm fronds with
preheated and unheated gasifying air. Bioresource Technology, 126, 224–232.

Lim C.H., Lam, H.L., 2014a. Introduction of Novel Analysis Technique: Biomass Element Life Cycle Analysis
(BELCA). Chemical Engineering Transactions, 39, 337-342.

Lim C.H., Lam, H.L., 2014b. Biomass Demand-Resources Value Targeting. Energy Conversion and
Management, 87, 1202–1209.

Lim C.H., Lam H.L., 2016. Biomass supply chain optimisation via novel Biomass Element Life Cycle Analysis
(BELCA). Applied Energy, 161, 733–745.

Lim C.H., Mohammed I.Y., Abakr Y.A., Kazi F.K., Yusup S., Lam H.L., 2016. Novel input-output prediction
approach for biomass pyrolysis. Journal of Cleaner Production, 136(PB), 51–61.

Liu H., Zhang L., Han Z., Xie B., Wu S., 2013. The effects of leaching methods on the combustion characteristics
of rice straw. Biomass and Bioenergy, 49, 22–27.

Malek A.B.M.A., Hasanuzzaman M., Rahim N.A., Al Turki Y.A., 2017. Techno-economic analysis and
environmental impact assessment of a 10 MW biomass-based power plant in Malaysia. Journal of Cleaner
Production, 141, 502-513.

McKendry P., 2002. Energy production from biomass (Part 1): overview of biomass. Bioresource Technology
83(1), 37–46.

MiGHT, 2013. Malaysian Biomass Industry Action Plan 2020: Driving SMEs towards Sustainable Future,
<http://biomass-sp.net/wp-content/uploads/2013/11/MIGHT.pdf>, accessed 15 May 2021.

Ng W.P.Q., Promentilla M.A., Lam H.L., 2015. An algebraic approach for supply network synthesis. Journal of
Cleaner Production, 88, 326–335.

Sabah Forestry Department, 2017. Annual Report 2017. Sabah Forestry Department, Sandakan, Malaysia.
Shafie S.M., Mahlia T.M.I., Masjuki H.H., 2013. Life cycle assessment of rice straw co-firing with coal power

generation in Malaysia. Energy, 57, 284-294.
Soh M., Chew J., Liu S., Sunarso J., 2019. Comprehensive Kinetic Study on the Pyrolysis and Combustion

Behaviours of Five Oil Palm Biomass by Thermogravimetric-Mass Spectrometry (TG-MS) Analyses.
BioEnergy Research, 12(2), 370–387.

Stich J., Ramachandran S., Hamacher T., Stimming U., 2017. Techno-economic estimation of the power
generation potential from biomass residues in Southeast Asia. Energy, 135, 930–942.

Wattana W., Phetklung S., Jakaew W., Chumuthai S., Sriam P., Chanurai N., 2017. Characterization of Mixed
Biomass Pellet Made from Oil Palm and Para-rubber Tree Residues. Energy Procedia, 138, 1128–1133.

Yancey N., Tumuluru J.S., Wright C., 2013. Drying, Grinding and Pelletization Studies on Raw and Formulated
Biomass Feedstock’s for Bioenergy Applications. Journal of Biobased Materials and Bioenergy, 7, 549–558.

1134