A Systematic Approach for the Synthesis and Optimization of Palm Oil

Subscriber access provided by READING UNIV

Article

A Systematic Approach for the Synthesis and Optimisation of Palm Oil Milling Processes Steve Z. Y. Foong, Yi Ling Lam, Viknesh Andiappan, Dominic Chwan Yee Foo, and Denny K. S. Ng Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b04788 • Publication Date (Web): 25 Jan 2018 Downloaded from http://pubs.acs.org on January 30, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Industrial & Engineering Chemistry Research

1

A Systematic Approach for the Synthesis and

2

Optimisation of Palm Oil Milling Processes

3 4

Steve Z. Y. Foong a, Yi Ling Lam a, Viknesh Andiappan b, Dominic C. Y. Foo a, Denny K. S. Ng a*

5 6 7 8 9 10

a

Department of Chemical and Environmental Engineering/Centre of Sustainable Palm Oil Research (CESPOR), The University of Nottingham Malaysia Campus, Broga Road, Semenyih 43500, Malaysia b School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, 62200, Putrajaya, Wilayah Persekutuan Putrajaya, Malaysia

11

ABSTRACT

12

Palm oil mill (POM) requires large amounts of steam and electricity to convert fresh fruit bunches

13

(FFBs) into multiple products. During the extraction of crude palm oil from FFBs, by-products

14

such as empty fruit bunches, palm kernel shell, etc. and palm oil mill effluent are generated. In

15

the past decades, palm oil millers have attempted various improvements in milling technologies

16

individually and collectively, to enhance extraction efficiency and to meet the process and product

17

requirements. However, oil lost in the milling process remains the major issue in POM and leads

18

to huge loss of profit. In order to address such issue, oil recovery technologies were introduced

19

and implemented in the current POM. Nevertheless, such technologies come with additional

20

capital investment and operating costs that may outweigh the profit generated. Therefore, in this

21

work, a systematic approach is presented to synthesise the palm oil milling processes with oil

22

recovery technologies which is technically feasible and economic viable.

ACS Paragon Plus Environment

Multi-period

1

Industrial & Engineering Chemistry Research 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 36

1

consideration is also incorporated in the optimisation model to address the variation in feedstock

2

availability.

3 4

Keywords: Palm oil mill; Process synthesis; Process optimisation; Oil recovery technologies;

5

Multi-period optimisation.

6 7

1.

8

Over the past few decades, palm oil industry has expanded dramatically as one of a major oils and

9

fats providers for global needs. In year 2016, palm oil contributes up to 30% of oils and fats

10

production globally1. As reported by American Soybean Association2, palm oil accounts for 62.1

11

million metric tonne (MT) out of 177.2 million MT of vegetable oils consumed worldwide

12

(contributed to 35%). Note that more than 17 million MT of crude palm oil (CPO) are produced

13

in Malaysia3, making Malaysia the second largest producer and exporter of palm oil products after

14

Indonesia. As the second largest producers and exporters of CPO, Malaysia plays an important

15

role in fulfilling the growing global need for oils and fats sustainability.

INTRODUCTION

16

In the palm oil industry, fresh fruit bunches (FFBs) are first harvested in oil palm plantation

17

and sent to palm oil mills (POM) to produce CPO. Figure 2 shows a typical process flow diagram

18

of palm oil milling process. As shown, the process can be generally divided into several unit

19

operations. Firstly, FFB is sterilised to deactivate any enzymatic activity and loosen the fruitlet

20

from the bunches. Different sterilisation technologies (i.e., horizontal, vertical4, continuous5 and

21

tilted6 sterilisers) were currently used in POMs. Besides, several sterilisation patterns from single-


2

Page 3 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

to triple-peak steam cycles are practised to enhance the oil extraction efficiency7,8 Next, the

2

fruitlets are removed in threshing process from empty fruit bunch (EFB)9 before digestion and

3

pressing processes4.

4

machine11,12 are commonly used.

In the current POM, mechanical screw press10 and double pressing

Fresh Fruit Bunch (FFB) Palm Oil Mill Effluent (POME)

Sterilisation Sterilised fruit bunch

Empty Fruit Bunches (EFBs)

Threshing Sterilised fruitlet

Digestion Digested fruitlet

Pressing Liquid product

Solid product

Decanter Cake (DC)

Nut Separation

Palm Pressed Fibre (PPF)

Palm nut

Clarification

Nut Cracking

Palm Oil Mill Effluent (POME)

Cracked mixture

Recovered Oil

Kernel Separation

Purification

Palm Kernel Shell (PKS)

Wet kernel

Drying

5 6

Crude Palm Oil (CPO)

Palm Kernel (PK)

Figure 1. Typical palm oil mill processing unit operations

7

During the pressing process, solid and liquid products are generated. The solid product

8

consists of palm pressed fibre (PPF) and palm nuts, which was then sent to the nut separation

9

system (e.g., inclined rotary separator13, depericarper14). The palm nuts are then cracked into palm


3


Page 4 of 36

1

kernel (PK) and palm kernel shell (PKS), and separated via clay bath, hydrocyclone15 or multiple-

2

staged winnowing system16,17. On the other hand, the liquid product that is made up of water, oil

3

and fibrous materials18 is sent for clarification process to remove the entrained impurities. In this

4

respect, technologies such as clarification tanks4 and decanters19,20 are utilised. The entrained solid

5

particles are removed as decanter cake (DC), while the water discharge is commonly known as

6

palm oil mill effluent (POME). The oil is then further purified into CPO through centrifugal and

7

drying operations.

8

Based on above discussion, it is noted that many alternative technologies can be used for

9

different unit operations in a POM. Note also that throughout the milling process, various by-

10

products such as PPF, PKS, DC, EFB and POME are being generated. In the current practise, PPF

11

and PKS are used as feedstock for biomass boiler for steam and power generation21,22, while DC

12

is used as an ingredient in ruminant feedstock23,24. Meanwhile, EFB are converted to value added

13

products (e.g. pellet, biofertiliser, dried long fiber, etc.) or returned to the plantation as mulching

14

material.

15

Note that significant amount of POME, which contains high organic contents (i.e. high

16

chemical (COD) and biochemical oxygen demand (BOD) values25) are generated throughout the

17

milling process. The effluent is acidic with pH ranging between 4 – 526 in which a direct discharge

18

to the watercourse will be a threat to the environment. Besides, POME releases methane gas

19

anaerobically27 which has been recognised as one of the main causes of global warming.

20

Therefore, to minimise the pollution, stricter regulatory control through Environmental Quality

21

Regulations, 1997 is enforced by Department of Environmental (DOE)28. Most POMs in Malaysia

22

are utilising the ponding system for POME treatment while some opted for open digesting tank29.


4

Page 5 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

High-rate anaerobic bioreactors have been developed to treat POME while generating biogas. This

2

however involves a higher capital and maintenance costs.

3

In order to develop a sustainable POM, all by-products or waste should be fully recovered.

4

In addition, oil which is trapped in the by-products and POME should also be recovered. Chaisri

5

et al.30 reported that 10.6 g/L of oil and grease were trapped in POME. EFB and PPF contains

6

approximate 3-4%31 and 1.8-3.96%32 of residual oil (wet basis), respectively. A rough estimation

7

of 10% oil lost occurs across the entire milling process33. As a result, a 10% profit cut was expected

8

as CPO serves as the main product and income generator in a POM. To address this issue, various

9

technologies such as tilted steriliser6, double screw press11,12, vacuum clarifier, etc. have been

10

developed to reduce oil losses in the milling process. Furthermore, oil recovery technologies such

11

as EFB pressing screw31 and three-phase decanter20 were introduced to recover oil from EFB and

12

POME, which in turn improve the overall oil yield. However, most technology providers only

13

focused on individual equipment or process. Besides, such technologies come with additional

14

capital investment and operating costs. Hence, there is a need to develop a systematic approach

15

for flowsheet synthesis and optimisation of palm oil milling process which performs a trade-off

16

analysis between increments in oil yield and costs.

17

According to the literature34,35, various process synthesis tools have been established to

18

synthesis flowsheet (the optimal interconnection of processing systems as well as the optimal type

19

and design of the units within a process system36). Process System Engineering (PSE) is an area

20

of work in which systematic computer-based approaches are developed to synthesise a

21

flowsheet37,38. Conventionally, flowsheet synthesis is carried out in a hierarchical approach which

22

may be divided into three distinct levels, namely synthesis optimisation, design optimisation and

23

operational optimisation, to be solved in sequence39. The hierarchical approach has been used in


5


Page 6 of 36

1

various studies such as synthesis of wastewater treatment40, thermal41, biorefinery42,43 and chlor-

2

alkali production systems44. Despite its simplicity, the hierarchical approach possesses a challenge

3

when operational failures and other uncertainties are considered. As a result, the flowsheet

4

developed is no longer optimal under such operational uncertainties. To overcome the previous

5

limitation, mathematical programming approaches were developed for flowsheet synthesis. As

6

shown in the literature, many mathematical optimisation approaches for flowsheet synthesis have

7

been presented45,46.

8

biorefinery50,51, industrial symbiosis52, biomass trigeneration53,54 and biogas systems55. It has been

9

proven that mathematical approaches are capable in handling uncertainties such as price

10

These include the synthesise of water network

47

, trigeneration

48,49

,

variations48, supply and demand changes53 as well as operational reliability54.

11

In general, uncertainties can be categorised into short and long-term types. Short term

12

uncertainties include failure in equipment and variation in operation56, while long term

13

uncertainties include fluctuation in product demand, feedstock supplies and costs57. CPO price is

14

an example of long term uncertainties as it fluctuates throughout the year, depending on the

15

international economic conditions. According to Malaysia Palm Oil Board (MPOB)58, in August

16

2015 where CPO is under high demand, CPO price hikes up to 720 USD/t but the price also falls

17

to 475 USD/t in November 2016 during low CPO demand. In addition to economic considerations,

18

operational uncertainties such as variations in feedstock availability must be considered in the

19

optimisation process studied as well. The operation of POM is scheduled based on the availability

20

and productivity of FFBs, which is subjected to various natural factors (e.g. rain fall, seasons, haze,

21

etc.). To maintain the quality of oil produced, the fresh harvested FFBs must be processed within

22

24 hours. This causes the utility (e.g. water and steam) and electricity consumption of a mill

23

changes accordingly. Performance of operations may deviate from the optimal solution if these


6

Page 7 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

uncertainties were not considered. Hence, a systematic multi-period optimisation approach is used

2

to address such variation. Previous research works on multi-period optimisation have been

3

performed and presented on trigeneration59, poly-generation system60, and hydrogen networks61,62.

4

The abovementioned contributions focused on developing process synthesis approaches for

5

integrated biorefinery, resource networks or energy systems in which water and energy (i.e. heat

6

and electricity) consumption are used as the major parameter in the optimisation models.

7

However, limited works were reported for the synthesis of POM flowsheet. In fact, there is a

8

scarce number of contributions particularly focused on tracing the oil content in POM flowsheet

9

synthesis. This is essential as oil loss will lead to huge losses in profit in the POMs. In this context,

10

is necessary to trace the oil content when a POM flowsheet is synthesised. This allows the oil lost

11

in each unit operation and by-product to be determined, providing a detailed and quantitative

12

measurement on the oil yield in the milling process. As such, this work presents a systematic

13

approach to determine the optimal technology for FFBs processing with consideration of oil

14

content, energy consumptions, process capacity and their respective economic impact. In addition,

15

seasonal feedstock supply and product prices is considered in the flowsheet development. To

16

illustrate the proposed approach, a typical palm oil milling case study is solved to produce a POM

17

configuration that can cope with potential changes and ensure operational stability.

18

In the following section, the problem statement and a generic superstructure for palm oil

19

milling process developed for this work is presented. A detailed formulation for material balance,

20

utility balance and economic analysis with multiperiod optimisation considered is delivered in

21

Section 3. Next, a typical palm oil milling process in Malaysia is synthesised and optimised in

22

Section 4, based on the proposed approach previously.

23

compared with the conventional milling process to highlight the improvements achieved. It is then

The flowsheet developed are then


7


Page 8 of 36

1

followed by a sensitivity analysis on the variation in product prices. Lastly, a conclusion of this

2

work is drawn and given at the end of this paper.

3

2.

4

As mentioned previously, POM flowsheet synthesis, particularly with regards to considering oil

5

loss has received limited attention. In this respect, the problem addressed in this work is based on

6

a generic graphical representation a shown in Figure 2. The synthesis problem is stated as follows.

7

The feedstock i with given flowrate of Fi (in this case, FFB is the only feedstock) can be converted

8

to intermediate product p ϵ P (e.g. sterilised fruit bunch, FSFB, pressed liquid, FPL, etc.) through

9

technology j ϵ J. Intermediate product p can then be further converted to final product p′ ϵ P′ (e.g.

10

CPO and PK) via technology j′ ϵ J′. By-products (e.g. PKS, PPF, DC, etc.) are represented as a

11

form of intermediate product p or final product p′ in the model. The oil content of feedstock i,

12

intermediate product p and final product p′ are defined as Oi, Op and Op’ respectively. Note that

13

every technology j ϵ J and j′ ϵ J′ may have more than one inlet and outlet stream, allowing every

14

stream to merge or split, depending on constraints set on the model. Besides, intermediate product

15

p can also be taken as final product p’ if it could be sold directly. The mass conversion (X), oil

16

loss (L) and oil recovery (R) of technology j from feedstock i and technology j’ from intermediate

17

product p are specified as Xijp, Xpj’p’, Lijp, Lpj’p’ Rijp and Rpj’p’ respectively. Meanwhile, utility

18

consumption FuCon (e.g. low pressure steam, FLPS, medium pressure steam, FMPS and utility water,

19

FWATER) and electricity consumption EeCon for the entire mill are specified as Uujp, Yejp for

20

technology j and Uuj’p’, Yej’p’ for technology j′.

PROBLEM STATEMENT


8

Page 9 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Feed (i)

Primary Technology (j)

Intermediate Product (p)

Secondary Technology (j

Final Product (p

i=1

j=1

p=1

j' = 1

p' = 1

i=2

j=2

p=2

j' = 2

p' = 2

i=I

j=J

p=P

j' = J'

p' = P'

1 2

Figure 2. Generic representation of superstructure for scenario s

3

In this work, the objective is to develop a systematic approach to generate an optimal and

4

robust POM configuration with maximum economic performance (EP). The material flow in

5

every technologies j ϵ J and j′ ϵ J′ will be traced accordingly. At the same time, multiple fruit

6

supply scenarios, s ϵ S has been considered. Usually, the available equipment in the market have

7

a fixed design capacity of technology j ( Fj

8

will determine the number of units required for technologies j and j′ selected, represented by zj and

9

zj’ respectively. The total capital cost CAPEX and total operating cost OPEX can be calculated

10

Design

Design

) and j′ ( Fj '

), therefore, the proposed approach

from the capital cost based on the selected technology j and j′ (CCj, CCj’, OCj and OCj’).

11

The following section describes the developed model, the parameters and the variables

12

involved in a more descriptive manner. The equations formulating the optimisation model are

13

clearly presented and defined methodically to deliver a smooth learning of the constructed model.

14

3.

15

In this section, the mathematical formulation for this work is described in detailed. Note that the

16

mathematical formulation in this section is divided into two cases; constant feedstock and variable

MATHEMATICAL MODEL FORMULATION


9


Page 10 of 36

1

feedstock. The formulation for constant feedstock can be found in the Associated Content section.

2

Meanwhile, the following subsections present a detailed formulation for variable feedstock with

3

multiperiod optimisation model. Note that italic mathematical notations represent variables in the

4

mathematical model while non-italic notations are fixed parameters.

5

Material Balance

6

Eq. (1) shows the component balance for feedstock i where Fi represents the flowrate of feedstock

7

i which may be sent to potential technology j with flowrate of Fij. The index s represents the

8

season in which a given feedstock i would vary while αs is the fraction of occurrence for season s.  J  (Fi ) s =   B j Fij   j 1 s

9

∀i, ∀s

(1)

∀i, ∀s

(2)

The oil content of feedstock i (Oi) is calculated using Eq. (2)

(Oi )s = (Fi OPi )s 10

where OPi is the oil percentage of feedstock i.

11

In technology j, feedstock i is converted to intermediate product p with conversion Xijp. The total

12

production rate for intermediate product p (Fp) for all technologies j is given in Eq. (3).  I J  ( Fp ) s =   B j Fij X ijp   i 1 j 1 s

∀p, ∀s

(3)

13

Next, intermediate product p can be distributed to potential technology j′ for further

14

processing to produce final product p′. The component balance for intermediate product p is shown

15

in Eq. (4) where Fp represents the flowrate of intermediate product p which may be sent to potential

16

technology j’ with flowrate of Fpj’


10

Page 11 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


 J'  ( Fp ) s =   B j ' Fpj '   j '1 s

∀p, ∀s

(4)

1

Technology j′ then converts the intermediate product p (Fpj) to final product p′ with conversion

2

Xpj’p’. The total production rate for final product p’ (Fp’) for technologies j’ is given in Eq. (5).  P J'  (Fp ' ) s =   B j ' Fpj ' X pj ' p '   p 1 j '1 s

3 4

∀p′, ∀s

(5)

Eqs. (6-7) shows the oil content of intermediate product p (Op) and final product p’ (Op’) respectively I J I J   (O p ) s =  Oi   B j Fij L ijp   B j ' Fij R ijp  i 1 j 1 i 1 j 1  s P J' P J'   (O p ' ) s =  O p   B j Fpj ' L pj ' p '   B j ' Fpj ' R pj ' p '  p 1 j '1 p 1 j '1  s

∀p, ∀s

(6)

∀p’, ∀s

(7)

5

where Lijp and Lpj’p’ represents the percentage oil loss while Rijp and Rpj’p’ represents the oil

6

recovery across technologies j and j’. The oil percentage of intermediate product p (OPp) and final

7

product p’ (OPp’) could then be calculated using Eqs. (8-9). O  (OPp ) s =  p   100% F   p s

∀p, ∀s

(8)

O  (OPp ' ) s =  p '   100% F   p' s

∀p’, ∀s

(9)

8

Despite that only two stages of conversion technologies j and j’ are shown in Figure 2, the

9

formulation can easily be expanded in a repetitive manner for any number of conversion stages to

10

match the case study.


11


Page 12 of 36

1

Utility Balance

2

Utilities u (e.g. steam, electricity and water) are required for material conversion in technologies j

3

and j’. Depending on the technology selected, amount and quality of utilities u consumed, varies

4

accordingly. Total utility u consumption, FuCon and electricity e consumption, EeCon can be

5

calculated with Eqs. (10-11). P J'  I J  ( FuCon ) s =   B j Fij U ujp   B j ' Fpj ' U uj ' p '  p 1 j '1  i 1 j 1 s

∀p′, ∀s

(10)

J' P' J J'  J P  ( EeCon ) s =   B j Fij Yejp   B j ' Fpj ' Yej ' p '   z j Yej   z j ' Yej '  j '1 p '1 j 1 j '1  j 1 p 1 s

∀p′, ∀s

(11)

6

where Fij and Fpj’ are the flowrate of feed i and intermediate product p into technology j and j′, Uujp

7

and Uuj’p’ are the utility requirement per unit flowrate, Yejp and Yej’p’ are the specific electricity

8

consumption per product formation, Yej and Yej’ are the electricity consumption per unit operation

9

while zj and zj’ are the number of equipment unit needed for technologies j and j’ respectively. As

10

shown in Eqs. (12-13), the equipment units needed, zj and zj’ are determined based on the operating

11

capacity  P  ( z j ) s  FjDesign    B j F jp   p 1 s

∀s

(12)

 P'    B j ' Fj' p'  ( z j ' ) s  FjDesign  '    p '1 s

∀s

(13)

12

Design Design where Fj and Fj ' represent the design capacities available to be purchased for technologies

13

j and j’ respectively. Both zj and zj’ are positive integers to reflect the number of units of

14

technologies j and j′ with given design capacity obtained in literature. However, the design

15

capacities used can be revised according to current market availability to provide a produce an up-

16

to-date result.


12

Page 13 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

In this work, it is assumed that due to inevitable losses in transmission and distribution of utility

2

such as electricity and steam, an additional 20% of utility u consumption, FuCon and electricity e

3

consumption EeCon were required, given in Eqs. (14-15).

F E

  1.2 F    1.2 E 

Demand u s

Con u s

∀u, ∀s

(14)

Demand e s

Con e s

∀s

(15)

4

where FuDemand and EeDemand are the total utility demand and electrical demand of the milling process

5

synthesised.

6

Economic Analysis

7

The economic feasibility (EP) of the milling process developed is evaluated via Eq. (16)

EP  GP  CRF  CAPEX H

(16)

8

where GP and CRF and represents the gross profit and capital recovery factor of the process

9

developed respectively. CAPEXH represents capital costs required during high season, αH with the

10

biggest operating capacity. Note that EP shall always be positive and a greater value indicates a

11

greater interest in investing on the system developed. In the event where EP is a negative value,

12

it means the cost is higher than the revenue and it is an infeasible design. GP can be calculated

13

using Eq. (17) I U E   P'   GP  AOT    s    F p ' C p '   Fi C i   FuDemand C u   E eDemand C e   OPEX  s i 1 u 1 e 1   p '1  s 

∀i, ∀p′, ∀u, ∀s

(17)

14

where AOT is the annual operational time, OPEX is the total operating costs, C p ' is the selling

15

price of final product p’, Ci is the cost of feedstock (FFB), while Cu and Ce is the cost of utility and

16

electricity purchased.

17

Eq. (17) is subject to


13




s

Page 14 of 36

1

(18)

s

1

in which the inclusion of αs assessed the GP of POM developed in all s. Each fraction of

2

occurrence represents the time fraction where a season occurs. The sum of these fractions must

3

equal to one as shown in Eq. (18) as the time fraction is obtained by dividing the duration of a

4

season s with the total duration considered.

5

The CRF is used to annualise capital costs by converting its present value into a stream of equal

6

annual payments over a specified operation lifespan t max and discount rate r. The CRF is determined k

7

via Eq. (19). max

CRF 

r (1  r) t k (1  r)

t max k

𝑘 𝜖 𝑗, 𝑗 ′

1

(19)

8

CAPEXH and OPEX are calculated based on the selected technologies j and j′ as well as their

9

equipment unit zj and zj’ required as shown in Eqs. (20-21) J'  J  CAPEX H    z j CC j   z j ' CC j '  j '1  j 1 H



J

J'





j 1

j '1

s

OPEX s    z j OC j   z j ' OC j ' 

∀j, ∀j’

(20)

∀j, ∀j’, ∀s

(21)

10

where OCj and OCj’ are operating costs while CCj and CCj’ are capital costs for technologies j and

11

j′ respectively.

12

Additional Constraints

13

Base on Eq. (1), multiple technology j options are given for selection. In this work, in order to

14

reduce the maintenance and operation costs, only one type of technology j will be selected. Hence,

15

additional constraint Eq. (23) is introduced.


14

Page 15 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


B   0 ,1 j s

 J   Bj  1    j 1  s

∀j, ∀s

(22)

∀j, ∀s

(23)

1

where Bj is a binary variable denoting the existence of technology j in Eq. (1).

2

Similarly, multiple technology j’ options are given for selection in Eq. (4). The introduction of

3

Eq. (25) specifies that only one type of technology j’ will be selected.

B   0 ,1 j' s

 J'    B j'   1    j '1  s

∀j’, ∀s

(24)

∀j’, ∀s

(25)

4

In order to restrict the equipment units required, zj and zj’ for technologies j and j′ in each

5

season s, Eqs. (26-27) are added to ensure the technology selected for all seasons s will remain the

6

same.

(z j )L  (z j )M  (z j )H

(26)

(z j' )L  ( z j' )M  ( z j' )H

(27)

7

To illustrate this optimisation approach, a case study is presented based on information from

8

literature and Malaysian palm oil industry. An optimal POM configuration is synthesised based

9

on the variation in feedstock availability. The developed Mixed-Integer Non-Linear Programming

10

(MINLP) model is solved via LINGO v14, with Global63, with an Intel® Core™ i5 (2 x 3.20 GHz)

11

with 8 GB DDR3 RAM desktop unit.

12

4.

13

As discussed previously, FFBs from the plantation are sent to POM to be converted into products

14

such as CPO and PK, as well as by-products such as PKS, EFB, POME, etc. In Malaysia, a typical

CASE STUDY


15


Page 16 of 36

1

POM has a daily production capacity of 60 t/h FFBs, and operates for 12 hours daily. In this case

2

study, a potential owner in Malaysia is interested to optimise its POM to increase economic

3

performance, EP with maximum oil yield. As mentioned previously, the milling process consists

4

of several unit operations and there exist a variety of technology in the market for each kind of

5

operation. Thus, it is important to screen every alternative configuration to synthesise an optimal

6

milling process. Table 1 shows the given economic parameters considered in this work.

7

Table 1. Economic parameters for case study Annual operational time, AOT Operation lifespan,

4,350 hours/year

max

15 years

tk

Discount rate, r Capital recovery factor, CRF Currency conversion rate

5% 0.0963 1 USD (4 MYR)

8

Utilities Electricity

Water

Steam

Products Feedstock

Crude Palm Oil (CPO)

Palm Oil Mill (POM)

Fresh Fruit Bunch (FFB)

Palm Kernel (PK)

By-products Empty Fruit Bunch (EFB)

Palm Kernel Shell (PKS)

Decanter Cake (DC)

9 10

Material flow

Palm Pressed Fibre (PPF)

Palm Oil Mill Effluent (POME) Energy flow

Figure 3. Material and energy flows in a POM


16

Page 17 of 36

1

Changes in natural factors such as rain fall, haze, sunlight, etc. are usually inevitable in

2

plantation. As a result, FFB quality and availability tend to vary between agricultural seasons and

3

species. Such deviations must be taken into consideration to ensure the robustness in the developed

4

POM configuration to deal with the potential changes. Oil content of FFB feedstock, OPi is given

5

in a range between 22 to 25%

6

22% in this work. Andiappan et al.

7

different seasons, i.e. low, medium and high. Based on national statistics, FFBs obtained from the

8

plantation ranges from 0.97 to 1.57 t/hectare in year 2016 (Figure 4) 65. In this case study, the FFB

9

yield lower than 1.3 t/hectare is taken as low season and that higher than 1.5 t/hectare is taken as

10

high season. Meanwhile, the medium season yield occurs in between 1.3 and 1.5 t/hectare. Thus,

11

the fraction of occurrence can be estimated based on the number of months in which the FFB yield

12

falls in each season (as shown in Table 2). Fraction of occurrence, α value of 0.25, 0.333, and

13

0.417 represents a duration of 3, 4 and 5 months correspondingly.

64

. For conservative measure, the FFB oil content is assumed as 53

showed that the production of CPO can be divided into 3

FFB yield for 2016 in Malaysia 1.6

High season

1.5 FFB yield (t/hectare)

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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1.4 1.3

Low season

1.2 1.1 1.0 0.9

14 15

Figure 4. Fresh fruit bunch yield in Malaysia for 2016

16


17


1

Page 18 of 36

Table 2. Fraction of occurrence for FFB processing throughout a year

Season Low Medium High

Occurrence less than 1.3 t FFB/hectare between 1.3 and 1.5 t FFB/hectare more than 1.5 t FFB/hectare

45 t FFB/h 60 t FFB/h 85 t FFB/h

Fraction of Occurrence αH = 0.25 αM = 0.333 αL = 0.417

2 3

The price of CPO fluctuates throughout the year, depending on the international economic

4

conditions58,66. Hence, prices of the FFBs feedstock and palm products are influenced by CPO

5

market demand price. The price of materials during high and low CPO demands are given in Table

6

3 with the mean of both prices as average price. Note however that utility prices may be assumed

7

to stay constant over the time67,68, given in Table 4.

8

Table 3. Changes in cost of feedstock and products under different market demand

Materials Fresh fruit bunch, FFB Crude palm oil, CPO Palm kernel, PK Pressed empty fruit bunch, PEFB Palm kernel shell, PKS Palm pressed fibre, PPF Decanter cake, DC

High Demand Price (USD/t) 128 645 402 10 50 25 45

Low Demand Price (USD/t) 113 450 375 6 40 20 40

Average Price (USD/t) 121 548 389 8 45 23 43

9 10

Table 4. Cost of utilities Utilities Water Electricity Medium pressure steam, MPS Low pressure steam, LPS

Price 0.55 0.084 17 12

Unit USD/m3 USD/kWh USD/t USD/t

11


18

Page 19 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

All products and by products are assumed to be sold at the POM. Hence, transportation cost and

2

supply chain issue are not considered in this work. A superstructure that incorporate all available

3

technologies for palm oil milling process is developed, shown in Associated Content (Figure S1).

4

A single box presented in the superstructure for technologies j and j’ may consist of more than one

5

Design Design equipment unit zj and zj’ required due to constant design capacity, Fj and Fj ' for each

6

technology (as described in Eqs. (12-13)). Mathematical model was applied to synthesise and

7

optimise a palm oil milling process for quantitative analysis. A list of technologies considered

8

with design capacity, material conversion, utility requirement, oil loss, oil recovery, capital and

9

operating cost in this case study are summarised in the Associated Content (Table S1).

10

To demonstrate the proposed work, three scenarios are presented. In Scenario 1, the

11

optimisation objective is set to maximise EP of the synthesised milling process. For Scenario 2, a

12

robust POM configuration is developed with multi-period consideration that takes into account the

13

variation in feedstock availability with respect to seasonal change. In the last sub-section, the

14

changes in material price were taken into consideration. The impact of such changes on the EP of

15

synthesised process in Scenario 2 is evaluated in this study.

16

Scenario 1 – Single period Consideration

17

In this scenario, a milling process to convert 60 t/h of FFBs into CPO is to be synthesised.

18

The objective is set to maximise economic performance, EP. It is assumed the mill will have an

19

operation lifespan, t k of 15 years. In this scenario, the average material prices shown in Table 3

20

are used to evaluate the economic performance of the milling process. Meanwhile, the utility

21

prices are given in Table 4. The model is optimised using the objective in Eq. (28), subject to the

22

constraints given in the Associated Content (Eqs. (S1-S24)). The optimisation problem consists

max


19


Page 20 of 36

1

of 297 continuous variables with 52 nonlinear variables, 48 integer variables and 285 constraints.

2

Negligible computational time (16 seconds) is required to achieve the global solution for the model

3

developed. Maximise EP

(Eq.28)

4

The optimised POM configuration is shown in Figure 6. For comparison purpose, a conventional

5

milling process based on current POM design in Malaysia is shown in Figure 7. Table S2

6

summarised the economic parameters for the flowsheet synthesised as compared to the values

7

estimated using data obtained in the industry. An increment by 50.7% in EP value for the optimal

8

design (4.29 Million USD/y) as compared to the conventional configurations (2.91 Million USD/y)

9

clearly shows that the flowsheet synthesised is the better choice to invest on. Note however that

10

an additional 18.2% and 9.7% of capital investment (1.24 Million USD) and operating cost (0.1

11

Million USD/y) are required for the optimal configuration. Besides, higher electricity demand (by

12

11.9%) is required to operate the optimum design (shown in Table S3). This is due to the selection

13

of technologies with lower oil loss and higher conversion rate, i.e. tilted steriliser, double pressing,

14

Rolek nut cracker, etc. in the optimal configuration. Hence, the overall costs for the optimum

15

configuration are higher.

16

On the other hand, much higher GP value (5.42 Million USD/y) was reported for the

17

optimal design as compared to the conventional design (3.56 Million USD/y). This leads to an

18

increment of 52.2% in GP value generated as a greater POM net output is produced, given in Table

19

S3. As shown, 12.4 t/h of CPO is produced in the optimal configuration. On the other hand, only

20

11.9 t/h of CPO is produced in the conventional configuration, with the same amount of FFB

21

feedstock (60 t/h).

Selection of tilted steriliser, double pressing and oil pressing screw


20

Page 21 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

technologies reduce the oil content in POME, EFB and PPF by 13.8, 60 and 54.1%, respectively

2

(see Table S4). The total oil lost in the entire milling process is reduced by 3.9%, increasing the

3

CPO yield by 4.2% in return. Meanwhile, PK production is raised by 7.1% in the optimal

4

configuration (4.5 t/h) with the selection of Rolek nut cracker technology as compared to the

5

conventional configuration (4.2 t/h). To determine the effectiveness of the investment made, a

6

cost-benefit analysis (CB) for each design is performed using Eq. (29)

CB 

GP CAPEX  CRF  OPEX

(Eq.29)

7

The result shows that it is 35.1% more efficient to invest on the optimal design as compared

8

to the conventional design with CB values of 2.85 and 2.11 respectively. It is worth mentioning

9

that 18.7% cut in utility water requirement is reported in the optimal design. This means that the

10

dependency of POM operation on water supply could be reduced significantly, decreasing the risk

11

of operational failure in the event of water shortage during drought season69. As mentioned earlier,

12

POME is a heavily contaminated wastewater which must be treated before being discharged to the

13

watercourse. Installation of the optimal design reduced the POME generated by 6.5%, i.e. to 41.7

14

t/h (from 44.6 t/h in the conventional design). In this respect, it could also lead to a lower treatment

15

cost as a smaller wastewater treatment plant is needed.


21

Industrial & Engineering Chemistry Research 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Pressed empty fruit bunch (PEFB)

Recovered Oil

11.3 t/h Empty fruit bunch (EFB)

13 t/h

EFB Pressing

1.7 t/h

54 t/h

Sterilised fruit bunch

41 t/h

Low Pressure Steam 15 t/h

25.6 t/h

Steam Injection Digester

Digested fruitlet 42.7 t/h

60 t/h

Fresh Fruit Bunch (FFB)

13.8 t/h

Palm oil mill effluent (POME)

Organic phase 14.2 t/h

Liquid product

25.7 t/h


Organic phase

Centrifugal Purifier

Clarified oil 12.6 t/h

Vacuum Dryer 12.4 t/h

2.2 t/h

Crude palm oil (CPO)


Solid product

Depricarper 10.1 t/h

Three-Phase Decanter 0.6 t/h

Double Press 17.1 t/h

Tilted Steriliser

29.6 t/h

17.8 t/h

Vertical Clarifier

5.3 t/h

Sterilised fruitlet

Aqueous phase

Water

Recovered Oil

Decanter cake (DC)

3.3 t/h

0.4 t/h

Low Pressure Steam

Rotating Thresher

Page 22 of 36

Palm nut

Rolek Nut Cracker

7 t/h Cracked mixture 10 t/h

Palm pressed fibre (PPF)

Four-Stage Winnowing Column

4.5 t/h

Palm kernel (PK)

3.5 t/h

Palm kernel shell (PKS)

2 t/h


Figure 5. Optimum Palm Oil Mill configuration

22 ACS Paragon Plus Environment

Page 23 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


Palm oil mill effluent (POME) Recovered Oil

13.8 t/h

Fresh Fruit Bunch (FFB) 60 t/h

Horizontal Low Pressure Steriliser 52.2 t/h

15 t/h

39.7 t/h


Digested fruitlet

41.2 t/h


7.3 t/h

Organic phase 12.5 t/h

Liquid product

Solid product

Rotating Drum Separator 10 t/h

Organic phase


Decanter cake (DC)

24.8 t/h


Clarified oil 12.3 t/h



Water 5.2 t/h

5.2 t/h

Clay Bath 7.7 t/h

Cracked mixture 9.5 t/h

Vacuum Dryer 11.9 t/h

0.8 t/h

Palm nut

Nut Cracker

Three-Phase Decanter 0.6 t/h

Screw Press 17.3 t/h

12.5 t/h

Empty fruit bunches (EFB)

28.6 t/h

Vertical Clarifier 23.9 t/h

Sterilised fruitlet

Aqueous phase

16.7 t/h

0.5 t/h

5.2 t/h


Rotating Thresher

Low Pressure Steam

Water

3.2 t/h

3.3 t/h

Cracked nut

Palm kernel shell (PKS)

4.4 t/h

Wet kernel

Air Cyclone

Silo Dryer

1.8 t/h

4.2 t/h


Palm kernel (PK)

Figure 6. Conventional Palm Oil Mill configuration

23 ACS Paragon Plus Environment


Page 24 of 36

Scenario 2 – Multi-period Consideration In this scenario, it is desired that the POM to be able in handling different FFBs feedstock availability as shown in Table 2. Similarly, the average material prices and fixed utility prices given in Table 3 and 4 were used. The model is optimised using the objective in Eq. (28), subject to the constraints in Eqs. (1-27). The model formulated for this study consists of 892 continuous variables with 156 nonlinear variables, 144 integer variables and 908 constraints. An average computational time of 10,477 seconds (3 hours) is required to achieve a global solution. The optimum result in this scenario produced the same POM configuration as in Scenario 1 (Figure 6) and the detailed results are summarised in Tables S5-S7. The maximum EP was determined as 4.19 Million USD/y with an average GP value of 5.32 Million USD/y generated (GP value of 3.86, 5.42 and 7.63 Million USD/y for low, medium and high season respectively). Capital investment of 11.71 Million USD is required to build the robust POM configuration that can cater for variation in FFBs feedstock availability. Due to seasonal change in FFB availability, CPO will be produced at 9.3, 12.4 and 17.6 t/h, while 3.4, 4.5 and 6.4 t/h of PK will be generated during low, medium and high seasons, respectively. The breakdown for materials flow (e.g. PKS, DC, PPF, PEFB and POME) and utilities (e.g. LPS, water and electricity) are presented in Table S6 for each season. As presented in Table S7, a total of 31 processing units will be operated during the high season to extract CPO from 85 t/h FFBs. However, the number of operating equipment reduces to 24 and 19 during medium and low seasons correspondingly. This means that not all equipment units will be utilised throughout the year. Hence, lesser service and maintenance will be required during low and medium seasons. Thus, a lower OPEX is expected during these two seasons as compared to high season.


24

Page 25 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Sensitivity Analysis – Variation in Product Prices In this sub-section, a sensitivity analysis is performed on the important parameters (material prices) to evaluate the changes of EP based on the synthesised milling process in Scenario 2. As shown in Table 3, the price of FFB and palm-based products changes based on the market demands (high and low demands). Therefore, it is important to analyse the economic performance of the synthesised milling process which consideration of the changes of product prices. In order to performance the sensitivity analysis, the proposed optimisation model (Eqs. (16-17)) were resolved based on the given low and high demands product prices given in Table 3. Besides, additional equation of payback period, PP as shown in Eq. (30) is also included. Note that PP were calculated to identify the time required to return the initial investment made before making a profit by investing on this mill.

    1  ln  1 - ( CAPEX  r )    GP  PP  

(Eq.30)

ln1  r 

The optimised results under these variations were presented in Table S8. In an extreme case where CPO is under high demand in the market for a year, the optimum POM configuration developed generated an EP up to 8.14 Million USD/y (GP value 9.27 Million USD/y). On the other extreme end, when CPO is under low demand in the market, the EP generated greatly reduces to 0.37 Million/y (GP value 1.50 Million USD/y). Note that the positive EP under both extreme cases proved the flowsheet synthesised is capable to manage such variation in products and feedstock prices without losing money. In this context, it is also noted that the CAPEX and OPEX remains the same as Table S5 as there is no change the process configuration. Meanwhile, the calculated PP range between 1.34 to 10.12 years, depending on the market demand prices.


25


5.

Page 26 of 36

CONCLUSION

In this work, a systematic approach for synthesis and optimisation of palm oil milling process is presented. A developed model simplifies the overall formulation without losing the insights for effective design, synthesis and integration of the process. Technology selection and flowsheet synthesis are performed simultaneously in a systematic manner with the material and energy flows for the process presented. It is shown that an optimal milling process generates a higher EP as compared to the conventional process practiced in the industry. In addition to that, the higher oil yield and lower POME production with fixed FFB supply further attracts the interest of potential owners to invest on the flowsheet developed. The model also considers seasonal variations in feedstock supply via multi-period optimisation approach to synthesise a robust POM configuration. Besides, the EP generated varies according to the feed and products’ market price. It is worth mentioning that the proposed approach can be easily revised and re-formulated to handle the possible uncertainties arising from technologies advancement and market prices, which will be reflected as prospects for future works. 6.

ASSOCIATED CONTENT

Supporting Information A detailed mathematical model derivation for constant feedstock and information on each technology such as the economic data (capital and operating costs), design capacity, material conversion, utility requirement (electricity, steam and water), oil loss and oil recovery are provided in the Supporting Document Sheet. The complete superstructure for palm oil milling process and comprehensive results for each scenario presented are attached at the end of the document.


26

Page 27 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


7.

AUTHOR INFORMATION

Corresponding Author *Email: [email protected] (DKS Ng). Telephone: +6 (03) 8924 8606. Fax: +6 (03) 8924 8017. Emails: [email protected] (SZY Foong); [email protected] (YL Lam); [email protected] (Viknesh Andiappan); [email protected] (DCY Foo). Notes The authors declare no competing financial interest. 8.

ACKNOWLEDGMENT

The financial support from the Ministry of Higher Education, Malaysia through LRGS Grant (LRGS UPM Vot 5526100 and LRGS/2013/UKM-UNMC/PT/05) is gratefully acknowledged. Industrial data provided by Havy’s Oil Mill is also accredited in building a realistic case study in this work. 9.

NOMENCLATURE

Abbreviation AOT

Annual Operational Time

CPO

Crude Palm Oil

CRF

Capital Recovery Factor

DOE

Department of Environment

EFB

Empty Fruit Bunch

LPS

Low Pressure Steam

MINLP

Mixed-Integer Non-Linear Programming


27


MPOB

Malaysian Palm Oil Board

MPS

Medium Pressure Steam

MT

Metric Tonne

DC

Decanter Cake

PEFB

Pressed Empty Fruit Bunch

PK

Palm Kernel

PKS

Palm Kernel Shell

POM

Palm Oil Mill

POME

Palm Oil Mill Effluent

PPF

Palm Pressed Fibre

PSE

Process System Engineering

Page 28 of 36

Sets e

Index for electricity

i

Index for resources

j

Index for technologies at level j

j’

Index for technologies at level j’

p

Index for intermediate products

p'

Index for final products

s

Index for scenarios

u

Index for utility

Variables

EeCon

Total electricity consumption

FuCon

Total flowrate of utilities

EeDemand

Total electricity demand

FuDemand

Total utility demand

F jDesign

Design capacity of technology j

F jDesign '

Design capacity of technology j’


28

Page 29 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


CAPEX

Total capital cost

CB

Cost-benefit

EP

Economic performance

Fij

Flowrate of resources i to technology j

Fj’p’

Flowrate of final product p’ from technology j’

Fjp

Flowrate of intermediate product p from technology j

Fp

Flowrate of intermediate products p

Fp’

Flowrate of final products p’

Fpj’

Flowrate of intermediate products p to technology j’

GP

Total gross profit

Oi

Oil content of resources i

Op

Oil content of intermediate products p

Op’

Oil content of final products p’

OPEX

Total operating cost

OPp

Oil percentage of intermediate products p

OPp’

Oil percentage of final products p’

PP

Payback period

zj

Number of units of technology j selected

zj’

Number of units of technology j’ selected

Parameters

t max k

Operational lifespan

CCj

Capital cost for technology j

CCj’

Capital cost for technology j’

Ci

Cost of resources i

Fi

Flowrate of resources i

Lijp

Percentage oil loss technology j

Lpj’p’

Percentage oil loss technology j’

OCj

Operating cost for technology j

OCj’

Operating cost for technology j’

OPi

Oil percentage of resources i

r

Discount rate


29


Rijp

Percentage oil recovery technology j

Rpj’p’

Percentage oil recovery technology j’

Uuj’p’

Utility specification of final product p’

Uujp

Utility specification of intermediate product p

Xijp

Component mass conversion of resources i

Xpj’p’

Component mass conversion of intermediate product p

Yej

Electricity consumption rate per unit of technology j

Yej’

Electricity consumption rate per unit of technology j’

Yej’p’

Electricity consumption rate of technology j’ per unit of final product p’ produced

Yejp

Electricity consumption rate of technology j per unit of intermediate product p produced

αs

Fraction of occurrence for scenario s

10.

Page 30 of 36

REFERENCES

(1)

Mielke, T. Global Supply, Demand and Price Outlook for Vegetable Oils as well as for Palm Oil. ISTA Mielke GmbH, OIL WORLD, Hambg. 2017.

(2)

The American Soybean Association (ASA). International: World Vegetable Oil Consumption http://soystats.com/international-world-vegetable-oil-consumption/ (accessed Mar 29, 2017).

(3)

Malaysian Palm Oil Board (MPOB). Production of Crude Palm Oil 2016 http://bepi.mpob.gov.my/index.php/en/statistics/production/168-production-2016/746production-of-crude-oil-palm-2016.html (accessed Mar 29, 2017).

(4)

Poku, K. Small-Scale Palm Oil Processing in Africa; Food and Agriculture Organization of the United Nations, 2002.

(5)

Kandiah, S.; Basiron, Y.; Suki, A.; Taha, R. M.; Tan, Y. H. Continuous Sterilization: The New Paradigm for Modernizing Palm Oil Milling. J. Oil Palm Res. 2006, 144–152.

(6)

Loh, T. K. Tilting Sterilizer. Palm Oil Eng. Bull. 2010, 94, 29–42.

(7)

EnergyWise. Selection of Sterilizer Technology for Energy Efficient Operation of Palm Oil Mills – EnergyWise http://rank.com.my/energywise/?p=310#sthash.9s0KT99m.dpbs (accessed Jul 17, 2017).

(8)

Noerhidajat; Yunus, R.; Zurina, Z. A.; Syafiie, S.; Ramanaidu, V.; Rashid, U. Effect of High Pressurized Sterilization on Oil Palm Fruit Digestion Operation. Int. Food Res. J. 2016, 23 (1), 129–134.

(9)

Department of Industrial Works. Environmental Management Guideline for the Palm Oil Industry. Environ. Advis. Assist. Ind. 1997.

(10)

Palm Oil Research Institute of Malaysia (PORIM). Oil Extraction. In Palm Oil Factory


30

Page 31 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Process Handbook, Part 1: General Description of The Palm Oil Milling Process; Shah Alam, 1985; pp 39–55. (11)

Palm Oil Mill Consultants and Training. Press Station Operations http://palmoilmill.co.nz/index.php/processing-knowledge/10-press-station-operations (accessed Apr 10, 2017).

(12)

Harun, M. Y.; Che Yunus, M. A.; Morad, N. A.; Ismail, M. H. S. An Industry Survey of the Screw Press System in Palm Oil Mills: Operational Data and Malfunction Issues. Eng. Fail. Anal. 2015, 54, 146–149 DOI: 10.1016/j.engfailanal.2015.04.003.

(13)

Obincowelds Construction Company Ltd. Palm Nut Fibre http://obincoweldconst.blogspot.my/2015/05/palm-fruit-fibre-separator.html Apr 11, 2017).

(14)

HUATAI Cereals and Oils Machinery. Palm Kernel Recovery Station http://www.palmoilmachine.com/Palm_Kernel_Recovery_Station_62.html (accessed Apr 11, 2017).

(15)

Hartley, C. W. S. The Oil Palm (Elaeis guineensis Jacq.), 3rd ed.; Harlow, Essex, England, Longman Scientific & Technical, 1988.

(16)

Rohaya, M. H.; Nasrin, A. B.; Mohd Basri, W.; Choo, Y. M.; Ridzuan, R.; Ma, A. N.; Ravi, N. M. Chemistry, Processing Technology and Bio Energy | Palm Oil. In Proceedings of the Malaysian Palm Oil Board (MPOB) International Palm Oil Congress; Kuala Lumpur, Malaysia, 2009; pp 9–12.

(17)

Rohaya, M. H.; Ridzuan, R.; Che Rahmat, C. M.; Choo, Y. M.; Nasrin, A. B.; Nu’man, A. H. Dry Separation of Palm Kernel and Palm Shell Using a Novel Five-Stage Winnowing Column System. Technologies 2016, 4 (13), 1–15 DOI: 10.3390/technologies4020013.

(18)

Department of Environment (DOE), Ministry of Science, Technology and The Environment, M. The Extraction Process for Crude Palm Oil and Sources of Pollution. In Industrial Process and the Environment- Crude Palm Oil Industry; 1999; pp 11–21.

(19)

Jorgensen, H. K.; Singh, G. An Introduction of The Decanter - Drier System in The Clarification Station for Crude Oil and Sludge Treatment. Semin. Malaysian Dep. Environ. 1980.

(20)

Alfa Laval. PANX Decanters for Crude Palm Oil: High-Performance Three-Phase Decanters http://www.alfalaval.com/products/separation/centrifugalseparators/decanters/PANX/ (accessed Apr 3, 2017).

(21)

Husain, Z.; Zainal, Z. A.; Abdullah, M. Z. Analysis of Biomass-Residue-Based Cogeneration System in Palm Oil Mills. Biomass and Bioenergy 2003, 24 (2), 117–124 DOI: 10.1016/S0961-9534(02)00101-0.

(22)

Raj, N. T.; Iniyan, S.; Goic, R. A Review of Renewable Energy Based Cogeneration Technologies. Renew. Sustain. Energy Rev. 2011, 15 (8), 3640–3648 DOI: 10.1016/j.rser.2011.06.003.

(23)

Alimon, A. R.; Wan Zahari, W. M. Recent Advances in Utilization of Oil Palm By-Products as Animal Feed. In International Conference on Livestock Production and Veterinary Technology; 2012; pp 211–219.

Separator (accessed


31


Page 32 of 36

(24)

Gafar, A. A.; Alimon, A. R.; Sazili, A. Q.; Man, Y. C.; Abubakr, A. R. Effect of Varying Levels of Palm Oil Decanter Cake on Feed Intake, Growth Performance and Carcass Characteristics of Kacang Goats. IOSR J. Agric. Vet. Sci. 2013, 3 (4), 2319–2372 DOI: 10.9790/2380-0342429.

(25)

Ahmed, Y.; Yaakob, Z.; Akhtar, P.; Sopian, K. Production of Biogas and Performance Evaluation of Existing Treatment Processes in Palm Oil Mill Effluent (POME). Renew. Sustain. Energy Rev. 2015, 42, 1260–1278 DOI: 10.1016/J.RSER.2014.10.073.

(26)

Najafpour, G. D.; Zinatizadeh, A. A. L.; Mohamed, A. R.; Hasnain Isa, M.; Nasrollahzadeh, H. High-rate Anaerobic Digestion of Palm Oil Mill Effluent in an Upflow Anaerobic Sludge-Fixed Film Bioreactor. Process Biochem. 2006, 41 (2), 370–379 DOI: 10.1016/J.PROCBIO.2005.06.031.

(27)

Hassan, M. A.; Yacob, S.; Shirai, Y.; Hung, Y. T. Treatment of Palm Oil Wastewaters. In Waste Treatment in the Food Processing Industry; Wang, L. K., Hung, Y.-T., Lo, H. H., Yapijakis, C., Eds.; CRC Press, 2005; pp 101–117.

(28)

Malaysia Palm Oil Board (MPOB). Oil Palm & The Environment http://www.mpob.gov.my/en/palm-info/environment/520-achievements (accessed Dec 4, 2017).

(29)

Poh, P. E.; Chong, M. F. Development of Anaerobic Digestion Methods for Palm Oil Mill Effluent (POME) Treatment. Bioresour. Technol. 2009, 100 (1), 1–9 DOI: 10.1016/j.biortech.2008.06.022.

(30)

Chaisri, R.; Boonsawang, P.; Prasertsan, P.; Chaiprapat, S. Effect of Organic Loading Rate on Methane and Volatile Fatty Acids Productions from Anaerobic Treatment of Palm Oil Mill Effluent in UASB and UFAF Reactors. J. Sci. Educ. Technol. 2007, 29 (2), 311–323.

(31)

Gomez, J. C.; Mokhtar, M. N.; Sulaiman, A.; Baharuddin, A. S.; Busu, Z. Recovery of Residual Crude Palm Oil from the Empty Fruit Bunch Spikelets Using Environmentally Friendly Processes. Sep. Sci. Technol. 2015, 50 (11), 1677–1683 DOI: 10.1080/01496395.2014.994781.

(32)

Subramaniam, V. Residual Oil Recovery System: Crude Palm Oil Recovery from Pressed Mesocarp Fibre in the Palm Oil Mill. In International Exhibition and Conference on Water Technologies, Environmental Technologies, and Renewable Energy; Mumbai, India, 2013.

(33)

Cock, J.; Donough, C. R.; Oberthür, T.; Indrasuara, K.; Rahmadsyah; Gatot, A. R.; Dolong, T. Increasing Palm Oil Yields by Measuring Oil Recovery Efficiency from the Fields to the Mills. In International Oil Palm Conference (IOPC); International Oil Palm Conference (IOPC), 2014.

(34)

Hendry, J. E.; Rudd, D. F.; Seader, J. D. Synthesis in the Design of Chemical Processes. AIChE J. 1973, 19 (1), 1–15 DOI: 10.1002/aic.690190103.

(35)

Rudd, D. F.; Powers, G. J.; Siirola, J. J. Process Synthesis; Prentice-Hall, 1973.

(36)

Gandikota, M. S.; Davis, J. F. An Expert System Framework for the Preliminary Design of Process Flowsheets. In Knowledge Based Computer Systems; Springer-Verlag: Berlin/Heidelberg, 1990; pp 88–104.

(37)

Sargent, R. Process Systems Engineering: A Retrospective View with Questions for the Future. Comput. Chem. Eng. 2005, 29 (6), 1237–1241 DOI:


32

Page 33 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


10.1016/j.compchemeng.2005.02.008. (38)

Andiappan, V. State-Of-The-Art Review of Mathematical Optimisation Approaches for Synthesis of Energy Systems. Process Integr. Optim. Sustain. 2017 DOI: 10.1007/s41660017-0013-2.

(39)

Frangopoulos, C. A.; Spakovsky, M. R. von; Sciubba, E. A Brief Review of Methods for the Design and Synthesis Optimization of Energy Systems. Int. J. Thermodyn. 2002, 5 (4), 151–160 DOI: 10.5541/IJOT.1034000097.

(40)

Freitas, I. S. F.; Costa, C. A. V.; Boaventura, R. A. R. Conceptual Design of Industrial Wastewater Treatment Processes: Primary Treatment. Comput. Chem. Eng. 2000, 24 (2–7), 1725–1730 DOI: 10.1016/S0098-1354(00)00450-6.

(41)

Manninen, J.; Zhu, X. X. Level-By-Level Flowsheet Synthesis Methodology for Thermal System Design. AIChE J. 2001, 47 (1), 142–159 DOI: 10.1002/aic.690470114.

(42)

Sánchez, Ó. J.; Cardona, C. A. Conceptual Design of Cost-Effective and EnvironmentallyFriendly Configurations for Fuel Ethanol Production from Sugarcane by Knowledge-Based Process Synthesis. Bioresour. Technol. 2012, 104, 305–314 DOI: 10.1016/j.biortech.2011.08.125.

(43)

Ng, D.; Pham, V.; El-Halwagi, M.; Jiménez-Gutiérrez, A.; Spriggs, H. A Hierarchical Approach to the Synthesis and Analysis of Integrated Biorefineries. In Design for Energy and the Environment; CRC Press, 2009; pp 425–432.

(44)

Smith, R. Conceptual Chemical Process Design for Sustainability. In Sustainability in the Design, Synthesis and Analysis of Chemical Engineering Processes; Elsevier, 2016; pp 67– 85.

(45)

Grossmann, I. E.; Santibanez, J. Applications of Mixed-Integer Linear Programming in Process Synthesis. Comput. Chem. Eng. 1980, 4 (4), 205–214 DOI: 10.1016/00981354(80)85001-0.

(46)

Biegler, L. T.; Grossmann, I. E.; Westerberg, A. W. Systematic Methods for Chemical Process Design; Prentice Hall: Old Tappan, NJ (United States), 1997.

(47)

Karuppiah, R.; Grossmann, I. E. Global Optimization for the Synthesis of Integrated Water Systems in Chemical Processes. Comput. Chem. Eng. 2006, 30 (4), 650–673 DOI: 10.1016/j.compchemeng.2005.11.005.

(48)

Yoshida, S.; Ito, K.; Yokoyama, R. Sensitivity Analysis in Structure Optimization of Energy Supply Systems for a Hospital. Energy Convers. Manag. 2007, 48 (11), 2836–2843 DOI: 10.1016/J.ENCONMAN.2007.06.045.

(49)

Lozano, M. A.; Carvalho, M.; Serra, L. M. Allocation of Economic Costs in Trigeneration Systems at Variable Load Conditions. Energy Build. 2011, 43 (10), 2869–2881 DOI: 10.1016/j.enbuild.2011.07.002.

(50)

Kasivisvanathan, H.; Ng, R. T. L.; Tay, D. H. S.; Ng, D. K. S. Fuzzy Optimisation for Retrofitting a Palm Oil Mill into a Sustainable Palm Oil-Based Integrated Biorefinery. Chem. Eng. J. 2012, 200–202, 694–709 DOI: 10.1016/j.cej.2012.05.113.

(51)

Ng, R. T. L.; Tay, D. H. S.; Ng, D. K. S. Simultaneous Process Synthesis, Heat and Power Integration in a Sustainable Integrated Biorefinery. Energy & Fuels 2012, 26 (12), 7316–


33


Page 34 of 36

7330 DOI: 10.1021/ef301283c. (52)

Ng, R. T. L.; Ng, D. K. S.; Tan, R. R.; El-Halwagi, M. M. Disjunctive Fuzzy Optimisation for Planning and Synthesis of Bioenergy-Based Industrial Symbiosis System. J. Environ. Chem. Eng. 2014, 2 (1), 652–664 DOI: 10.1016/j.jece.2013.11.003.

(53)

Andiappan, V.; Ng, D. K. S.; Bandyopadhyay, S. Synthesis of Biomass-Based Trigeneration Systems with Uncertainties. Ind. Eng. Chem. Res. 2014, 53 (46), 18016– 18028 DOI: 10.1021/ie502852v.

(54)

Andiappan, V.; Tan, R. R.; Aviso, K. B.; Ng, D. K. S. Synthesis and Optimisation of Biomass-Based Tri-Generation Systems with Reliability Aspects. Energy 2015, 89, 803– 818 DOI: 10.1016/J.ENERGY.2015.05.138.

(55)

Othman, M. N.; Lim, J. S.; Theo, W. L.; Hashim, H.; Ho, W. S. Optimisation and Targeting of Supply-Demand of Biogas System Through Gas System Cascade Analysis (GASCA) Framework. J. Clean. Prod. 2017, 146, 101–115 DOI: 10.1016/j.jclepro.2016.06.057.

(56)

Subrahmanyam, S.; Pekny, J. F.; Reklaitis, G. V. Design of Batch Chemical Plants Under Market Uncertainty. Ind. Eng. Chem. Res. 1994, 33 (11), 2688–2701 DOI: 10.1021/ie00035a019.

(57)

Mcmanus, H.; Hastings, D. A Framework for Understanding Uncertainty and its Mitigation and Exploitation in Complex Systems. In Fifteenth Annual International Symposium of the International Council on Systems Engineering (INCOSE); Rochester, New York, 2005; pp 1–20.

(58)

Malaysian Palm Oil Board (MPOB). Prices of Palm Products; Monthly http://bepi.mpob.gov.my/index.php/en/statistics/price/monthly.html (accessed Jun 16, 2017).

(59)

Li, C.-Z.; Shi, Y.-M.; Liu, S.; Zheng, Z.; Liu, Y. Uncertain Programming of Building Cooling Heating and Power (BCHP) System based on Monte-Carlo Method. Energy Build. 2010, 42 (9), 1369–1375 DOI: 10.1016/j.enbuild.2010.03.005.

(60)

Fazlollahi, S.; Maréchal, F. Multi-Objective, Multi-Period Optimization of Biomass Conversion Technologies using Evolutionary Algorithms and Mixed Integer Linear Programming (MILP). Appl. Therm. Eng. 2013, 50 (2), 1504–1513 DOI: 10.1016/j.applthermaleng.2011.11.035.

(61)

Ahmad, M. I.; Zhang, N.; Jobson, M. Modelling and Optimisation for Design of Hydrogen Networks for Multi-Period Operation. J. Clean. Prod. 2010, 18 (9), 889–899 DOI: 10.1016/j.jclepro.2010.01.003.

(62)

Hwangbo, S.; Lee, I.-B.; Han, J. Multi-Period Stochastic Mathematical Model for The Optimal Design of Integrated Utility and Hydrogen Supply Network Under Uncertainty in Raw Material Prices. Energy 2016, 114, 418–430 DOI: 10.1016/j.energy.2016.08.003.

(63)

LINDO Systems Inc. LINGO The Modeling Language and Optimizer. LINDO Systems Inc.: Chicago 2016.

(64)

Malaysian Palm Oil Board (MPOB). About Palm http://www.palmoilworld.org/about_palmoil.html (accessed Aug 26, 2017).

(65)

Malaysian

Palm

Oil

Board

(MPOB).

Yield

Oil 2016


34

Page 35 of 36 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


http://bepi.mpob.gov.my/index.php/en/statistics/yield/172-yield-2016/766-yield2016.html (accessed Oct 23, 2017). (66)

Ng, D. K. S.; Ng, R. T. L. Applications of Process System Engineering in Palm-Based Biomass Processing Industry. Curr. Opin. Chem. Eng. 2013, 2 (4), 448–454 DOI: 10.1016/j.coche.2013.09.005.

(67)

Ng, R. T. L.; Ng, D. K. S.; Tan, R. R. Systematic Approach for Synthesis of Integrated Palm Oil Processing Complex. Part 2: Multiple Owners. Ind. Eng. Chem. Res. 2013, 52 (30), 10221–10235 DOI: 10.1021/ie400846g.

(68)

Ng, R. T. L.; Ng, D. K. S. Systematic Approach for Synthesis of Integrated Palm Oil Processing Complex. Part 1: Single Owner. Ind. Eng. Chem. Res. 2013, 52 (30), 10206– 10220 DOI: 10.1021/ie302926q.

(69)

New Straits Times. Preventing a water crisis | New Straits Times | Malaysia General Business Sports and Lifestyle News https://www.nst.com.my/news/2016/09/170607/preventing-water-crisis (accessed Jan 16, 2018).


35


Low Pressure Steam

Tilted Steriliser Fresh Fruit Bunch (FFB)

Aqueous phase Recovered Oil

Low Pressure Steam

Rotating Thresher


Oil Pressing

Sterilised fruitlet


Decanter cake (DC)

Recovered Oil

Pressed empty fruit bunch (PEFB) Empty fruit bunch (EFB)

Page 36 of 36

Water

Vertical Clarifier Digested fruitlet

Organic phase

Three-Phase Decanter Organic phase



Clarified oil

Vacuum Dryer

Liquid product Palm oil mill effluent (POME)

Double Press


Solid product Palm oil mill effluent (POME)


Depricarper Palm nut

Rolek Nut Cracker

Cracked mixture

Four-Stage Winnowing Column

Palm kernel (PK) Palm kernel shell (PKS) Palm pressed fibre (PPF)

Figure 7. For Table of Contents Only


36

A Systematic Approach for the Synthesis and Optimization of Palm Oil

Recommend Documents