Journal of Cleaner Production 314 (2021) 127927
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
Cooperative game-based anchor process allocation within sustainable palm
oil based complex for environment-food-energy-water nexus evaluation
Yue Dian Tan a, b, Jeng Shiun Lim a, b, *, Viknesh Andiappan c, Sharifah Rafidah Wan Alwi a, b
a
Process Systems Engineering Centre (PROSPECT), Research Institute of Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor,
Malaysia
b
School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Malaysia
c
School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, 1, Jalan Venna P5/2, Precinct 5, 62200, Putrajaya, Wilayah Persekutuan, Malaysia
A R T I C L E I N F O
A B S T R A C T
Handling editor: Cecilia Maria Villas Bôas de
Almeida
The challenge in clean palm oil production falls on the management of palm oil mill effluent which is a notable
source of greenhouse gas emissions and water pollution. To address these critics against edible palm oil, an
integrated palm oil-based complex (POBC) considering effluent elimination and refinery integration is suitable
for environmental-food-energy-water (EFEW) nexus development. Optimal retrofit of palm oil mill into EFEW
nexus-integrated POBC requires multi-objective considerations to balance the trade-offs between profitability,
energy contribution, greenhouse gas, water and land footprints via fuzzy optimisation. With limited practical
knowledge, potential flowsheet modifications should be investigated for flexible POBC design. In a cooperative
game context, interconnecting processes act as multiple players cooperating to achieve the goal of the game, i.e.,
POBC performance, where each player has a distinctive impact on the outcome. In this work, such process
performance was suggested to be distributed using cooperative game model, to target the EFEW-based anchor
process, i.e., the process stage of greatest contribution in the weighted EFEW nexus, for desired flowsheet
advancement. Considering these aspects, an integrated fuzzy and cooperative game optimisation framework was
developed to identify the anchor process of an EFEW nexus-integrated POBC. EFEW objective-based process
performance allocation in the fuzzy optimal POBC was weighted by the decision-maker to allocate the anchor
process using developed models and Excel tools. Nut/kernel separation and cogeneration stage is the EFEWbased anchor process in the fuzzy optimal POBC with EFEW nexus score of 41% in this work. A comparative
analysis between the proposed method with other approach was done. The favourability of EFEW contributions
by POBC in terms of benefit-drawback ratio increased with the percentage of boiler efficiency increment within
the targeted anchor process. Targeting anchor process aids planning for process maintenance and advancement
to avoid resource wastage on sub-critical processes.
Keywords:
Cooperative game
Waste elimination
Environment-food-energy-water nexus
Optimisation
Sustainable development
Multi-objective
1. Introduction
There is no doubt that palm oil industry secures a major role in global
food production by supplying 34% of the international vegetable oil
demand (The American Soybean Association, 2018). To major palm oil
exporters such as Malaysia, the plus from palm oil industry in gross
domestic product growth comes with the minus in terms of environ­
mental threats such as water pollution and climate change (Andiappan
et al., 2018). Despite the need to satisfy increasing global oilseed-based
food demand (Abdul-Hamid et al., 2020), the sustainability critics
hinder Malaysia from achieving palm oil production targets via oil
extraction rate improvement in Malaysian palm oil mills for projected
biodiesel consumption (Dompok, 2013) and palm oil economic potential
exploitation (Ministry of Economic Affairs, 2019). In palm oil mill,
increased palm oil production associates with greater energy and water
consumption, indirectly contributes to greenhouse gas (GHG) emissions
and water scarcity (Subramaniam et al., 2011) due to steam-intensive
and water-consuming milling processes. Additionally, greater palm oil
mill effluent (POME) generation is unfavourable towards water security
and GHG mitigation due to its polluting nature and GHG-emitting
anaerobic treatment (International Energy Agency (IEA), 2014).
To address the regarded concerns and comply with the mandatory
Malaysian Sustainable Palm Oil certification scheme (Shahida et al.,
* Corresponding author. Process Systems Engineering Centre (PROSPECT), Research Institute of Sustainable Environment (RISE), Universiti Teknologi Malaysia,
81310, Johor Bahru, Johor, Malaysia.
E-mail address: jslim@utm.my (J.S. Lim).
https://doi.org/10.1016/j.jclepro.2021.127927
Received 14 December 2020; Received in revised form 4 June 2021; Accepted 12 June 2021
Available online 17 June 2021
0959-6526/© 2021 Elsevier Ltd. All rights reserved.
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
List of symbols and abbreviations
Binary indicator for unprocessed product i for external
RindEXPRO
i
processing or treatment
Abbreviations
AHP
Analytic Hierarchy Process
B/D
Benefit-drawback
BOD
Biological Oxygen Demand
CHP
Combined Heat and Power
CN
Cracked Nut
CO2
Carbon Dioxide
CPLEX
IBM ILOG CPLEX Optimizer
CPO
Crude Palm Oil
DF
Digested Fruitlet
EFB
Empty Fruit Bunches
EFEW
Environment, Food, Energy and Water
FFB
Fresh Fruit Bunches
GAMS
General Algebraic Modelling System
GHG
Greenhouse Gas
GP
Gross Profit
HPS
High-Pressure Steam
LPS
Low-pressure Steam
MPS
Medium-pressure Steam
PFAD
Palm Fatty Acid Distillate
PFN
Palm Fruit Nut
PK
Palm Kernel
PKS
Palm Kernel Shell
PL
Pressed Liquid
PMF
Palm Mesocarp Fibre
POBC
Integrated Palm Oil-Based Complex
POME
Palm Oil Mill Effluent
PORE
Palm Oil Refinery Effluent
PS
Pressed Solid
RBDPOL Refined, Bleached, Deodorised Palm Olein
RBDPS Refined, Bleached, Deodorised Palm Stearin
SBR
Sequential Batch Reactor
SF
Sterilised Fruitlet
SFB
Sterilised Fruit Bunch
SSI
Shapley-Shubik Power Index
WFP
Water Footprint
Sets
f
i
p
u
z
Objective function variables
NE
Net amount of electrical energy converted from biogas or
biomass (MWh)
GP
Annual gross profit generated by POBC (USD/y)
EP
Annualised economic potential of POBC (USD/y)
GHGBAL Overall GHG impacts at the POBC (kgCO2eq/h)
LFP
Human infrastructure based land footprint (hectare)
TWFP
POBC’s WFP which accounts grey WFP and blue WFP (t/h)
λ
Fuzzy aggregate membership degree between multiple
objective functions
Parameters
AOT
Number of operation hours for POBC in a year (h/y)
AVRESLOCAL
Basis amount of available resource for import (t/h)
i
Additional
amount of intermediate resource i made
AVRESPEXT
i,p
available during failure of process p (t/h)
Cact
Pollutant concentration in actual water supply (mg/L)
Pollutant concentration of treated effluent (mg/L)
Ceff
Cmax
Maximum BOD value at waterways (mg/L)
BOD value of natural water supply (mg/L)
Cnat
EPL ,EPU Fuzzy lower and upper limits for economic potential (USD/
y)
EFEWwOBJ Defined weightages for EFEW objectives (GP, NE,
GHGBAL and TWFP) to calculate EFEW nexus score and B/
D ratio
GHGBALFuzzy Fuzzy optimal GHG balance in POBC multi-objective
optimisation (kgCO2eq/h)
GHGBALL GHGBALU Fuzzy lower and upper limits for GHG
balance (kgCO2eq/h)
LFPL ,LFPU Fuzzy lower and upper limits for land footprint (hectare)
MCMi,p Material i consumption factor in process p
Non-consumed material i for the failure of process p
MCMBY
i,p
NEL , NEU Fuzzy lower and upper limits for net energy (MWh)
PRICEi Market price for selling one unit of system-generated
product i (USD/t)
PRCMi,p Resource i conversion factor in process p
PRCMBY
Non-generated material i for the failure of process p
i,p
Set of process stages defining groups of processes based on
their specific function in POBC
Set of resources including material and product involved in
POBC
Set of processes or technology considered in POBC process
route design
Set of stand-alone process stages as basis for objective
improvement calculation
Set of process failure scenarios defining various working
combinations of process stages for different process failure
possibilities
PWOBJ
f
process stage f for EFEW objectives (GP, NE, GHGBAL and
TWFP) in cooperative game model
Ref GHG
Parameter addressing GHG emission from one unit of
i,p
material i in process p (kgCO2eq/t)
RindGHGPRO
GHG-emitting indicator for unconsumed product
i
(kgCO2eq/t)
RindGHGRES
GHG-emitting indicator for external resource import
i
(kgCO2eq/t)
TWFPFuzzy Fuzzy optimal WFP in POBC multi-objective optimisation
(t/h)
TWFPL , TWFPU Fuzzy lower and upper limits for WFP (t/h)
UCAPEXp Capital expenditure for installing required units of
process p (USD/unit)
UOPEXp Unit cost for operation of process p (USD/unit)
URCOSTi Unit import price of external material i (USD/t)
URCOSTEXT
Unit external processing cost of resource i (USD/t)
i
Binary parameters
BYindADD
Binary indicator for additional available intermediate
i
product i
Binary indicator for intermediate material i in core process
MindEXT
i,p
p to be processed externally
PindBYPASS
Binary indicator for failing process p
p
PindEXIST
Binary indicator for functioning process p
p
PindINT
p
PindREL
p
PRindREL
i,p
Marginal contributions or performance weightage for
Binary indicator for integrated milling process p
Variables
ALLOBJ
Distributed EFEW objective-based performance to process
f
Binary indicator for correlated process p
Binary indicator for intermediate resource i to be
considered in correlated process p
RindEXGEN
Binary indicator for externally generated resource i
i
AVRESi
2
stage f (GP, NE, GHGBAL and TWFP)
Overall amount of resource i available for purchase from
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
external sources (t/h)
capp
Total units of equipment operating for process p (unit)
EFF
Hourly flowrate of treated effluent (t/h)
ELECCOST Expense for grid electricity import (USD/h)
ELECREV Income from exporting self-generated electricity (USD/h)
ELECEX Required amount of grid electricity for import (MWh)
ELECEXCESS Surplus electricity generated to be sold (MWh)
EXRESi Hourly flowrate of imported material i (t/h)
n
NSEFEW
f
Number of process stage f defined
Percentage weighted-sum EFEW nexus score (%)
PALLOBJ
Percentage EFEW objective-based performance allocation
f
PRESp
PROi
SGRESi,p
imp
GPimp
Characteristic functions for GP and net energy
z , NEz
distribution defined as GP and NE improvements in
scenario z compared to basis scenario u
GPNew , NENew , TWFPNew , GHGBALNew Optimal values for EFEW
objectives (GP, NE, GHGBAL and TWFP) in GP-maximised
scenario for modified POBC
IMPOBJ
Percentage EFEW objective (for GP, NE, GHGBAL and
TWFP) improvements in new POBC flowsheet with anchor
process parameter changes (%)
MATi,p
Hourly flowrate of feed resource i to process p (t/h)
TIS
v(z)OBJ
v(ℵ)OBJ
(GP, NE, GHGBAL and TWFP) for process stage f (%)
Total amount of resource i to be processed in process p (t/
h)
Generated material i sold directly as product (t/h)
Hourly flowrate of system-generated resource i from
process p (t/h)
Percentage total improvement score for the modified POBC
based on anchor process parameter changes (%)
Characteristic function for EFEW objectives (GP, NE,
GHGBAL and TWFP)
Characteristic function value of the full operation scenario
with no failure of process stages included for EFEW
objectives (GP, NE, GHGBAL and TWFP)
food-energy-water nexus are investigated. The work of Jaroenkietkajorn
and Gheewala (2020) has compared two food-energy-water nexus as­
sessments in studying different regions of oil palm plantation in
Thailand. Multi-objective optimisation studies with nexus consider­
ations are still limited to food-energy-water objectives. For hypothetical
food-energy-water system evaluations, Zhang and Vesselinov (2017)
have presented a multi-period modelling approach to perform trade-off
analysis for the economic advantage, food supply, energy supply and
water consumption. In the optimisation of biofuel production system,
López-Díaz et al. (2018) developed a mixed-integer linear programming
optimisation model to integrate with food-energy-water nexus. Tan et al.
(2020b) have attempted food-energy-water nexus evaluations to address
trade-offs between biogas recovery and POME elimination pathways by
maximising food revenue and energy balance while minimising the
water footprint (WFP) of optimal POBC design. To consider GHG impact
and land use, Tan et al. (2020c) further the study by including GHG
emissions and land footprint minimisation in the multi-objective opti­
misation of POBC. It is desired to further evaluate the flexibility of fuzzy
optimal POBC design for practical application and EFEW nexus devel­
opment. The flexibility of optimal palm oil mill design with maximum
economic performance was studied in the optimisation work of Foong
et al. (2019a). However, their work only focused on the mill side without
considering POME management and EFEW performance. There is still a
lack of EFEW nexus elaboration within the integrated palm oil produc­
tion system, especially on the process level impact, which is crucial to
provide insights for palm oil mill flexibility.
To integrate EFEW elements in the optimal planning of a new system
such as POBC, multi-objective optimisation alone could not exhibit the
flexibility of designed POBC flowsheet. Flowsheet modification should
be targeted to achieve desired performance improvements in consider­
ation of relative impacts to EFEW nexus. The challenge is to identify the
anchor process, i.e., the process which provides the greatest contribu­
tion to the plant’s performance concerning all aspects in the EFEW
nexus, to discover opportunities for process advancement in the
designed system. Failing to target the anchor process before flowsheet
modification could result in wasting of financial, material and human
resources as well as unbalanced EFEW performance trade-offs due to
investment in sub-critical processes which provides limited or negative
contribution to the EFEW nexus-integrated POBC. The key strategy in
anchor process determination is to demonstrate individual contribution
of each process towards multiple EFEW objectives achieved by the
optimal POBC flowsheet. The concept of anchor process was proposed
by Tan et al. (2020d) in their work to determine internal process in­
fluence on POBC’s economic performance. A cooperative game-based
2019), palm oil holders need a guideline to link the planning of palm oil
mill with the environment-food-energy-water (EFEW) nexus to consider
trade-offs between sustainability drivers. Efforts such as POME-based
biogas recovery, bio-fuel commercialisation and palm oil value addi­
tion has been initiated under the Palm Oil National Key Economic Area
for positive impacts on the EFEW nexus (Wan Ab Karim Ghani et al.,
2019). However, unfavourable economics have impeded the adoption
rate of these projects especially biogas facility among Malaysian palm oil
mills (Loh et al., 2017). Tan et al. (2020c) have suggested an alternative
palm oil production structure based on POME elimination technologies
and food, energy, water, effluent integrations between palm oil mill and
refinery. For cleaner palm oil production, the proposed structure known
as integrated palm oil-based complex (POBC) considers alternative
POME elimination pathway which applies undiluted clarification to
reduce POME load for further evaporation to recover trapped oil, avoid
methane emissions and convert effluent to marketable solid (Kandiah
and Batumalai, 2013). Nevertheless, potential linkages exist in the
energy-intensive evaporation process and elimination of renewable en­
ergy feedstock (i.e., biogas) which contribute distinctively to the EFEW
nexus. EFEW nexus integration is desired to evaluate the synergies be­
tween GHG impact, food, energy, and water resources in local produc­
tion systems such as POBC for sustainability enhancement (Leung Pah
Hang et al., 2016).
The concept of EFEW nexus was broadened from the food-energywater nexus applied to address the United Nation Sustainable Devel­
opment Goals by considering the environmental element (Zhang et al.,
2018). According to Hamidov and Helming (2020), the concept of
food-energy-water nexus has been proven critical to natural resource
management studies especially on irrigated agriculture systems to
investigate the coupled relationships between food production and
competing water use for agriculture and energy generation. The
assessment of food-energy-water nexus could highlight the impacts of
water governance principles on transboundary water use such as the
Indus Water Treaty (Kalair et al., 2019). Sun et al. (2020) have per­
formed quantification of potential synergies within the water, energy
and environmental pollutant nexus of the petrochemical production
system via an integrated approach. EFEW nexus evaluation has been
gaining attention in palm oil case studies. Recently, the elaboration in
EFEW nexus has been done on empty fruit bunches (EFB) value chain
optimisation in Peninsular Malaysia (James Rubinsin et al., 2020). Wan
Ab Karim Ghani et al. (2019) have discussed the impacts of EFEW nexus
on the biomass value chain planning in Malaysia using palm-based
biomass as case study. In the experimental-based study of Loh et al.
(2019), the impacts of POME-based organic fertiliser on the
3
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
optimisation approach has been suggested to assist the aforementioned
task (Tan et al., 2020d).
The cooperative game theory model developed by Maali (2009)
based on linear programming has been applied by previous researchers
in formulating profit allocation among collaborating plants. The coop­
erative game approach is desired when the players in a “game” are
willing to compromise and collaborate, thus is suitable for describing
interdepending processes within a system. In this context, the internal
processes within the POBC serve as multiple “players” responsible for
the overall performance of the “game”, allowing pooled benefits or
impacts with respect to each targeted objective to be rationally
distributed among the processes using adapted cooperative game model.
Researchers have been utilising cooperative game-based framework to
perform rational savings allocation between participating parties within
eco-industrial parks (EIP) (Tan et al., 2016). Andiappan et al. (2015) has
adapted Maali’s model to perform cost savings distribution among
collaborating facilities within a palm-based EIP. Tan et al. (2016)
demonstrated interplant profit allocation in a palm oil EIP using a linear
programming model according to game theory. Andiappan et al. (2016)
extended their previous model to evaluate the economic viability of a
palm-based EIP based on cooperative game theory to include stability as
one of the criteria in achieving industrial integration between palm oil
mill, biomass trigeneration system and palm biomass biorefinery of
different ownerships. Andiappan et al. (2018) adapted his published
results to perform cooperative game-based allocation of incremental
benefits among stakeholders in the palm-based EIP, concerning their
respective contributions.
Besides the cooperative game optimisation model, another potential
approach to provide rational basis for benefit distribution using coop­
erative game theory is the application of Shapley-Shubik Power Index
(SSI). SSI was originally proposed for evaluating the power of each voter
in affecting the result of a voting system (Wilms, 2020). The quantifi­
cation of SSI involves the generation of sequential coalitions by
considering each vote to be added one-by-one in different sequences to
the coalition until the number of positive votes meets the winning quota.
In a sequential coalition, the vital voter that secures the winning status
of the coalition when his/her vote enters the coalition in the defined
sequence is known as the pivotal voter. According to Shapley and Shubik
(1954), the frequency of each participated voter being determined as the
pivotal voter among all possible sequential coalitions could be used to
define the power of each voter in influencing the voting outcome. By
applying this concept, SSI has been considered to validate the
cost-benefit distribution for an energy supply network by Wu et al.
(2017). Mizuno et al. (2020) have explored the use of SSI in quantifying
corporate control among stakeholders. Recently, the utility of SSI has
been extended for process impact evaluation within a palm oil-based
complex to target the potential system bottleneck (Tan et al., 2021).
Previous literature has shown limited work in process level benefit
allocation within an EFEW nexus-integrated plant such as POBC. As
mentioned previously, Tan et al. (2020d) attempted economic perfor­
mance distribution among internal processes in the POBC using coop­
erative game model. Their recent work proposed a debottlenecking
framework to identify the profit and energy driving system bottleneck in
the multi-objective optimal POBC based on SSI allocation for flowsheet
debottlenecking (Tan et al., 2021). However, both works lack simulta­
neous consideration of economic, energy and environmental perfor­
mance allocation for designed POBC to investigate all aspects from the
EFEW nexus. In this regard, this study aims to address the gaps in pre­
vious studies to propose a systematic cooperative game-based optimi­
sation framework for targeting the anchor process based on
multi-objective process impact allocation in an optimal EFEW
nexus-integrated POBC. The proposed approach and mathematical
models could be applied consecutively to aid palm oil holders in sus­
tainable planning of palm oil mill retrofit to comply with sustainability
standards and provide insights on optimal budget allocation for POBC
investment.
In this study, the problem statement could be addressed as below:
• Given a set of technologies p (POME elimination, palm oil milling,
biogas recovery, physical refining) and resources i, an optimal POBC
flowsheet is aimed to be designed with simultaneous consideration of
multiple objectives (GHG, land and water footprints, economic po­
tential, net energy) via fuzzy optimisation method based on essential
process, economic, and environmental data.
• Groups of processes p carrying specific function in the given fuzzy
optimal POBC flowsheet are defined as the set of process stages f. By
considering different possibilities of process stage failures, all
working combinations of process stage f are defined under the set of
process failure scenario z.
• The characteristic function v(z) is defined as the pooled economic
and energy benefits or environmental impacts contributed by all
process stages f working together in scenario z. The values of v(z) are
obtained according to the description in Section 2.2.1.2.
• Based on the optimal values of characteristic function, allocation of
EFEW contributions among process stages f is performed via the
adapted cooperative game model proposed by Maali (2009) and
subsequently determine the specific anchor process for each EFEW
objective.
• Given the heuristic weights for EFEW objectives (gross profit, net
energy, GHG impacts and WFP) from decision-maker, the final aim is
to identify the overall anchor process in the fuzzy optimal POBC,
which is the process stage f of highest weighted-sum performance
allocation, (i.e., EFEW nexus score), in defined objectives. Benefitdrawback ratio analysis is conducted to verify the feasibility of an­
chor process advancement in POBC flowsheet modification.
Assumptions and limitations:
1) Extra cost is incurred to purchase resources processed externally to
allow operation of stand-alone process stages within the palm oil
mill.
2) To perform palm oil mill retrofit with compliance to current policies
and standards, the evaluation scope of environmental POBC foot­
prints is assumed within the system boundaries of a palm oil mill.
3) Only electricity converted from biomass in excess on-site or exported
to grid is added to the net energy of POBC.
4) To eliminate waste, all by-products and waste are assumed to be
consumed completely at the POBC thus GHG emissions from trans­
porting resource and logistic constraints are beyond the scope of the
work.
The content of this paper is outlined below. The problem is first
stated to include EFEW-based anchor process targeting in the optimal
design of a POBC with EFEW nexus integrations. The development of the
suggested integrated approach and mathematical models will be elab­
orated in Section 2 before applying them consecutively in Section 3 to
solve the given case study. Cooperative game-based performance allo­
cation will be conducted to target the POBC anchor process for concerns
in the EFEW nexus followed by the comparative analysis with SSI
method and anchor process validation via benefit-drawback analysis in
Section 4.
2. Methodology
The proposed systematic framework to identify the anchor process
for an optimal POBC design concerning multiple EFEW objectives is
illustrated in Fig. 1 consisting of fuzzy optimisation and cooperative
game-based process impact distribution approaches. The fuzzy multiobjective optimisation approach as shown in Fig. 1 is adopted from
the published work by Tan et al. (2020c) to introduce the beginning step
for the new cooperative game-based anchor process targeting frame­
work. Using the fuzzy optimal POBC flowsheet produced from Stage 1,
4
Journal of Cleaner Production 314 (2021) 127927
Y.D. Tan et al.
Fig. 1. Fuzzy optimisation and cooperative game-based anchor process targeting framework proposed for optimal design of EFEW nexus-integrated POBC.
different optimal scenarios are generated for all possibilities of process
stage failure via the scenario optimisation model in the initial phase of
Stage 2. Based on the optimal results, the developed cooperative
game-based performance allocation method is applied to quantify the
impacts of POBC process stages on multiple EFEW objectives. The
EFEW-based anchor process is then targeted based on the heuristic
EFEW objective priority weights obtained from the decision-maker. The
benefit-drawback (B/D) ratio analysis is included in the developed
framework to validate the determination of anchor process by evalu­
ating the impacts of selected process parameter change on the
EFEW-based performances of the POBC with maximum profit. The detail
description of each methodological approach in Fig. 1 is given in the
subsections.
5
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
2.1. Formulation of fuzzy multi-objective optimisation model
2.2.1.1. Formulations of EFEW objective functions. The target objective
functions for EFEW nexus evaluations are gross profit (GP), net energy,
GHG impacts and total WFP of the POBC. GP of a food production system
(i.e., POBC which gains main revenue from selling refined palm oil
products), could reflect its degree of contribution to the food market
thus should be evaluated in the EFEW nexus for global food security and
economic feasibility. GP is evaluated as the variable economic perfor­
mance of the POBC to exclude the capital expenses of selected and
installed processes in the fuzzy optimal POBC while distributing the
process impacts on long-term POBC profitability. The GP of POBC is
calculated via Eq. (8) by deducting the annual costs for operating
working units of technology p (capp ), purchasing material i externally at
required amount (EXRESi ) and importing electricity from grid
(ELECCOST) given the unit processing cost (UOPEXp ) and material
purchase cost (URCOSTi ) from the overall product revenue of selfgenerated product i at optimal output flowrates (PROi ) and excess
electricity (ELECREV) sold at unit selling prices (PRICEi ). To evaluate
the energy synergies of POBC in the EFEW nexus using Eq. (9), the POBC
net energy contribution (NE) accounts the amount of on-grid electricity
supply from biogas-based power plant (PROi=32 ) and excess on-site
electricity generated from biogas or biomass (ELECEXCESS ) after deduct­
ing the external electricity requirements (ELECEX ). In this study, the
environmental aspects in the EFEW nexus to be evaluated is the GHG
impacts of the POBC defined as the net GHG balance (GHGBAL) and the
total WFP. Based on Eq. (10), GHGBAL sums up the GHG emissions from
external resources and process materials. The process GHG emissions are
contributed by resources defined via the GHG process material reference
parameter (Ref GHG
i,p ) at optimal amounts of system-generated resource i
(SGRESi,p ) and process feed material i (MATi,p ). Some unconsumed
products and imported resources with positive GHG indicators for
external resource (RindGHGRES
) and product (RindGHGPRO
) will increase
i
i
GHG balance. This applies when raw POME emits methane and grid
electricity import increases national demand of fossil-based energy. For
water scarcity and security concerns, both blue WFP and grey WFP for
freshwater consumption and effluent generation are considered for the
overall WFP of POBC (TWFP) for EFEW nexus study. TWFP in Eq. (11) is
calculated as the sum of blue WFP, i.e., freshwater requirements per
hour (EXRESi=24 ), and grey WFP defined as the water demand estimated
to assimilate the effluent discharged at certain amount (EFF) given the
pollutant concentrations in the actual water supply (Cact ) and discharged
effluent (Ceff ), natural water quality (Cnat ) and maximum pollutant
concentration (Cmax ) (Subramaniam et al., 2014). The formulations for
the four objective functions, Eqs. (8)-(11), are based on the paper of Tan
et al. (2020d).
[
(
∑
∑
GP = AOT ×
PROi × PRICEi −
EXRESi × URCOSTi
To obtain optimal process route selection and flowsheet design
considering multiple objectives for sustainable POBC planning, fuzzy
optimisation approach is applied to study the trade-offs between five
objective functions, namely economic potential (EP), net energy (NE),
GHG emissions in balance (GHGBAL), overall WFP (TWFP) and opera­
tional land footprint (LFP). The fuzzy optimisation of POBC serves as the
beginning stage in the new anchor process targeting framework via the
fuzzy approach and optimisation models developed by Tan et al. (2020c)
to generate the multi-objective optimal POBC flowsheet. To determine
the fuzzy lower limits (e.g. EPL ) and upper limits (e.g. EPU ) for all ob­
jectives, the generic POBC optimisation model is solved individually
subjected to each objective function. The fuzzy limits are incorporated
into the fuzzy constraints, Eqs. (2)–(6), in the multi-objective optimi­
sation model. The fuzzy model is then solved by an integrated objective
function defined as λ, which indicates the aggregate degree of mem­
bership in the optimal fuzzy set (i.e., overall fuzzy level of satisfaction of
the fuzzy goals). Solving the model with the objective function in Eq. (1)
generates the fuzzy optimal POBC flowsheet.
Maximise λ
(1)
EP − EPL
≥λ
EPU − EPL
(2)
NE − NEL
≥λ
NEU − NEL
(3)
GHGBALU − GHGBAL
≥λ
GHGBALU − GHGBALL
(4)
TWFPU − TWFP
≥λ
TWFPU − TWFPL
(5)
LFPU − LFP
≥λ
LFPU − LFPL
(6)
0≤λ ≤ 1
(7)
2.2. Cooperative game-based anchor process targeting approach for
environment-food-energy-water (EFEW) nexus
As shown in Fig. 1, the following stage aims to determine the anchor
process for critical enhancement of the fuzzy optimal POBC flowsheet
concerning EFEW nexus contributions. In this stage, anchor process is
defined as the process stage with the greatest contribution to POBC
performance considering all aspects of the EFEW nexus. The proposed
anchor process determination approach adopts Maali’s cooperative
game linear programming model to rationally distribute the overall
plant performance among internal process stages f in the fuzzy optimal
POBC flowsheet with respect to each EFEW objective. The procedure for
EFEW-based anchor process targeting will be elaborated in the
subsections.
i
)]
+ ELECREV − ELECCOST
i
∑
−
capp × UOPEXp
(8)
p
(9)
NE = PROi=32 + ELECEXCESS − ELECEX
2.2.1. Multi-objective process impact distribution
The objective of targeting anchor process is to identify which process
stage provides the best trade-offs on the EFEW nexus improvements if
being invested for POBC flowsheet enhancement. This step is essential to
avoid incurring unnecessary capital for sub-optimal process investments
and time wastage for simulating all potential process variations in a
complex production system. To determine the EFEW-based anchor
process, allocation of process stage impacts for multiple POBC perfor­
mances is proposed to be done via a systematic cooperative game
optimisation approach. The detailed methodology will be elaborated as
follows.
GHGBAL =
(
)
∑ ∑
∑
GHG
Ref GHG
×
MAT
+
Ref
×
SGRES
i,p
i,p
i,p
i,p
p
i
i
(
)
+ RindGHGRES
× ELECEX
i=31
+
+
i
∑
RindGHGPRO
× PROi
i
i
(10)
(
TWFP = EXRESi=24 + EFF
∑
RindGHGRES
× EXRESi
i
Ceff − Cact
Cmax − Cnat
)
(11)
2.2.1.2. Scenario generation and characteristic function formulation. The
6
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
cooperative game model developed by Maali (2009) is adapted to pro­
vide optimal benefit or impact distribution among POBC processes.
According to Maali (2009), characteristic function addresses the defined
benefits for distribution and should be determined for all potential co­
alitions with different combinations of cooperative players, in this case,
interconnecting processes, to perform rational benefit allocation via the
linear programming model. The challenge is to obtain the optimal
characteristic function values for different process coalitions. Using the
fuzzy optimal POBC flowsheet obtained via the optimisation approach in
Section 2.1, the members of set f are defined as the vital process stages
by grouping selected processes p in the flowsheet according to the spe­
cific function of process stages and process interdependencies. To
evaluate different coalitions, it is assumed that one or more process
stages f exit from the coalition when they fail to operate, a set of process
stage failure scenarios z is thus determined to consider all working
combinations of process stage f based on failures of process stage using
an Excel Spreadsheet. For instance, process stages of {1,2,3,4,5} are
defined under set f. When process stage f = 1 fails in the process failure
scenario z = 1, f = 1 will not be included as a member in this scenario,
resulting in z1 = {2,3,4,5} only. Generally, every subset of f is the
element of set z except the empty set in mathematical means. Given n
number of process stages f defined, the combination formula, Eq. (12),
can be used to calculate the total number of potential scenario z (Vel­
leman and Call, 1995).
∑
Number of z = |z| =
r
n!
, r ∈ f , n = |f |
r!(n − r)!
emissions-minimised and WFP-minimised scenario z, GHGBALz and
TWFPz according to Eqs. (15)-(16).
v(z)GP = GPimp
= GPz − GPu
z
∀z u∈z
(13)
v(z)NE = NEzimp = NEz − NEu
∀z u∈z
(14)
v(z)GHGBAL = GHGBALz
v(z)TWFP = TWFPz
∀z
∀z
(15)
(16)
2.2.1.3. Integrated formulations for scenario optimisation model. To
obtain the optimal values of GPz , NEz , GPu , NEu , GHGBALz and TWFPz
for characteristic function calculation, each scenario z should be opti­
mised individually for every objective function. Conventionally,
different superstructures should be developed for every process failure
scenario z to formulate the respective mathematical models, which is
undesirable in terms of efficiency and conveniency. By including
scenario-specific parameters and formulations in Eqs. (17)-(20), a
mixed-integer linear programming optimisation model is formulated
and solved with subject to each objective function to produce optimal
objective values for calculating the characteristic functions of all sce­
narios z. Additional formulations of Eqs. (17)-(20) based on the work of
Tan et al. (2020d) need to be integrated with the POBC optimisation
model to simultaneously generate specific optimal results for each
process failure scenario z.
To identify the correlated, integrated, by-passing, existing processes
p based on working process stages f in each scenario z, binary parameters
INT
BYPASS
PindREL
and PindEXIST
are assigned with binary
p , Pindp , Pindp
p
values. When mill and refinery process integration is considered
(PindINT
= 1) in scenario z, intermediate resource i that requires
p
external processing due to failure of core process p, is given value 1 for
binary external intermediate material indicator (MindEXT
i,p ), whereas
externally generated resources such as crude palm oil (CPO) to be pur­
chased at market price to substitute lost self-generated resources, is
given value 1 for binary external generated resource indicator
(RindEXGEN
). Absent core process p between two working units is defined
i
as correlated process p (PindREL
= 1) to estimate the required amount of
p
intermediate resource. Polluting waste or unprocessed product
(RindEXPRO
) retained due to process p failure, i.e., POME, requires
i
external treatment or processing. Considering the defined binary pa­
rameters, Eq. (17) summed up the external processing costs for unpro­
cessed and intermediate resources during specific process failures with
fixed unit external resource processing costs (URCOSTEXT
). The overall
i
availability of external resource (AVRESi ) includes the basic available
quantity of resource without process failures (AVRESLOCAL
) and the
i
extra available quantity of intermediate resource during specific process
) as in Eq. (18). To eliminate by-product generation
failures (AVRESPEXT
i,p
and utility consumption during process p failures, the original process
resource conversion matrices, MCMi,p and PRCMi,p , require corrections
BY
by incorporating MCMBY
i,p and PRCMi,p into Eqs. (19)-(20) to estimate an
accurate amount of total processing resource in technology p (PRESp ).
Scenario-specific PRCMBY
i,p is used to correct the general PRCMi,p in Eq.
(20) as well. In Eq. (20), the quantity of intermediate process material
that requires external processing is predicted by multiplying PRCMi,p
with the binary process resource material indicator PRindREL
i,p to allow
(12)
For multi-objective performance allocation, distinctive characteristic
functions, v(z), need to be defined for each EFEW objective to evaluate
the pooled benefit or impact in each process stage failure scenario z
(Maali, 2009). The four objective functions formulated in Section
2.2.1.1 are used to calculate the characteristic functions for developed
cooperative game models. To allocate process impacts to each EFEW
objective using the cooperative game model, different objective-based
characteristic functions, v(z)OBJ as in Eqs. (13)-(16), are calculated
using an Excel Spreadsheet based on single-objective optimisation re­
sults of scenario z. For process impact distribution towards GP and net
energy of POBC, the characteristic functions are addressed as overall
benefits received from the process stage coalition, given as GP im­
imp
provements (GPimp
z ) and net energy improvements (NEz ). GPz is ob­
tained as the optimal GP when performing scenario z optimisation with
the objective function of maximising GP. To calculate the objective
improvements for each scenario z, the optimal objective function values
of each scenario z need to be compared to the performance of
stand-alone process stage operation. Therefore, a set of basis process
stage u is selected from the operating process stage f in each scenario z as
the basis stand-alone scenario used to allocate overall performance
improvements during process stage coalition. In this context, GPu and
NEu represent the optimal GP and net energy during sole operation of
basis process stage u according to the case study. In Eq. (13), GPimp
is
z
calculated by deducting GPu obtained in stand-alone process stage u
operation scenario from GPz which is the optimal GP generated for
scenario z including operation of basis process stage u (Tan et al., 2016).
Similarly, the relative net energy increment in the process stage failure
scenarios, NEimp
is calculated via Eq. (14). For the environmental foot­
z
print distribution in terms of GHG emissions and total WFP, the char­
acteristic functions are defined as the overall footprint accounted in each
scenario z to demonstrate the environmental impacts contributed by
resource conversion in the correlated processes (PindREL
= 1) for in­
p
termediate material flowrate prediction as part of the system-generated
each process stage f in all scenarios z. Hence, v(z)GHGBAL and v(z)TWFP are
defined as the optimal objective function values for GHG
7
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
process output resource (SGRESi,p ).
∑
×
SGRESi,p × RindEXPRO
+ URCOSTEXT
COSTiEXT = URCOSTEXT
i
i
i
p
∑
BYPASS
×
SGRESi,p × MindEXT
× PindINT
+
i,p × Pindp
p
p
× RindEXGEN
×
i
∑
SGRESi,p
ALLfOBJ ≥ v(z = {f } )OBJ ∀f
OBJ = GP, NE, GHGBAL, TWFP
∑
(
f
PRICEi
OBJ = GP, NE, GHGBAL, TWFP
)
PALLfOBJ = v(ℵ)fOBJ ∀f
OBJ = GP, NE, GHGBAL, TWFP
(17)
)
(
∑
∀i
AVRESPEXT
× PindBYPASS
p
i,p
)]
[
(
BYPASS
× PRESp
= MCMi,p − MCMBY
i,p × Pindp
∀i ∀p
[
(
REL
SGRESi,p = PRESp × PRindREL
× PRCMi,p − PRCMBY
i,p × Pindp
i,p
)]
BYPASS
∀i ∀p
× Pindp
(19)
(20)
2.2.1.4. Cooperative game process performance allocation model. The
optimal values of characteristic functions for EFEW objectives in Section
2.2.1.2 are essential inputs to the adapted Maali’s (2009) cooperative
game model. The linear programming model is solved by maximising
the aggregate degree of λ as described in Eq. (21) based on max-min
aggregation method (Maali, 2009). To ensure the Pareto optimality of
solution from the optimisation model, scenario-specific values of
objective-based characteristic functions are used to calculate the mar­
ginal contributions or performance weightage denoted as PWOBJ
in Eq.
f
formula of weighted sum score reviewed by Kolios et al. (2016). The
respective weights for each EFEW objective, named as the EFEW
weightage (EFEWwOBJ ), represent the subjective priorities of POBC
objectives in EFEW nexus contributions based on the decision-maker’s
interest. Heuristic values of EFEWwOBJ are considered sufficient in this
work as the proposed anchor process determination approach aims to
suggest possible process advancements for the unbiased fuzzy optimal
POBC flowsheet according to the decision-maker’s specific focus in
EFEW nexus. In the future direction, priority quantification methods, i.
e., analytic hierarchy process (AHP) method, could be considered for
targeting anchor process in non-subjective means, by performing
pair-wise comparison on collected priorities of respondents between
defined objectives to calculate the rational weightage (Ren et al., 2019).
The final anchor process for POBC is determined as the process stage
f with the highest score of NSEFEW
, indicating that this process stage
f
(22) for each process stage f using Excel Spreadsheet, to formulate the
constraint in Eq. (23) concerning the EFEW objectives (Tan et al., 2016).
Based on Eq. (22), PWOBJ
for process stage f is calculated as the sum of
f
average deviation values between the characteristic function of every
scenario z including operating process stage f, v(z)OBJ , and the charac­
teristic function of the scenario z − {f}, which includes all existing
process stages in scenario z except process stage f, v(z − {f})OBJ , divided
by the characteristic function of the zero failure scenario where all POBC
OBJ
process stages operate, v(ℵ)OBJ . As an example, to determine PWf=1
when
set f =
{1, 2, 3}, the
calculation
[(v(z = {1, 2, 3})OBJ − v(z = {2, 3})OBJ )
+(v(z = {1, 2})OBJ − v(z = {2})OBJ )
OBJ
OBJ
will
be
as follows:
+(v(z = {1, 3})OBJ − v(z = {3})OBJ )
+v(z = {1}) ] /v(z = {1, 2, 3}) . Constraints in Eqs. (23)-(26) are
formulated based on the cooperative game optimisation model (Maali,
2009) to provide optimal values of distributed EFEW objective-based
performance (ALLOBJ
) among process stages f within the fuzzy optimal
f
provides the greatest contribution to the weighted EFEW performance of
POBC thus attains the highest priority in process advancement and
maintenance for long-term POBC flexibility.
)
∑(
NSfEFEW =
PALLOBJ
× EFEWwOBJ ∀f
f
OBJ
(27)
OBJ = GP, NE, GHGBAL, TWFP
POBC. The respective percentage allocation score is denoted as PALLOBJ
f
as in Eq. (26) which reflects the process stage’s degree of contribution
towards the POBC performance in the EFEW nexus. The calculated
values of PWOBJ
obtained from the Excel Spreadsheet based on Eqs.
f
(22)-(23) are inputted to the formulated cooperative game models and
solved using the objective function λ in Eq. (21) to obtain optimum re­
sults of PALLOBJ
concerning EFEW objectives for the following anchor
f
2.2.3. EFEW-based benefit-drawback (B/D) ratio analysis
Results validation is an essential step to demonstrate the feasibility of
a new innovative concept (Kuznetsova et al., 2016). In this study, the
concept of anchor process is introduced to target the process stage
within a food production system such as POBC which requires major
focus in process maintenance and advancement for sustainable EFEW
nexus development. To validate the anchor process obtained from the
proposed cooperative game optimisation framework, B/D ratio assess­
ment is suggested to perform advantage versus disadvantage analysis in
terms of EFEW nexus contributions from the anchor process advance­
ment. Firstly, a suitable process parameter is selected within the tar­
geted anchor process concerning available technology advancements.
The impacts of anchor process parameter variation on the multiple
process determination (Andiappan et al., 2015).
(21)
Maximise λ
PWOBJ
=
f
∑v(z)OBJ −
z∋f
v(z− {f } )OBJ
v(ℵ)OBJ
∀f
OBJ = GP, NE, GHGBAL, TWFP
1
PWOBJ
f
ALLOBJ
≥ λ ∀f
f
OBJ = GP, NE, GHGBAL, TWFP
(26)
2.2.2. EFEW-based anchor process determination
For this work, anchor process is defined as the process stage which
allocates the highest weighted-sum of POBC performance contribution
to all EFEW objectives based on cooperative game distribution. Due to
the significant contributions by the anchor process, the highest budget
allocation should be considered for its maintenance and advancement to
ensure the flexibility of POBC in the EFEW nexus. In terms of definition,
anchor process is different from bottleneck which is the potential root
cause threatening future system performance (How and Lam, 2019).
Nevertheless, targeting anchor process for advancement could achieve
similar aim for system enhancement and discover possible bottleneck
within the process. To consider multiple EFEW objectives simulta­
neously in targeting the anchor process for optimal POBC flowsheet
improvement, the weighted-sum method could be applied to solve such
multi-objective decision-making problem via obtaining a summation
score for all weighted objectives (Kolios et al., 2016).
For this work, the weighted-sum score for EFEW objective-based
impact allocation in each process stage f is defined as the percentage
EFEW nexus score (NSEFEW
) obtained via Eq. (27) adapted from the
f
(18)
p
MATi,p
(25)
ALLOBJ
∀i
p
AVRESi = AVRESLOCAL
+
i
ALLOBJ
= v(ℵ)OBJ ∀f
f
(24)
(22)
(23)
8
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
EFEW performances of POBC will then be evaluated in terms of B/D
ratio to reflect the feasibility of anchor process investment in enhancing
the EFEW aspects for the designed POBC. To investigate the effects of
anchor process parameter changes on the POBC objectives, the fuzzy
optimal POBC case study obtained in Section 2.1 is revised according to
percentage improvements on the selected process parameter to be
solved by the mixed-integer linear programming optimisation model
adapted from Section 2.2.1.3 to generate GP-maximised results with
subject to the environmental constraints formulated from fuzzy optimal
values of GHG emissions and total WFP in Eqs. (28)-(30). The
GP-optimal results for the baseline fuzzy POBC case study and the new
results generated from process parameter variation are compared to
obtain the respective percentage improvements in terms of EFEW ob­
jectives. The percentage of EFEW objective improvements (IMPOBJ ) in
terms of net energy, GP, GHG emissions and total WFP in the new POBC
results are essential to calculate the total weighted improvement score,
TIS (Kolios et al., 2016) and B/D ratio for each anchor process parameter
improvement using Eqs. (31)-(32). Using Eq. (32), the B/D ratio ob­
tained could represent the overall percentage improvements of EFEW
objectives with respective anchor process parameter changes over the
percentage deterioration in the performance of POBC (Wouters et al.,
2014). EFEW nexus weightage is used in the weighted B/D ratio and
total improvement score calculation to consider the heuristic priorities
for each EFEW-related objective. Positive value of TIS suggests favour­
able net improvements in weighted EFEW performances from varied
POBC process parameter whereas B/D ratio greater than 1 implies
feasible process parameter changes such that the POBC benefits gained
via anchor process parameter improvement outweigh the associated
drawbacks considering EFEW nexus development.
GHGBAL ≤ GHGBALFuzzy
(28)
TWFP ≤ TWFPFuzzy
(29)
Maximise GP
(30)
TIS =
∑
IMPOBJ × EFEWwOBJ ,
∑
/
Positive IMPOBJ × EFEWwOBJ
⃒
D ratio = ∑ OBJ⃒⃒
,
OBJ ⃒ × EFEWwOBJ
OBJ Negative IMP
= GP, NE, GHGBAL, TWFP
4. Results and discussion
The palm oil mill retrofit case study described in Section 3 is solved
by the mathematical models and Excel Spreadsheet developed in Section
2 consecutively according to the systematic framework proposed in
Fig. 1 to be discussed in the subsections.
(31)
OBJ
OBJ = GP, NE, GHGBAL, TWFP
B
portfolio and process pathway design of the retrofitted POBC should
consider economic potential and net energy maximisation along with
minimisation of GHG, water, and land footprints. Additionally, the
company owner aims to target an anchor process within the fuzzy
optimal POBC flowsheet for investment in process advancement to
further improve the POBC design towards desired EFEW nexus contri­
butions and evaluate the individual POBC process impacts on the syn­
ergies between EFEW resources.
Fig. 2 illustrates all alternative technologies and process routes
proposed for POBC retrofit adapted from the fuzzy POBC optimisation
problem in the work of Tan et al. (2020c) to demonstrate the subsequent
anchor process targeting approach. Their work has successfully
considered two POME management pathways: a) biogas recovery and b)
POME elimination, in the process route selection to generate a
methane-eliminated POBC. In the adaptation of the cited work, this
study considers updated process-utility flows and process units based on
the latest technology advancement from suppliers. Besides, the process
selections for biogas recovery are coloured in orange whereas the
distinctive process alternatives for POME elimination are coloured in
blue in the modified Fig. 2. The related process, economic, and envi­
ronmental data applied in the case study are retrieved from the work of
Tan et al. (2020c). Under Appendix, Table A.1 compiled all resources i
involved including their corresponding unit prices for external pro­
cessing, purchasing, and selling. The environmental factors for GHG
emission from unconsumed products and external resources are sum­
marised in Table A.2. The set of available technology p for constructing
the optimum process pathway of POBC is given with the process
resource input and out data in Table A.3 whereas their respective
operating and capital costs are summarised in Table A.4. It is worth
noting that no capital cost will be incurred for the pre-existing process
units in Table A.4 with the label “Existing”.
4.1. Fuzzy multi-objective POBC optimisation results
OBJ
Based on the fuzzy approach in Section 2.1 proposed by Tan et al.
(2020c), the developed mixed-integer linear programming optimisation
model is used to solve the case study in Section 3 via CPLEX solver
(12.6.3.0) in the General Algebraic Modelling System (GAMS) software
(version 24.7.4) to obtain the fuzzy optimal POBC flowsheet as illus­
trated in Fig. 3 considering trade-offs between economic potential, net
energy, water, land and GHG footprints. According to Fig. 3, POME
elimination pathway is chosen for methane avoidance where undiluted
clarification and multi-effect evaporation process units are installed to
convert POME and PORE into process condensate and concentrate for
minimal GHG impacts and WFP. The process condensate is recycled as
process water while solvent extraction is invested for additional oil and
decanter solid recovery from process concentrate. It is proven that
POME elimination offers better trade-offs between conflicting objectives
of profit, energy efficiency and environmental in addressing POBC sus­
tainability. The fuzzy multi-objective optimal results are summarised in
Table 1 which are essential inputs for the following anchor process
determination.
(32)
3. Case study application
The proposed multi-objective optimisation and anchor process tar­
geting framework is used to solve the case study adapted from the work
of Tan et al. (2020c) to demonstrate the applicability of the framework.
As an effort to receive the Malaysian Sustainable Palm Oil certification
scheme, a palm oil company owning a mill in Peninsular Malaysia plans
to retrofit the palm oil mill into a methane-mitigated POBC, via
investing biogas recovery technologies or POME elimination strategies
and form a mill-refinery production complex with a self-owned refinery
within 1 km distance to share the resources and directly convert the CPO
extracted from 60 t/h free fresh fruit bunches (FFB) into refined palm oil
products such as refined, bleached, deodorised palm stearin (RBDPS)
and palm olein (RBDPOL), fatty acid distillate (PFAD). The mill operates
4,350 h/y to extract CPO from FFB pre-treated with steam via conven­
tional diluted clarification processes. Palm-based biomass in the form of
shell and fibre are utilised as biomass boiler fuel to supply the steam and
electricity demand in the mill. Open anaerobic pond is the current POME
treatment method which does not comply with the methane avoidance
policy whereas palm oil refinery effluent (PORE) is treated at the
wastewater treatment plant.
With rising concerns in palm oil sustainability, the optimal product
4.2. Scenario specific optimisation results and objective-based
characteristic functions
The fuzzy optimal POBC flowsheet serves as the case study for tar­
geting the EFEW-based anchor process via the proposed cooperative
game-based framework presented in Section 2.2 with the consecutive
9
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Fig. 2. Potential technologies and process routes for POBC (modified from Tan et al. (2020c)).
application of developed optimisation models and Excel tools. To
determine optimal values in different process stage coalitions for char­
acteristic function calculation, the selected processes p in Fig. 3 are first
classified into five process stages under set f based on their specific
function in supporting refined palm oil production. Table 2 shows the
selected processes p grouped in every process stage f.
Total 31 process failure scenarios z are expected via Eq. (12) to
include all working combinations of five process stages f via Excel tool.
The fuzzy optimal POBC serves as the case study to generate the input
data for 31 process failure scenarios as tabulated in Tables A.5–A.10.
The input values of binary parameters defined in Section 2.2.1.3 for
differentiating the existing, correlated, integrated and by-passing pro­
cesses in every scenario z are compiled in Tables A.5–A.8. Binary and
cost parameters related to import and external processing of interme­
diate resources and unconsumed products due to respective process
failures in scenarios z are summarised in Tables A.9–A.10. The input
data is then solved with the mixed-integer linear programming optimi­
sation model developed in Section 2.2.1.3 to generate optimal results for
different individual optimisation scenarios (maximum net energy sce­
nario, maximum GP scenario, minimum GHG balance scenario and
minimum WFP scenario) by assigning different objective functions. The
optimal values of the objective function (net energy, GP, GHG balance
and WFP) for each individual optimisation scenario are simultaneously
generated for 31 process failure scenarios using the integrated scenario
optimisation model in Section 2.2.1.3. For instance, when solving the
scenario optimisation model with the objective function of maximising
GP, 31 optimal values of GP are generated for the 31 process failure
scenarios. Subsequently, these optimal values of objective function are
used to calculate the objective-based characteristic functions, v(z)OBJ ,
based on Eqs. (13)-(16) described in Section 2.2.1.2. The values of
v(z)OBJ with respect to four EFEW objectives defined as GP improve­
ment, net energy improvement, GHG emissions and overall WFP are
calculated in an Excel Spreadsheet based on the optimal and basis
objective values compiled in Table 3. The GAMS coding for the process
failure scenario optimisation modelling and solving could be found at
GitHub repository (Tan et al., 2020a).
4.3. Cooperative game-based allocation of process stage impacts to EFEW
objectives
The results in Table 3 are integrated into the cooperative game dis­
tribution models developed in Section 2.2.1.4 to perform optimal per­
formance allocation among the fuzzy optimal POBC process stages for
the economic performance, energy efficiency, GHG impacts and WFP of
the POBC. To generate the essential inputs for the cooperative game
allocation model, the objective-specific performance weightage, PWOBJ
f
for each process stage f as shown in Table 4 are calculated based on the
imp
values of defined v(z)OBJ in Table 3 (GPimp
z , NEz , GHGBALz , TWFPz )
with respect to each EFEW objective using Eq. (22). The breakdown of
v(z)OBJ and v(z − {f})OBJ values used for PWOBJ
calculation for five
f
process stages f via Eq. (22) and description in Section 2.2.1.4 is shown
in Tables A.11–A.15. Note that the value of v(z − {f})OBJ is obtained as
the value of v(z)OBJ with process stage f being removed in all process
10
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Fig. 3. Fuzzy optimal flowsheet for multi-objective POBC.
{2}). With reference to Fig. 1, the linear programming optimisation
models for EFEW objective distribution including Eqs. (23)-(26) are
solved with the objective function of maximising variable λ in GAMS
using CPLEX solver to generate Pareto optimal results for rational dis­
tribution of process impact towards defined EFEW objectives as sum­
marised in Table 4.
Based on Table 4, the nut/kernel separation and combined heat and
power (CHP) process stage attains the highest allocation of GP and net
energy improvements with percentage scores of 41% and 75% in the
fuzzy optimal POBC. This implies that this process stage provides major
contributions to the POBC’s profitability and energy efficiency. The CHP
system fuelled by recovered palm mesocarp fibres (PMF) and palm
kernel shell (PKS) is responsible for fulfilling the demand of energyextensive milling, evaporating and refining processes in the POBC to
reduce import requirements on steam and grid electricity associated
Table 1
Fuzzy optimal results from POBC multi-objective optimisation.
Objective function
Fuzzy optimal value
Max λ
0.457
Max EP (M USD/y)
41.140
Max NE (MWh)
0.919
Min GHGBAL (kgCO2eq/h)
21.420
Min TWFP (t/h)
22.800
Min LFP (hectare)
0.103
failure scenario z considering process stage f operating. For example, to
for f = 1 using Table A.11, the value of v(z − {1})OBJ for
calculate PWOBJ
f
z = 6 (z = {1,2}) is obtained as the value of v(z)OBJ at scenario z = 2 (z =
Table 2
Identification of technologies p considered in each defined process stage f
f
Process stage
1
2
3
4
5
FFB pre-treatment
Undiluted CPO extraction
POME elimination
Nut/kernel separation and CHP
Palm oil refinery
Process p
1
2
3
√
√
√
4
5
√
√
9
10
11
12
13
14
15
√
√
16
17
18
19
√
√
√
√
22
23
√
√
√
√
√
√
11
√
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table 3
Optimal characteristic function values for GP, NE, GHGBAL and TWFP objectives in all scenarios z.
Process failure scenario z
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
v(z)NE
v(z)GP
v(z)GHGBAL
v(z)TWFP
GPz (M USD/y)
GPu (M USD/y)
GPimp
z (M USD/y)
NEz (MWh)
NEu (MWh)
NEimp
(MWh)
z
GHGBALz (kgCO2eq/h)
TWFPz (t/h)
27.57
30.32
0.00
7.33
2.46
29.25
27.71
36.11
30.03
30.40
37.71
32.78
7.33
2.38
10.14
29.60
37.81
31.71
36.43
30.25
38.92
37.95
32.93
40.46
10.12
38.44
32.15
40.62
39.33
40.81
41.35
27.57
30.32
0.00
7.33
2.46
27.57
27.57
27.57
27.57
30.32
30.32
30.32
7.33
2.46
7.33
27.57
27.57
27.57
27.57
27.57
27.57
30.32
30.32
30.32
7.33
27.57
27.57
27.57
27.57
30.32
27.57
0.00
0.00
0.00
0.00
0.00
1.69
0.14
8.55
2.46
0.08
7.39
2.46
0.00
-0.08
2.81
2.03
10.25
4.14
8.86
2.68
11.35
7.63
2.62
10.15
2.79
10.87
4.58
13.05
11.76
10.49
13.78
-0.336
-0.245
0.000
3.203
-1.500
-0.581
-0.522
2.867
-1.836
-0.430
2.941
-1.745
3.203
-1.686
1.700
-0.767
2.605
-2.081
2.681
-2.022
1.367
2.755
-1.930
1.441
1.515
2.419
-2.267
1.105
1.181
1.255
0.919
-0.336
-0.245
0.000
3.203
-1.500
-0.336
-0.336
-0.336
-0.336
-0.245
-0.245
-0.245
-1.500
3.203
3.203
-0.336
-0.336
-0.336
-0.336
-0.336
-0.336
-0.245
-0.245
-0.245
3.203
-0.336
-0.336
-0.336
-0.336
-0.245
-0.336
0.000
0.000
0.000
0.000
0.000
-0.245
-0.186
3.203
-1.500
-0.185
3.186
-1.500
4.703
-4.889
-1.503
-0.431
2.941
-1.745
3.018
-1.686
1.703
3.000
-1.685
1.686
-1.688
2.755
-1.931
1.441
1.518
1.500
1.255
6538.78
284.39
0.00
0.00
975.94
6823.17
7184.10
214.62
7514.72
803.33
154.24
1260.33
0.00
1196.00
55.54
7886.71
365.98
7799.11
17.63
8279.92
259.12
2.37
1898.93
209.07
13.23
17.72
8983.20
410.49
18.87
13.70
18.97
1.109
0.852
0.000
0.000
2.724
1.961
0.000
24.124
3.834
0.000
0.852
3.576
0.000
1.041
2.724
0.000
24.976
4.685
19.357
0.000
27.307
0.000
0.000
3.576
1.081
16.549
0.000
28.158
21.501
0.000
18.698
Table 4
Percentage allocation score of EFEW-based performance for five process stages in the fuzzy optimal POBC.
f
1
2
3
4
5
Process stage
FFB pre-treatment
Undiluted CPO extraction
POME elimination
Nut/kernel separation and CHP
Palm oil refinery
GP impact allocation
NE impact allocation
GHGBAL impact allocation
TWFP impact allocation
PWGP
f
PALLGP
(%)
f
PWNE
f
PALLNE
(%)
f
PWGHGBAL
f
PALLGHGBAL
(%)
f
PWTWFP
f
PALLTWFP
(%)
f
4.34
3.62
0.29
7.76
2.72
23.18
19.32
1.53
41.42
14.54
5.96
5.85
-2.06
35.61
-24.52
12.58
12.35
0
75.08
0
2579.7
230.9
182.9
-3461.8
402.7
75.96
6.80
5.39
0
11.86
9.34
-1.41
-41.61
134.72
21.03
5.66
0
0
81.6
12.74
with expensive tariffs. The contribution of nut/kernel separation and
CHP process stage is vital in assisting the operation of all POBC process
stages in terms of thermal and electrical energy to achieve refined palm
oil production target. Excess PMF and PKS could either enhance the
renewable energy contribution or create additional income by direct
trading. Attractive revenue from selling separated palm kernel (PK) as
high quality fuel also supports the POBC profitability (Husain et al.,
2002). Maintenance focus in the nut/kernel separation and CHP process
stage is essential to secure the energy and economic performance of the
fuzzy optimal POBC. However, nut/kernel separation and CHP system is
also the dominant process stage which makes up 82% of WFP accounted
in the fuzzy optimal POBC. In other words, the operation of this process
stage provides the greatest negative impact on water scarcity issue
within the EFEW nexus-integrated POBC. This is due to the water con­
sumption for steam generation in the biomass-fuelled CHP system to
satisfy POBC energy demand. Thus, the CHP system could be targeted
for process advancement to achieve critical improvements in water use
of POBC.
For GHG impacts evaluation, the process stage which releases the
highest amount of GHG emissions is the FFB pre-treatment, accounting
for 76% of POBC’s GHG footprint. This is due to the steam pre-treatment
technology selected in the fuzzy optimal POBC which consumes large
amount of steam to sterilise and digest FFB. If insufficient biomass is
available for energy conversion, the dependency of the process stage on
fossil fuel-based energy will increase the GHG emission of POBC. Be­
sides, FFB pre-treatment contributes the most to POME generation in the
fuzzy optimal POBC which releases high global warming potential
methane if not evaporated. Nevertheless, to target the final anchor
process in the fuzzy optimal POBC for EFEW nexus evaluations, per­
formance allocation for all objectives needs to be considered simulta­
neously with assigned priorities.
4.4. Targeted EFEW-Based anchor process
The weighted-sum EFEW nexus scores for all process stages (NSEFEW
)
f
are tabulated in Table 5 with reference to Eq. (27) using heuristic values
of EFEW weightage describing the relative importance of GP, net energy,
GHG impacts and WFP objectives in the desired EFEW nexus. Based on
Table 5, the EFEW-based anchor process for the fuzzy optimal POBC is
targeted as the nut/kernel separation and CHP process stage (f = 4) with
the highest score of NSEFEW
at 41% followed by FFB pre-treatment and
f
undiluted CPO extraction. According to the weighted focus on economic
performance, energy contribution, GHG emissions and WFP, the nut/
12
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table 5
Percentage EFEW nexus score of the process stages and EFEW-based anchor process in the fuzzy optimal POBC.
f
1
2
3
4
5
Process stage
FFB pre-treatment
Undiluted CPO extraction
POME elimination
Nut/kernel separation and CHP
Palm oil refinery
EFEW weightage, EFEWwOBJ
PALLGP
(%)
f
(%)
PALLNE
f
PALLGHGBAL
(%)
f
(%)
PALLTWFP
f
EFEW nexus score, NSEFEW
(%)
f
23.18
19.32
1.53
41.42
14.54
0.6
12.58
12.35
0
75.08
0
0.1
75.96
6.80
5.39
0
11.86
0.2
5.66
0
0
81.60
12.74
0.1
30.93
14.19
2.00
40.52
12.37
kernel separation and CHP stage in the fuzzy optimal POBC is the pro­
cess stage that creates the greatest impact on the EFEW nexus.
Fig. 4a and b illustrate the non-weighted and weighted EFEW-based
performance allocation for the five POBC process stages based on given
EFEW weightage. In both diagrams, the identified anchor process (i.e.,
nut/kernel separation and CHP) achieves the largest coverage on the
four EFEW objectives due to its superior contributions in profit,
renewable energy balance and WFP. By considering defined priorities in
the EFEW nexus, the difference in the EFEW nexus coverage between the
anchor process and FFB pre-treatment which rates second in terms of
EFEW nexus score, is smaller in Fig. 4b due to the greater focus on GHG
impacts compared to energy contribution and WFP for sustainable
POBC. The importance of the identified anchor process to the EFEW
nexus could be exemplified by the water-energy synergies in steam and
electricity generation from water-consuming boilers within the CHP
system, trade-offs between sales revenue and renewable energy from
shell and fibre biomass, GHG-water linkages in considering biomassbased electricity for neutral GHG emissions by utilising more bolier
feedwater and profitability-WFP trade-offs for importing fossil fuelgenerated energy to reduce water consumption in the CHP system.
Therefore, the role of nut/kernel separation and CHP stage as the EFEWbased anchor process of the fuzzy optimal POBC could be justified. Based
on the results, the decision-maker should allocate more budget for
maintenance and technology advancement in the identified anchor
process, which is the nut/kernel separation and CHP process stage, to
target critical improvements in long-term sustainability of the EFEW
nexus-integrated POBC.
EFEW-based Anchor Process
✓
modifications to debottleneck the fuzzy optimal POBC. The percentage
weighted-sum critical score for process stage f was calculated by
considering the percentage allocation of process impact on the GP and
net energy performance of POBC in terms of SSI values (PALLGP
and
f
PALLNE
f ) and the respective priority weights for GP and net energy,
termed as the criticality weightage (CSwGP and CSwNE ) as shown in Eq.
(33). The criticality weightage was determined based on the decision-­
maker’s interest, which represents the subjective priorities of SSI eval­
uation objectives when considering process advancement within POBC.
) (
)
(
NE
Critical scoref = PALLfGP × CSwGP + PALLNE
∀f
(33)
f × CSw
In this study, anchor process with the highest EFEW nexus score
allocated via proposed cooperative game optimisation framework is
defined as the POBC process stage which deserves greater focus in
process maintenance and advancement for optimal EFEW nexus con­
tributions. The weighted-sum scores for both methods are differentiated
by the objectives considered and respective weights as shown in Table 6.
In this regard, the EFEW-based anchor process targeting approach could
be applied in multi-objective POBC debottlenecking to select the best
process advancement strategy prior to generating an enhanced POBC
flowsheet with desired multi-objective improvements. The result com­
parison between the cooperative game-based anchor process targeting
method in this study and the SSI-integrated system bottleneck deter­
mination approach is given in Table 6. For the cooperative game
method, both critical score and EFEW nexus score are tabulated to
compare the performance of SSI and current method in targeting sig­
nificant POBC process stage for GP and energy concerns besides inves­
tigating the impacts of integrating EFEW objectives in the weighted
process impact allocation. The critical scores for cooperative gamebased process performance allocation in this study are calculated
using Eq. (33) based on the given values of GP and net energy weightage
in Table 6 and the percentage EFEW-based performance allocation
NE
scores for GP and net energy (PALLGP
f and PALLf ) in Table 5 whereas the
4.5. Comparative analysis between cooperative game and Shapley-Shubik
index (SSI) methods for POBC debottlenecking
The recent work of Tan et al. (2021) suggested a multi-objective
debottlenecking approach for optimal POBC planning based on SSI
allocation. In the mentioned paper, the objective-based values of SSI for
GP and net energy objectives were allocated to calculate the
weighted-sum critical score for each POBC process stage to identify the
system bottleneck, which is defined as the process stage with the highest
influence in achieving the economic and energy performance goals in
the POBC. The system bottleneck was then targeted for optimum process
EFEW nexus scores are directly extracted from Table 5. The critical
scores for the SSI-based debottlenecking work were extracted from the
study of Tan et al. (2021).
Based on Table 6, it can be concluded that the anchor process and
Fig. 4. Allocation charts for (a) non-weighted and (b) weighted process stage EFEW performance.
13
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table 6
Results comparison between cooperative game and Shapley-Shubik Index approaches
Process stage
FFB pre-treatment
Undiluted CPO extraction
POME elimination
Nut/kernel separation and CHP
Palm oil refinery
GP weightage
NE weightage
GHGBAL weightage
TWFP weightage
a
Shapley-Shubik Index (SSI) method
Cooperative game method (This study)
Critical Score (%)
System bottleneck
Critical Score (%)
√
21.06
17.93
1.22
48.15
11.63
0.8a
0.2a
0a
26.67a
0a
46.67a
26.67a
0.8a
0.2a
Anchor Process
√
EFEW nexus score (%)
30.93
14.19
2.00
40.52
12.37
0.6
0.1
0.2
0.1
Anchor Process
√
Extracted from published work (Tan et al., 2021)
system bottleneck determined via both approaches are the same for
similar POBC case study in all three scenarios. Therefore, it is proven
that the nut/kernel separation and CHP stage requires the highest
attention from decision-maker within the fuzzy optimal POBC. The
critical score allocation via cooperative game approach is different from
the results of the SSI method especially for the process stages ranked
second, third and fourth. This suggests that SSI-based score tends to
identify the critical process stages in sustaining the POBC performance
targets instead of providing distinctive impact distribution between
process stages as in the cooperative game method. Unlike the optimal
cooperative game distribution, SSI-based allocation is performed
considering defined quota or benchmark of targeted POBC objectives
therefore will generate different results based on the decision-maker’s
performance goals. Nevertheless, both methods are successful in deter­
mining the most influential and contributing process stage based on
multiple weighted objectives while cooperative game approach is more
suitable to study individual contribution of POBC process stages for
detailed EFEW nexus evaluations. Although the process stage targeted in
both studies is the same, the weighted multi-performance score allo­
cated for the process stages (i.e., critical score and EFEW nexus score)
varies due to different objectives being considered. Only GP and energy
contribution of the POBC process stages are concerned in identifying the
SSI-based system bottleneck whereas four EFEW objectives are evalu­
ated in this study for EFEW-based anchor process determination. It is
suggested that the proposed cooperative game-based strategy for anchor
process targeting delivers a sophisticated trade-off analysis between
conflicting economic and environmental objectives to provide compre­
hensive insights for EFEW nexus evaluations.
Fig. 5. Graph of anchor process parameter improvement (boiler efficiency
increment) against total improvement score and B/D ratio of modified POBC.
nexus-integrated POBC.
4.7. Discussion
The nut/kernel separation and CHP process stage is determined as
the EFEW-based anchor process of the fuzzy optimal POBC via the
proposed cooperative game-based performance allocation approach.
With 41% of EFEW nexus score allocated, the nut/kernel separation and
CHP system provides the greatest overall impact on the development of
EFEW nexus via possible synergies in the POME-eliminated POBC,
considering the heuristic objective priorities from decision-maker.
The profit-energy linkage in the anchor process leads to its contri­
bution to the GP and net energy of POBC with high and positive impact
allocation scores. The biomass-based CHP system serves as the only
source of self-generated thermal and electrical energy in the POBC.
Inoperability of the CHP system will force the energy demand of POBC to
be fully supplied by imported utilities and eliminates renewable energy
generation. The incurred cost of electricity and steam reduces the profit
of POBC. Additionally, failure of kernel recovery process due to the
unseparated nut/fibre mixture without fibre extraction creates signifi­
cant revenue loss due to the absence of high market value PK.
In the perspective of environmental footprints, the GHG impact of
the anchor process is considered neutral due to the displacement of
fossil-fuel-generated electricity and steam with biomass-converted en­
ergy to satisfy the POBC requirements. However, the favourability of the
CHP system in carbon offsetting tends to move inversely with its po­
tential in WFP reduction. This is because freshwater is consumed to
generate steam for cogeneration purposes thus high blue WFP is asso­
ciated with biomass-to-energy conversion to reduce GHG emission.
Similar trend is observed for water-energy linkage within the EFEW
nexus in POBC as more water will be fed into the boiler to generate
higher amount of surplus energy for national energy mix. Synergies
4.6. B/D ratio analysis for anchor process parameter variation
The improvement in biomass boiler efficiency (Nasution et al., 2014)
is selected for POBC flowsheet enhancement based on the targeted
EFEW-based anchor process, i.e., nut/kernel separation and CHP process
stage. To demonstrate the applicability of the proposed framework and
validate the selection of anchor process advancement to improve the
EFEW nexus aspects in the fuzzy optimal POBC, B/D ratio analysis as
described in Section 2.2.3 is performed on the GP-optimal results to
evaluate the impacts of boiler efficiency variation to the total
improvement score and B/D ratio as illustrated in Fig. 5. The graph
shows the total improvement scores and B/D ratios for percentage boiler
efficiency increment from 0% to 40%. It can be projected from the rising
trend of positive total improvement score that boiler efficiency incre­
ment always provides net improvements on the GP-maximised POBC for
EFEW nexus concerns. Boiler efficiency improvement up to 20% brings
only EFEW-based benefits to the POBC thus no value of B/D ratio is
obtained with positive total improvement scores. Beyond 20% boiler
efficiency increment, the B/D ratios for the GP-optimal POBC exceeded
value 1 and increased with the boiler efficiency. Therefore, it is proven
that biomass boiler advancement within the targeted anchor process is
applicable and desirable towards sustainable development of an EFEW
14
Journal of Cleaner Production 314 (2021) 127927
Y.D. Tan et al.
flowsheet with maximised fuzzy membership degree in the fuzzy goals
of GP, net energy, land footprint, GHG emissions and WFP is generated.
To address long-term sustainability of the POBC with concerns in the
EFEW nexus, the anchor process in the designed POBC is determined as
the process stage which allocates the greatest overall degree of impact to
the performance of the fuzzy optimal POBC considering multiple con­
flicting EFEW objectives. Nut/kernel separation and CHP process stage
attains the highest EFEW nexus score thus is acknowledged as the EFEWbased anchor process for the given case study due to existing linkages
within the EFEW nexus. For application in multi-objective POBC
debottlenecking, the proposed cooperative game-based anchor process
targeting approach drives to the same conclusion as the system bottle­
neck identified via SSI method with justified difference in the process
stage allocation scores. The results of B/D ratio analysis have verified
the feasibility of anchor process advancement in terms of boiler effi­
ciency improvements for POBC performance enhancements towards
sustainable EFEW nexus. A comprehensive evaluation of POBC process
stage impacts in cleaner palm oil production and optimal EFEW syn­
ergies is provided in this study. The systematic cooperative game opti­
misation framework could assist capital distribution for process
maintenance and investment in any food production system with con­
cerns in the EFEW nexus.
should be targeted in the identified anchor process to address the waterenergy-GHG trade-offs for future sustainability enhancement in the
POBC.
Based on the outcome of anchor process targeting, decision-makers
with similar subjective weights on the EFEW objectives should priori­
tise technology advancement in the nut/kernel separation and CHP
system for the best integrated benefits to EFEW nexus development.
Moreover, the decision-maker could make full use of the EFEW nexus
scores as the reference for scheduling process maintenance and financial
planning after applying the fuzzy optimal POBC flowsheet for palm oil
mill retrofit. According to the B/D ratio analysis, the advancement in the
targeted anchor process in terms of boiler efficiency improvement is
valid for attractive overall improvement in EFEW nexus.
The proposed cooperative game approach in this study and the
previously applied SSI-based allocation method show different results
for critical scores allocation among the process stages in the same fuzzy
optimal POBC flowsheet. However, both approaches came to a similar
conclusion in identifying the anchor process and system bottleneck of
the POBC, in which the nut/kernel separation and CHP system obtained
the highest critical score among all five process stages in both methods.
The different values in critical score resulted from the two distinctive
methods suggest different definition and significance of cooperative
game-based anchor process and SSI-based system bottleneck which are
worth exploring and integrated for a more comprehensive POBC
debottlenecking framework in future work. It is worth noting that the
anchor process and EFEW nexus scores are highly dependent on the
EFEW weightage assigned by decision-makers thus different priority
weights on the evaluation objectives may produce different results. As
an example, the FFB pre-treatment stage may replace the nut/kernel
separation and CHP stage as the anchor process with a higher EFEW
nexus score, if the decision-maker aims for greater concern in reducing
WFP and/or lower focus in tackling GHG impacts. In this regard, a
standardised weightage could be useful in developing rational guide­
lines for EFEW nexus evaluation prior to new policy-making via
collaborative engagement of industry palm oil experts. Integration of
probability index could be considered in the future to broaden the
framework by addressing failure risks and reliability of process stages
for more concise results.
CRediT authorship contribution statement
Yue Dian Tan: Conceptualization, Methodology, Software, Formal
analysis, Visualization, Writing – original draft, preparation. Jeng
Shiun Lim: Conceptualization, Supervision, Data curation, Validation,
Writing – review & editing, Funding acquisition. Viknesh Andiappan:
Methodology, Writing – review & editing, Validation. Sharifah Rafidah
Wan Alwi: Supervision, Writing – review & editing, Resources.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
5. Conclusion
Acknowledgement
This paper presented an anchor process targeting approach based on
cooperative game theory and integration with the fuzzy optimisation
framework to perform optimal retrofit of palm oil mill into proposed
POBC with optimal EFEW nexus contributions. An optimal POBC
The authors would like to thank the technology suppliers for
providing essential information to this work and Universiti Teknologi
Malaysia (UTM) Research University Grant (grant number R.
J130000.7851.5F388 and Q.J130000.3551.05G97) for the funding.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2021.127927.
Appendix. Input data for POBC optimisation model
Table A.1
Defined resources and related data in POBC
i
Resource
Price (USD/t)
Purchase Cost (USD/t)
External Processing Cost (USD/t)
Availability (t/h)
1
2
3
4
5
6
7
8
9
10
FFB
POME
Sterilised fruit bunch
Empty fruit bunch (EFB)
Sterilised fruitlet (SF)
Digested fruitlet (DF)
Pressed liquid (PL)
Treated POME
Organic phase
Aqueous phase
–
–
–
6c
–
–
–
–
–
–
0c
–
–
–
–
2.99e
–
–
–
–
–
0.67e
2.53e
–
0.28e
0.18e
0.44e
–
–
–
60d
–
–
–
–
–
–
–
–
–
(continued on next page)
15
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table A.1 (continued )
a
i
Resource
Price (USD/t)
Purchase Cost (USD/t)
External Processing Cost (USD/t)
Availability (t/h)
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
CPO
Pressed solid (PS)
Palm fruit nut (PFN)
PMF
Cracked nut (CN)
PKS
PK
Decanter solid
PORE
Raw CPO
Treated PORE
Process concentrate
Process condensate
Water
RBDPOL
RBDPS
PFAD
Biogas
Medium pressure steam (MPS)
Low pressure steam (LPS)
Electricity
Electricity to grid
High pressure steam
492c
20e
–
22c
–
38c
389c
43c
–
–
–
–
–
–
555a
562a
406a
–
–
–
90c
107b
–
589c
20e
–
23c
–
45c
389c
–
–
–
–
–
–
0.71c
–
–
–
–
17c
12c
140c
–
–
1.5e
–
–
–
–
–
–
–
–
0.76e
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
60d
–
–
–
–
–
1d
–
–
–
Based on average price in September 2020.
Calculated based on Feed-in-tariff basic + bonus rates at USD 0.107 per kWh (locally manufactured gas engine technology below 40% efficiency).
c
Based on literature (Ng and Ng, 2013).
b
Table A.2
GHG emission factors in case study
a
Resource i
GHG emission (kgCO2eq/unit)
Unit
Ref.
2
24
29
30
31
32
+17.95
+0.34
+274.7
+270.2
+543
− 543
T
T
T
T
MWh
MWh
Calculationa
Jamaludin et al. (2019)
Calculationa
Calculationa
Asian Development Bank (2017)
Asian Development Bank (2017)
Calculated from case study.
Table A.3
POBC process selection and material conversion factors (Tan et al., 2020c)
p
Process
Input resource i
Required amount
Product i
Yield
1
Sterilisation
2
Threshing
1
30
3
1t
0.25 t
1t
3
Digestion
Pressing (Double pressing)
1t
0.19 t
1t
0.183 t
0.8997 t
0.24 t
0.76 t
1.04 t
4
5
30
6
2
3
4
5
6
5
Undiluted clarification
7
1t
6
Clarification (vertical clarifier)
7
3-phase decanter
7
24
10
1t
0.696 t
1t
8
Purification
9
1t
9
Nut separation
12
1t
10
11
Nut cracking
Kernel separation
13
15
1t
1t
12
POME evaporation
13
14
Undiluted purification
Solvent extraction
2
30
20
22
1t
0.25 t
1t
1t
7
12
20
18
2
9
10
9
2
18
2
11
13
14
15
14
16
17
22
23
11
20
0.6 t
0.4 t
0.58 t
0.09 t
0.33 t
0.54 t
1.156 t
0.02 t
0.867 t
0.113 t
0.034 t
0.928 t
0.59 t
0.41 t
0.99 t
0.19 t
0.357 t
0.453 t
0.143 t
0.853 t
0.93 t
0.03 t
(continued on next page)
16
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table A.3 (continued )
p
Process
Input resource i
15
16
Boiler feedwater treatment
PMF boiler
17
PKS boiler
18
Required amount
Steam turbine I
23
14
24
16
24
33
1t
1t
2.364 t
1t
3.482 t
1t
19
Steam turbine II
29
1t
20
Anaerobic digestion
2
1t
21
Biogas boiler
22
Physical refining and fractionation
24
28
11
24
30
0.87 t
1t
1t
0.18 t
0.0315 t
23
24
25
26
PORE mixer
Sequential Batch Reactor (SBR)
On-grid power plant
Gas engine
19
19
28
28
1t
1t
1t
1t
Product i
Yield
18
24
33
0.97 t
0.7 t
2.364 t
33
3.482 t
29
31
30
31
28
8
33
0.947 t
0.06026 MWh
1t
0.04362 MWh
0.278 t
1t
0.87 t
25
26
27
19
2
21
32
31
0.76 t
0.19 t
0.05 t
0.175 t
1t
1t
0.11 MWh
0.026
Table A.4
Costing and design parameters for available POBC technologies (Foong et al., 2019b)
a
Process p
Capacity indicating resource i
Design capacity (t/unit or MWh/unit)
Unit capital cost (M USD/unit)
Annual processing cost per process unit (USD/unit)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
18
19
20
21
22
24
25
26
1
3
5
6
7
7
10
9
12
13
15
2
20
22
31
31
28
28
11
19
32
31
20
40
20
20
20
7
20
10
10
10
10
22a
10
20
1
1
50
50
30
157
1.8
50
Existing
Existing
Existing
Existing
Existing
Existing
Existing
Existing
Existing
Existing
Existing
1.40a
Existing
0.18
Existing
0.14
2.24
2.16
Existing
0.34
5.04
0.24
180,000
33,750
15,000
20,000
35,000
15,000
35,000
55,000
30,000
36,000
13,000
12,000a
55,000
540
10
5
67,200
64,800
870
10,170
151,200
105
Provided by technology provider.
Table A.5
Binary input table for existing process stages f in scenarios z
Process failure scenario z
PindEXIST
p
p
1
2
3
4
5
6
7
8
9
10
11
12
13
1
2
3
1
1
1
4
5
1
1
9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
11
1
1
1
1
1
1
10
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
13
1
1
1
1
1
14
15
1
1
1
1
1
1
1
1
16
17
18
19
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
22
23
1
1
1
1
1
1
(continued on next page)
17
Journal of Cleaner Production 314 (2021) 127927
Y.D. Tan et al.
Table A.5 (continued )
Process failure scenario z
PindEXIST
p
p
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
1
1
1
1
1
1
2
1
1
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4
1
1
1
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
10
11
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
13
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
14
15
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
16
17
18
19
22
23
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
14
15
16
17
18
19
22
23
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Table A.6
Binary input table for correlated process stages f in scenarios z
Process failure scenario z
PindREL
p
p
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
10
11
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
13
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Table A.7
Binary input table for integrated process stages f in scenarios z
Process failure scenario z
PindINT
p
p
1
2
3
4
1
2
3
4
5
9
10
11
12
13
14
15
16
17
18
19
22
23
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
(continued on next page)
18
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table A.7 (continued )
Process failure scenario z
PindINT
p
p
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
10
11
12
13
14
15
16
17
18
19
22
23
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Table A.8
Binary input table for by-passing process stages f in scenarios z
Process failure scenario z
PindBYPASS
p
p
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4
5
9
10
11
12
13
14
15
16
17
18
19
22
23
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
19
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Journal of Cleaner Production 314 (2021) 127927
Y.D. Tan et al.
Table A.9
Binary input table for external processing resource, additional by-product indicator and external generated resources in scenarios z.
Process failure scenario z
RindEXPRO
i
i
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
BYindADD
i
RindEXGEN
i
2
20
2
11
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
11
1
1
1
1
1
Table A.10
Identified intermediate materials i to be purchased and import process resource availability during process p failures.
By-passing process p
Externally processed intermediate material i
Available intermediate material i
Availability of intermediate material (t/h)
3
5
6
7
11
–
–
–
–
–
12
–
29
30
–
–
–
18 t
–
50 t
50 t
1
2
3
4
13
18
Table A.11
Breakdown of input values in PWOBJ
f =1 calculation for process stage f = 1 with respect to GP, NE, GHGBAL, TWFP based on Eq. (22)
z
f
GPimp
(M USD/y)
z
v(z)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1
2
3
4
5
1,2
1,3
1,4
1,5
2,3
2,4
2,5
3,4
3,5
4,5
0.00
0.00
0.00
0.00
0.00
1.69
0.14
8.55
2.46
0.08
7.39
2.46
0.00
− 0.08
2.81
GHGBALz (kgCO2eq/h)
NEimp
(MWh)
z
v(z-{1})
v(z) –
v(z-{1})
0.00
0.00
0.00
0.00
0.00
0.00
1.69
0.14
8.55
2.46
v(z)
−
−
−
−
−
−
−
0.00
0.00
0.00
0.00
0.00
0.25
0.19
3.20
1.50
0.19
3.19
1.50
4.70
4.89
1.50
v(z-{1})
v(z) –
v(z-{1})
0.00
0.00
0.00
0.00
0.00
0.00
− 0.25
− 0.19
3.20
− 1.50
TWFPz (t/h)
v(z)
v(z-{1})
v(z) –
v(z-{1})
6538.8
284.4
0.0
0.0
975.9
6823.2
7184.1
214.6
7514.7
803.3
154.2
1260.3
0.0
1196.0
55.5
6538.8
0.0
284.4
0.0
0.0
975.9
6538.8
7184.1
214.6
6538.8
v(z)
1.11
0.85
0.00
0.00
2.72
1.96
0.00
24.12
3.83
0.00
0.85
3.58
0.00
1.04
2.72
v(z-{1})
v(z) –
v(z-{1})
1.11
0.00
0.85
0.00
0.00
2.72
1.11
0.00
24.12
1.11
(continued on next page)
20
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table A.11 (continued )
z
f
GPimp
(M USD/y)
z
v(z)
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1,2,3
1,2,4
1,2,5
1,3,4
1,3,5
1,4,5
2,3,4
2,3,5
2,4,5
3,4,5
1,2,3,4
1,2,3,5
1,2,4,5
1,3,4,5
2,3,4,5
1,2,3,4,5
2.03
10.25
4.14
8.86
2.68
11.35
7.63
2.62
10.15
2.79
10.87
4.58
13.05
11.76
10.49
13.78
GHGBALz (kgCO2eq/h)
NEimp
(MWh)
z
v(z-{1})
v(z) –
v(z-{1})
0.08
7.39
2.46
0.00
− 0.08
2.81
1.95
2.85
1.69
8.86
2.76
8.54
7.63
2.62
10.15
2.79
3.24
1.96
2.90
8.97
10.49
3.29
SUM
v(ℵ)
59.85
13.78
v(z)
− 0.43
2.94
− 1.75
3.02
− 1.69
1.70
3.00
− 1.69
1.69
− 1.69
2.76
− 1.93
1.44
1.52
1.50
1.26
v(z-{1})
v(z) –
v(z-{1})
− 0.19
3.19
− 1.50
4.70
− 4.89
− 1.50
−
−
−
−
0.25
0.25
0.25
1.69
3.20
3.21
3.00
− 1.69
1.69
− 1.69
− 0.25
− 0.25
− 0.25
3.21
1.50
− 0.25
SUM
v(ℵ)
7.49
1.26
TWFPz (t/h)
v(z)
v(z-{1})
v(z) –
v(z-{1})
v(z)
7886.7
366.0
7799.1
17.6
8279.9
259.1
2.4
1898.9
209.1
13.2
17.7
8983.2
410.5
18.9
13.7
19.0
803.3
154.2
1260.3
0.0
1196.0
55.5
7083.4
211.7
6538.8
17.6
7083.9
203.6
2.4
1898.9
209.1
13.2
15.4
7084.3
201.4
5.6
0.00
24.98
4.69
19.36
0.00
27.31
0.00
0.00
3.58
1.08
16.55
0.00
28.16
21.50
0.00
18.70
13.7
5.3
SUM
v(ℵ)
48927.2
19.0
v(z-{1})
v(z) –
v(z-{1})
0.00
0.85
3.58
0.00
1.04
2.72
0.00
24.12
1.11
19.36
− 1.04
24.58
0.00
0.00
3.58
1.08
16.55
0.00
24.58
20.42
0.00
18.70
SUM
v(ℵ)
174.72
18.70
Table A.12
Breakdown of input values in PWOBJ
f =2 calculation for process stage f = 2 with respect to GP, NE, GHGBAL, TWFP based on Eq. (22)
z
f
(M USD/y)
GPimp
z
v(z)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
1,2
1,3
1,4
1,5
2,3
2,4
2,5
3,4
3,5
4,5
1,2,3
1,2,4
1,2,5
1,3,4
1,3,5
1,4,5
2,3,4
2,3,5
2,4,5
3,4,5
1,2,3,4
1,2,3,5
1,2,4,5
1,3,4,5
2,3,4,5
1,2,3,4,5
0.00
0.00
0.00
0.00
0.00
1.69
0.14
8.55
2.46
0.08
7.39
2.46
0.00
− 0.08
2.81
2.03
10.25
4.14
8.86
2.68
11.35
7.63
2.62
10.15
2.79
10.87
4.58
13.05
11.76
10.49
13.78
v(z-{2})
v(z) –
v(z-{2})
0.00
0.00
0.00
1.69
0.00
0.00
0.00
0.08
7.39
2.46
0.14
8.55
2.46
GHGBALz (kgCO2eq/h)
NEimp
(MWh)
z
1.89
1.70
1.69
v(z)
−
−
−
−
−
−
−
−
−
−
0.00
− 0.08
2.81
7.63
2.70
7.34
8.86
2.68
11.35
2.01
1.90
1.70
2.79
11.76
7.70
2.02
SUM
v(ℵ)
49.88
13.78
−
−
−
0.00
0.00
0.00
0.00
0.00
0.25
0.19
3.20
1.50
0.19
3.19
1.50
4.70
4.89
1.50
0.43
2.94
1.75
3.02
1.69
1.70
3.00
1.69
1.69
1.69
2.76
1.93
1.44
1.52
1.50
1.26
v(z-{2})
v(z) –
v(z-{2})
0.00
0.00
0.00
− 0.25
0.00
0.00
0.00
− 0.19
3.19
− 1.50
− 0.19
3.20
− 1.50
− 0.25
− 0.26
− 0.25
4.70
− 4.89
− 1.50
− 1.70
3.20
3.19
3.02
− 1.69
1.70
− 0.26
− 0.24
− 0.26
− 1.69
1.52
3.19
− 0.26
SUM
v(ℵ)
7.35
1.26
21
v(z)
6538.8
284.4
0.0
0.0
975.9
6823.2
7184.1
214.6
7514.7
803.3
154.2
1260.3
0.0
1196.0
55.5
7886.7
366.0
7799.1
17.6
8279.9
259.1
2.4
1898.9
209.1
13.2
17.7
8983.2
410.5
18.9
13.7
19.0
TWFPz (t/h)
v(z-{2})
v(z) –
v(z-{2})
284.4
0.0
6538.8
284.4
0.0
0.0
975.9
803.3
154.2
284.4
7184.1
214.6
7514.7
702.6
151.4
284.4
0.0
1196.0
55.5
2.4
702.9
153.5
17.6
8279.9
259.1
0.1
703.3
151.4
13.2
18.9
0.5
0.1
SUM
v(ℵ)
4378.8
19.0
v(z)
1.11
0.85
0.00
0.00
2.72
1.96
0.00
24.12
3.83
0.00
0.85
3.58
0.00
1.04
2.72
0.00
24.98
4.69
19.36
0.00
27.31
0.00
0.00
3.58
1.08
16.55
0.00
28.16
21.50
0.00
18.70
v(z-{2})
v(z) –
v(z-{2})
0.85
0.00
1.11
0.85
0.00
0.00
2.72
0.00
0.85
0.85
0.00
24.12
3.83
0.00
0.85
0.85
0.00
1.04
2.72
0.00
− 1.04
0.85
19.36
0.00
27.31
− 2.81
0.00
0.85
1.08
21.50
− 1.08
− 2.80
SUM
v(ℵ)
− 1.77
18.70
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table A.13
Breakdown of input values in PWOBJ
f =3 calculation for process stage f = 3 with respect to GP, NE, GHGBAL, TWFP based on Eq. (22)
z
f
GPimp
(M USD/y)
z
v(z)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
1,2
1,3
1,4
1,5
2,3
2,4
2,5
3,4
3,5
4,5
1,2,3
1,2,4
1,2,5
1,3,4
1,3,5
1,4,5
2,3,4
2,3,5
2,4,5
3,4,5
1,2,3,4
1,2,3,5
1,2,4,5
1,3,4,5
2,3,4,5
1,2,3,4,5
0.00
0.00
0.00
0.00
0.00
1.69
0.14
8.55
2.46
0.08
7.39
2.46
0.00
− 0.08
2.81
2.03
10.25
4.14
8.86
2.68
11.35
7.63
2.62
10.15
2.79
10.87
4.58
13.05
11.76
10.49
13.78
v(z-{3})
GHGBALz (kgCO2eq/h)
NEimp
(MWh)
z
v(z) –
v(z-{3})
v(z)
0.00
0.00
0.00
0.14
−
−
0.00
0.08
−
−
0.00
0.00
0.00
− 0.08
1.69
0.35
8.55
2.46
0.31
0.22
7.39
2.46
0.24
0.16
2.81
10.25
4.14
− 0.02
0.62
0.44
11.35
10.15
13.05
0.42
0.34
0.73
SUM
v(ℵ)
3.95
13.78
−
−
−
−
−
−
−
−
−
0.00
0.00
0.00
0.00
0.00
0.25
0.19
3.20
1.50
0.19
3.19
1.50
4.70
4.89
1.50
0.43
2.94
1.75
3.02
1.69
1.70
3.00
1.69
1.69
1.69
2.76
1.93
1.44
1.52
1.50
1.26
v(z-{3})
v(z) –
v(z-{3})
0.00
0.00
0.00
− 0.19
0.00
− 0.19
0.00
0.00
4.70
− 4.89
− 0.25
− 0.19
3.20
− 1.50
− 0.19
− 0.19
3.19
− 1.50
− 0.19
− 0.19
− 1.50
2.94
− 1.75
− 0.19
− 0.19
− 0.19
1.70
1.69
1.44
− 0.19
− 0.19
− 0.19
SUM
v(ℵ)
− 2.60
1.26
v(z)
6538.8
284.4
0.0
0.0
975.9
6823.2
7184.1
214.6
7514.7
803.3
154.2
1260.3
0.0
1196.0
55.5
7886.7
366.0
7799.1
17.6
8279.9
259.1
2.4
1898.9
209.1
13.2
17.7
8983.2
410.5
18.9
13.7
19.0
v(z-{3})
TWFPz (t/h)
v(z) –
v(z-{3})
0.0
0.0
6538.8
645.3
284.4
518.9
0.0
975.9
0.0
220.1
6823.2
1063.5
214.6
7514.7
− 197.0
765.2
154.2
1260.3
− 151.9
638.6
55.5
366.0
7799.1
− 42.3
− 348.3
1184.1
259.1
209.1
410.5
− 240.3
− 195.4
− 391.5
SUM
v(ℵ)
3469.2
19.0
v(z)
1.11
0.85
0.00
0.00
2.72
1.96
0.00
24.12
3.83
0.00
0.85
3.58
0.00
1.04
2.72
0.00
24.98
4.69
19.36
0.00
27.31
0.00
0.00
3.58
1.08
16.55
0.00
28.16
21.50
0.00
18.70
v(z-{3})
v(z) –
v(z-{3})
0.00
0.00
1.11
− 1.11
0.85
− 0.85
0.00
2.72
0.00
− 1.68
1.96
− 1.96
24.12
3.83
− 4.77
− 3.83
0.85
3.58
− 0.85
− 3.58
2.72
24.98
4.69
− 1.64
− 8.43
− 4.69
27.31
3.58
28.16
− 5.81
− 3.58
− 9.46
SUM
v(ℵ)
− 52.23
18.70
Table A.14
Breakdown of input values in PWOBJ
f =4 calculation for process stage f = 4 with respect to GP, NE, GHGBAL, TWFP based on Eq. (22)
z
f
GPimp
(M USD/y)
z
v(z)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
1
2
3
4
5
1,2
1,3
1,4
1,5
2,3
2,4
2,5
3,4
3,5
4,5
1,2,3
1,2,4
1,2,5
1,3,4
1,3,5
1,4,5
2,3,4
2,3,5
2,4,5
3,4,5
1,2,3,4
0.00
0.00
0.00
0.00
0.00
1.69
0.14
8.55
2.46
0.08
7.39
2.46
0.00
− 0.08
2.81
2.03
10.25
4.14
8.86
2.68
11.35
7.63
2.62
10.15
2.79
10.87
v(z-{4})
0.00
0.00
0.00
0.00
v(z) –
v(z-{4})
v(z)
0.00
8.55
7.39
0.00
0.00
2.81
1.69
8.56
0.14
GHGBALz (kgCO2eq/h)
NEimp
(MWh)
z
8.72
2.46
0.08
8.89
7.55
2.46
− 0.08
2.03
7.69
2.87
8.83
−
−
−
−
−
−
−
−
−
−
−
−
0.00
0.00
0.00
0.00
0.00
0.25
0.19
3.20
1.50
0.19
3.19
1.50
4.70
4.89
1.50
0.43
2.94
1.75
3.02
1.69
1.70
3.00
1.69
1.69
1.69
2.76
v(z-{4})
v(z) –
v(z-{4})
0.00
0.00
0.00
3.20
0.00
3.19
0.00
4.70
0.00
− 1.50
− 0.25
3.19
− 0.19
3.20
− 1.50
− 0.19
3.20
3.19
− 1.50
− 4.89
− 0.43
3.19
3.20
3.19
v(z)
6538.8
284.4
0.0
0.0
975.9
6823.2
7184.1
214.6
7514.7
803.3
154.2
1260.3
0.0
1196.0
55.5
7886.7
366.0
7799.1
17.6
8279.9
259.1
2.4
1898.9
209.1
13.2
17.7
v(z-{4})
TWFPz (t/h)
v(z) –
v(z-{4})
0.0
0.0
6538.8
− 6324.2
284.4
− 130.2
0.0
0.0
975.9
− 920.4
6823.2
− 6457.2
7184.1
− 7166.5
7514.7
803.3
− 7255.6
− 801.0
1260.3
1196.0
7886.7
− 1051.3
− 1182.8
− 7869.0
v(z)
1.11
0.85
0.00
0.00
2.72
1.96
0.00
24.12
3.83
0.00
0.85
3.58
0.00
1.04
2.72
0.00
24.98
4.69
19.36
0.00
27.31
0.00
0.00
3.58
1.08
16.55
v(z-{4})
v(z) –
v(z-{4})
0.00
0.00
1.11
23.01
0.85
0.00
0.00
0.00
2.72
0.00
1.96
23.01
0.00
19.36
3.83
0.00
23.47
0.00
3.58
1.04
0.00
0.00
0.04
16.55
(continued on next page)
22
Y.D. Tan et al.
Journal of Cleaner Production 314 (2021) 127927
Table A.14 (continued )
z
f
GPimp
(M USD/y)
z
v(z)
27
28
29
30
31
1,2,3,5
1,2,4,5
1,3,4,5
2,3,4,5
1,2,3,4,5
4.58
13.05
11.76
10.49
13.78
GHGBALz (kgCO2eq/h)
NEimp
(MWh)
z
v(z-{4})
v(z) –
v(z-{4})
4.14
2.68
2.62
4.58
8.90
9.08
7.87
9.20
SUM
v(ℵ)
106.93
13.78
v(z)
− 1.93
1.44
1.52
1.50
1.26
v(z-{4})
v(z) –
v(z-{4})
1.75
1.69
1.69
1.93
3.19
3.20
3.19
3.19
SUM
v(ℵ)
44.70
1.26
−
−
−
−
v(z)
v(z-{4})
8983.2
410.5
18.9
13.7
19.0
7799.1
8279.9
1898.9
8983.2
SUM
v(ℵ)
TWFPz (t/h)
v(z) –
v(z-{4})
−
−
−
−
7388.6
8261.0
1885.2
8964.2
v(z)
0.00
28.16
21.50
0.00
18.70
− 65657.1
19.0
v(z-{4})
v(z) –
v(z-{4})
4.69
0.00
0.00
0.00
23.47
21.50
0.00
18.70
SUM
v(ℵ)
169.12
18.70
Table A.15
Breakdown of input values in PWOBJ
f =5 calculation for process stage f = 5 with respect to GP, NE, GHGBAL, TWFP based on Eq. (22)
z
f
GPimp
(M USD/y)
z
v(z)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
1,2
1,3
1,4
1,5
2,3
2,4
2,5
3,4
3,5
4,5
1,2,3
1,2,4
1,2,5
1,3,4
1,3,5
1,4,5
2,3,4
2,3,5
2,4,5
3,4,5
1,2,3,4
1,2,3,5
1,2,4,5
1,3,4,5
2,3,4,5
1,2,3,4,5
0.00
0.00
0.00
0.00
0.00
1.69
0.14
8.55
2.46
0.08
7.39
2.46
0.00
− 0.08
2.81
2.03
10.25
4.14
8.86
2.68
11.35
7.63
2.62
10.15
2.79
10.87
4.58
13.05
11.76
10.49
13.78
v(z-{5})
0.00
GHGBALz (kgCO2eq/h)
NEimp
(MWh)
z
v(z) –
v(z-{5})
v(z)
0.00
−
−
0.00
2.46
−
−
0.00
2.46
−
0.00
0.00
− 0.08
2.81
−
−
−
1.69
2.46
−
0.14
8.55
2.54
2.80
−
0.08
7.39
0.00
2.54
2.75
2.79
−
2.03
10.25
8.86
7.63
10.87
2.55
2.80
2.90
2.86
2.91
−
SUM
v(ℵ)
37.55
13.78
−
0.00
0.00
0.00
0.00
0.00
0.25
0.19
3.20
1.50
0.19
3.19
1.50
4.70
4.89
1.50
0.43
2.94
1.75
3.02
1.69
1.70
3.00
1.69
1.69
1.69
2.76
1.93
1.44
1.52
1.50
1.26
v(z-{5})
v(z) –
v(z-{5})
0.00
0.00
0.00
− 1.50
0.00
− 1.50
0.00
0.00
− 4.89
− 1.50
− 0.25
− 1.50
− 0.19
3.20
− 1.50
− 1.50
− 0.19
3.19
4.70
− 1.50
− 1.50
− 6.39
− 0.43
2.94
3.02
3.00
2.76
−
−
−
−
−
SUM
v(ℵ)
1.50
1.50
1.50
1.50
1.50
− 30.78
1.26
References
v(z)
6538.8
284.4
0.0
0.0
975.9
6823.2
7184.1
214.6
7514.7
803.3
154.2
1260.3
0.0
1196.0
55.5
7886.7
366.0
7799.1
17.6
8279.9
259.1
2.4
1898.9
209.1
13.2
17.7
8983.2
410.5
18.9
13.7
19.0
v(z-{5})
TWFPz (t/h)
v(z) –
v(z-{5})
975.9
0.0
6538.8
975.9
284.4
975.9
0.0
0.0
1196.0
55.5
6823.2
975.9
7184.1
214.6
1095.8
44.5
803.3
154.2
0.0
1095.6
54.8
13.2
7886.7
366.0
17.6
2.4
17.7
1096.5
44.5
1.2
11.3
1.2
SUM
v(ℵ)
7638.2
19.0
v(z)
1.11
0.85
0.00
0.00
2.72
1.96
0.00
24.12
3.83
0.00
0.85
3.58
0.00
1.04
2.72
0.00
24.98
4.69
19.36
0.00
27.31
0.00
0.00
3.58
1.08
16.55
0.00
28.16
21.50
0.00
18.70
v(z-{5})
v(z) –
v(z-{5})
2.72
0.00
1.11
2.72
0.85
2.72
0.00
0.00
1.04
2.72
1.96
2.72
0.00
24.12
0.00
3.18
0.00
0.85
0.00
0.00
2.72
1.08
0.00
24.98
19.36
0.00
16.55
0.00
3.18
2.14
0.00
2.15
SUM
v(ℵ)
26.40
18.70
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24