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OPTIMAL MULTI-SITE RESOURCE ALLOCATION AND UTILITY PLANNING FOR INTEGRATED RICE MILL COMPLEX Jeng Shiun Lim, Zainuddin Abdul Manan, Hashim Haslenda, and Sharifah Rafidah Wan Alwi Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/ie302884t • Publication Date (Web): 01 Feb 2013 Downloaded from http://pubs.acs.org on February 14, 2013

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Industrial & Engineering Chemistry Research

OPTIMAL MULTI-SITE RESOURCE ALLOCATION AND UTILITY PLANNING FOR INTEGRATED RICE MILL COMPLEX LIM JENG SHIUN, ZAINUDDIN ABDUL MANAN*, HASLENDA HASHIM, SHARIFAH RAFIDAH WAN ALWI. Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia *Corresponding author, Tel.: +607-5535478, Fax: +607-5581463 E-mail: [email protected]

Abstract A rice mill produces rice and a variety of by-products, including broken rice, rice bran and rice husk. Rice husk, in particular, has been widely utilised as a source of fuel to generate heat and electricity. Efficient conversion of the by-products into value-added products via an integrated, resource-efficient (IRE) rice mill complex provides a strategic option for rice mills to remain competitive. However, in the face of increasing global demand for rice, optimal resource allocation has become a major challenge for rice enterprises, which operate multiple rice mills with different technologies and capacities. This work proposes a Multi-Site Resource Efficient (MSIRE) System, which is a model-based generic framework for the efficient management of the aforementioned complex, interacting issues. The MSIRE system has been proposed to i) plan resource allocation and utility network at each site, ii) screen and select the most suitable technology, iii) determine the logistic network and iv) plan capacity expansion with the objective of maximising overall profit. The eight different scenarios analysed with the MSIRE framework demonstrate the tool’s powerful capability to guide planners towards a profitable rice mill business. Due to its generic element, the MSIRE can also be applied to other types of industry.

Keywords: cogeneration, downstream, resource allocation, rice mill, rice husk, processes, utility planning

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1.

Introduction Rice is an important staple food for almost half of the world’s population 1. It is

estimated that between 2000 and 2050, the global rice consumption will increase by 35% 2. To cope with the increasing demand, rice enterprises are expected to expand their current rice processing capacities. The expected increase in the production costs due to the increase in capacity may require rice enterprises to be resource efficient and to generate additional revenues by converting their rice mills’ by-products into valueadded products and sources of energy. These goals can be achieved by transforming the current rice mills into integrated, resource-efficient (IRE) rice mill complexes. An IRE rice mill complex integrates its various processes to efficiently utilise its resources and to produce fuel, electricity and value-added products. As shown in Fig. 1, the supply chain network of a rice enterprise with an IRE rice mill complex consists of multi-stage and multi-site elements. The stages include paddy fields, process sites and distribution centres. The process sites can consist of sub-stages or process units, which include the drying facility, the milling facility and other downstream processes. Conversely, the site consists of clusters of stages at different geographical locations. The supply chain begins with the transportation of wet paddy from the paddy fields to the process site. The wet paddy is then dried in the drying facility at the process site. The dried paddy is then fed to a rice mill where it is manufactured into full head rice, broken rice, rice bran and rice husk. The full head rice and broken rice are mixed in a rice packaging plant to produce graded rice of different compositions. Downstream of the rice mill, the by-products are processed into value-added products and utilised as sources of energy. For instance, rice husk is utilised as a biomass fuel in the cogeneration system, an inherently cleaner production as compared to fossil fuelbased energy source 3. Such utilisation prevents the negative environmental and social impacts associated with the conventional way of disposing rice husk 4. The byproducts are then transferred to the distribution centres at strategic locations. Note that an enterprise can operate a few process complexes and distribution centres at multiple sites, which provides the enterprise with the flexibility of producing its products at different locations, as opposed to the standard practice of centralising production at a single site. To maximise the economic benefits of an IRE rice mill complex, the enterprise may need to reconfigure its current resource allocation strategy and the corresponding utility network, at both the stage and site levels.


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At the stage level, the enterprise must select a profitable production portfolio and the corresponding technology while satisfying the variability supply and demand constraints of the IRE rice mill complex. Conversely, dealing with rice mills at multiple sites necessitates proper coordination among the various sites, subject to the facilities’ capacity constraint. The specific trade-offs involved in IRE rice mill complex are:

1) Variability in energy demand The harvested paddy must be dried within 48 hours to prevent degradation. Therefore, the rice mill will predominantly require thermal energy for drying during the harvesting season.

2) Energy supply options (cyclonic husk furnace vs cogeneration system) The cyclonic husk furnace (CHF) can provide a relatively cheaper thermal energy option as compared to the cogeneration system. While the CHF can directly generate hot air for dryers, the cogeneration system involves more losses as it channels the heat from the turbine exhaust steam into the air radiator to convert the energy source into hot air. However, the cogeneration system is able to generate on-site electricity while providing the thermal energy in the form of steam. Apart from satisfying the heat demand for the drying system, steam from the cogeneration system is also required in the rice bran oil extraction process. Therefore, to design an optimal utility system using rice husk as the fuel, the trade-off between the energy efficiency and the cost-effectiveness of the various energy alternatives needs to be carefully considered.

3) Rice husk limitation During the drying period, there may not be sufficient rice husk in a rice mill to satisfy the extensive thermal energy requirement of the dryers. Hence, it may be necessary to purchase and transport rice husk from other rice mills. The rice enterprise need to decide whether to purchase the rice husk from other rice mill to satisfy the requirements of the CHF or cogeneration system, or to directly buy electricity to satisfy both heat & electricity demand. Thus there is



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a trade-off between the costs of transportation, the purchase of additional rice husks and the electricity cost.

4) Centralised vs decentralised process complex. Harvested paddy and rice husk may be transferred to a centralised drying facility that has a large-scale cogeneration system. A decentralised drying facility may also be employed as an alternative. In this case, several smallerscale cogeneration systems may be installed at selected rice mills. Ultimately, selection of either a centralised or a decentralised drying facility essentially involves a trade-off between the capital and the logistic costs.

5) Rice mill capacity expansion vs rice mill construction on a new site. An enterprise can capitalise on its existing infrastructure, and save on the capital cost by choosing to expand a rice mill’s capacity at a current site,. From the perspective of logistic, however, the existing process complex may not be the most strategic site. The trade-off between the logistic cost to transport the resources and the capital cost needs to be evaluated. All of these issues highlight the need for a systematic framework to plan the optimal resource allocation and the utility network for a multi-site IRE rice mill complex. Multi-site production planning in processing and manufacturing industries has received significant attention from researchers. In the manufacturing industry, Guinet (2001) proposed the primal-dual approach and tested this approach with a wide range of problems to minimise variable and fixed costs 5. This model was used for global production planning and local workshop scheduling. Levis and Papageorgiou (2004) later presented a systematic mathematical programming framework for long-term, multi-site capacity planning with uncertainty for the pharmaceutical industry 6. In the petroleum refining industry, Kim et al. (2008) proposed a model that integrates the supply network with the production planning for multi-site refineries producing multiple products to examine the effects reallocating distribution centres. The results show that the reallocation of the distribution centre saves costs 7. Pitty et al. (2008) considered the stochastic variation in transportation, yields, price and operational problems in formulating an integrated refinery supply chain 8. Verderame and


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Floudas (2009) developed a model for multi-site planning with production disaggregation that was aimed at executing the operational-level decisions to increase the efficiency of production facility utilisation and customer-order fulﬁlment over a time horizon of several months 9. Recently, Kopanos and Puigjaner (2009) formulated a general mixed-integer linear programming (MILP) model based on the key aspects of scheduling/batching and production planning to identify the optimal scheduling decisions for a multi-site batch process system. In this work, the batch sizes and processing times were simultaneously optimised with the scheduling decisions 10. Narodoslawsky et al. (2008) highlighted several key challenges in the biomass supply chain planning and management. These include the competition posed by other resources, the need to identify the optimal locations as well as the corresponding capacities of process plants, issues of process synthesis and related technological challenges11. To address these challenges, there have been several recent works related to the biomass supply chain. For instance, van Dyken et al. (2010) formulated a mixed-integer linear model to represent the key components of the biomass supply chain which include the supply and demand, as well as the storage and processing considerations. The model correlates the energy content in the biomass and its moisture level 12. Bowling et al. (2011) formulated a mathematical model to determine the capacity, the operational strategies, and the locations of the biorefinery and the pretreatment facilities13. The model is capable of determining the optimal supply chain configuration that includes the site selection (centralized or decentralized) and the biomass feedstock location in order to maximise the overall profit. Another noteworthy study is conducted by You and Wang (2011). The authors considered the key factors in supply chain planning and management such as the conversion pathways, feedstock seasonality, geographical diversity, resource degradation and demand distribution 14. In another study, Čuček et al. (2010) formulated a regional biomass supply chain model with the objective function to maximise profitability. The model is based on a four-layer superstructure that includes harvesting, preparation, core processing and product distribution as the key components 15. Leduc et al. (2010) developed a model to identify the optimal locations of lignocellulosic ethanol refineries integrated with polygeneration 16. Later, Zhang et al. (2012) formulated an optimization model for a switchgrass-based bioethanol supply chain. The objective of the model is to determine the logistic decision by minimising the overall cost 17.



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Zhang et al. (2012), on the other hand, developed a model for biofuel supply chain. The model considers the key elements such as biomass harvesting/processing, transportation and storage to identify the optimal location for biofuel process site and corresponding logistic network 18. Marvin (2012) et al. proposed a model that considers the existing ethanol production facilities from corn, and the new potential sites for biofuel production. The minimum selling price for the biofuel at each facility is determined by performing a detailed cash flow analysis. Recently, Wang et al. (2012) presented an energy crop supply chain model to identify the optimal location and capacities for cogeneration facility, subject to the minimum cost of the overall system 19. For the pulp and paper industry, Aksoy et al. (2011) analysed four different technologies to convert woody biomass and mill waste into bio-energy products from the perspective of feedstock allocation, facility location and economic performance 20. For the rice industry, Delivan et al. (2011) performed a supply chain analysis on a rice straw-based cogeneration system that involves harvesting, loading, transporting, storage and process for energy exploitation 21. However, the authors did not consider the integration of cogeneration system with the rice mill. Later, Lim et al. (2011) proposed a model for the optimal design of a rice mill utility system that includes the cogeneration system and the CHF to satisfy the heat and electricity requirements of the rice mill throughout the year. To achieve the minimum cost, the model also considers the optimal planning of the rice husk logistic network 22. This study, however, does not include the potential of converting the rice mill by-products into value-added products. An analysis of the existing literature shows that only one study has focused on modelling the rice supply chain 23. The contributions of that study were twofold. i) It identified the drying and storage capacities as the bottlenecks of rice milling. ii) It explored various scenarios involving the capacity adjustments of the drying and milling processes and evaluated the economic performances of these scenarios. However, there are a few noteworthy limitations of the study. First, it only considered a single product. Second, the process technology was predefined by the user. Finally, the utility system was not considered. Thus the opportunity of utilising the by-products from the rice mill to generate commercial product or energy products has been overlooked. There is a clear need for a systematic approach to optimise the rice mill supply chain by integrating production planning with utility network, along with technology selection, capacity planning and site selection.


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To address these needs, an integrated model for a rice mill’s production planning utility planning has been formulated and presented in this paper. The model enables an enterprise to perform simultaneous resource allocation as well as to plan capacity expansion for rice mills that are located at multiple sites with the aim of maximising overall profit. This paper begins by presenting a superstructure that describes the MILP problem (Section 2) prior to the development of the Multi-Site Resource Efficient (MSIRE) model (Section 3). The model is tested using eight different scenarios (Section 4). Then, the impacts and sensitivities of the various scenarios on a rice mill’s profitability are analysed (Section 5).

1.1 Problem statement A rice mill enterprise operates two rice mills (Rice Mills A and B) in Northern Malaysia. Currently, these rice mills have a combined annual drying capacity of 50,000 tonnes of paddy harvested from several paddy fields in the region. The heat sources of the rice mills’ drying facilities are supplied by CHF, which is fuelled by rice husk. The main product of the rice mills is graded rice, which is a mixture of full head rice and broken rice. The unused broken rice and by-products from the rice milling process, such as rice husk and rice bran, are sold directly to downstream industries. To cope with the market demand and generate more profit, the rice enterprise has outlined two strategies, including i) transforming the rice mill into an IRE rice mill complex and ii) expanding the capacity of the rice mill facilities. By transforming the conventional rice mill into an IRE rice mill complex, the by-products can be utilised as feed to produce value-added products or as a renewable fuel for the mill’s utility system. For instance, the unused broken rice can be ground into rice flour using a flour-grinding machine. Furthermore, rice bran oil can be extracted from the rice bran using the appropriate technology for rice bran oil extraction. Rice husk can be consumed as a renewable fuel for the CHF or the cogeneration system. Along with the CHF, the cogeneration system can fully satisfy the heat and part of the electricity demands of the corresponding rice mill. To fulfil the fuel demands of the cogeneration and CHF systems during peak periods, it may be necessary to purchase and transport additional rice husk from other rice mills. Conversely, the expanded process facilities are expected to meet the increase in demand, which can be achieved



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by expanding the current process facilities in Rice Mills A and B. Alternatively, a new facility can be constructed at Rice Mills C or D. Note that these locations are selected by the rice enterprise, as they are situated closely to the paddy field and other companies’ rice mill, which can be a source of rice husk. Given the different sets of process and operating data for a technology (feed material composition, product/energy conversion yield, specific energy consumption, available capacities and seasonal operating hours), the logistic data (the distances linking paddy fields, process sites and distribution centres) and the cost data (product price, material cost, utility cost, transportation cost and capital cost), the problem consists of evaluating the following: • the product portfolio for each rice mill, • the utility system’s location and scale, • the decision of whether to expand the current processing facility or build a new facility and • the configuration of the paddy and rice husk logistic network. The relevant variables to be determined include the continuous and binary variables. The continuous variables are the quantity of resource/utility intake, the quantity of raw materials fed into a process unit and the corresponding product quantity. The decision variables are the i) technology, ii) capacity and iii) site location. The decisions are to fulfil product demand while considering the availability of resources from various locations. The next section describes the superstructure representing the formulated MSIRE model.

2.

Superstructure development A generalised MSIRE model has been developed with several key functionalities: a) Resource Allocation Planning: either to sell the resource directly or to further process it into a value-added product; b) Logistic Planning: to identify the logistic network inter-linking the source location, process site and distribution centre;


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c) Design of the IRE Complex: selection of the processes and the appropriate capacities for the process and storage facilities. Subsequently, Fig. 2 shows the superstructure of the multi-site IRE rice mill complex. The process stages in this figure include the external location f (paddy field and external rice mill), process site pc (process complex at the existing and new sites) and the distribution centre s, which are all situated at different locations. The line indicates the transhipment of resource i either between the aforementioned locations of interest or between two processes p within the same process site. The red dotted line represents the flow of utility u in a process site during period t. Note that resource i refers to the feed from the process or the subsequent product generated from the corresponding process. Depending on its functionality and source of intake, resource i can be termed as the system-generated (S.G.) resource, incoming intermediate resource or external resource (see “resource i” box in Fig. 2). In the context of this paper, S.G. resource (SGRES t,pc,p,m,i) refers to the material generated under the operating mode m of process p within the same process site pc during period t. The incoming intermediate resource (IMRES t,pc,i) is the resource transported from another process site pc during period t. The external resource (EXRESt,f,pc,i) is the resource purchased from other companies. Conversely, there are a few pathways to utilise the resources at the process site. First, the resource can be further utilised as feeds for other processes within the same process site (MAT t,pc,p,m,i). The resource can also be transferred to other process sites (pc’) as an outgoing intermediate resource (TRANSRES t,pc,pc’,i). Additionally, the resource can be sold directly as a by-product (BYPRO t,pc,i) from process site pc or transferred to distribution centre c as a product (TRANSPRO t,pc,s,i). Unused resources can be shifted to the next period (t+1) as an inventory (INVRES t, pc, i).

The classes of utilities include the utility generated on-site (SGU t,pc,p,m,p,u), the utility converted from other types of utilities (SCU t,pc,p,u) and the external utility (EXU t,pc,u). The utilities can be consumed to satisfy the needs of the processes (UDEM t,pc,p,u) or can be fed into a process (INU t,pc,p,u) and converted into other types of utilities. Note that there may be an excess amount of utilities generated within the system (EXCESSU t,pc,u).



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3.

Model formulation The elements of the framework are highlighted in following sections: i.

Generic: The framework and model are generic and can be applied to other industries with minor modifications. In some cases, a constraint equation may not apply to all situations. To maintain the generic nature of the equation, a binary parameter is associated with specific terms in the equation. The parameter will act as an indicator to allow the corresponding term to be switched on/off. Apart from the aforementioned issue, note that the input composition and output conversion yield for the functional units may be different from one another. Therefore, to handle these two features, various types of matrices and indicators are incorporated into the material balance equation sets.

ii.

Model formulation and time representation: The model is formulated in a multi-period where the annual time horizon is divided into 12 months. Note that the purpose of this model is focused on resource allocation, capacity and logistic planning for a long time horizon as opposed to dayto-day scheduling.

iii.

Assumptions: •

No storage charges are considered for the inventory, as the function of the proposed model is mainly for strategic, long-term resource allocation planning and technology screening, rather than short-term scheduling.

•

A fixed electricity load factor is not considered.

•

The costs of piping and a steam distribution system are considered negligible compared to the equipment cost.

•

No binary variable has been defined for the selection of the paddy field location or distribution centre because the number of sites does not need to be fixed. In addition, there is no fixed cost associated with these locations, as the incurred cost is solely due to the logistic cost, which depends on the type of resources and the distances between these locations and the rice mill complexes.


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3.1 Objective function The objective function of the model is to maximise the overall profit (PROFIT), as described by eq. 1. The function consists of the revenue (REV), by-product revenue (BYREV), material cost (RCOST), processing cost (PCOST), total capital cost (CCOST) and logistic cost (LCOST).

(1)

REV (revenue) is represented by eq. 2, where NOS is the number of seasons in a year, and PIi is the decision parameter to indicate the feasibility of selling the resource i as a product, while PROt,s,i is a variable that denotes the quantity of the distributed product i at distribution centre s during period t. PRIi denotes the unit price of product i, which is obtained from the published price. ∑,, ,,

(2)

BYREV represents the revenue generated from the selling of by-product i. BYREV can be expressed by eq. 3 in terms of BYPRO t,pc,i , which is the by-product i at process site pc. BIi represents the decision parameter to indicate the feasibility of selling resource i as a by-product. ∑,, ,,

(3)

RCOST represents the total material cost, where EXRESt,f,pc,i is the input rate of the external resource i from location f, while URCOSTi denotes the unit purchasing cost of the corresponding external resource i. RCOST is given by eq. 4. ∑,,, ,,,

(4)

PCOST represents the total cost involved in producing the resource via the corresponding technology (see eq. 5). MATt,pc,p,m,i denotes the input rate of material i



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into technology p under operating mode m at processing complex pc. The UPCOSTp represents the unit processing of process p. ∑,,,, ,,,,

(5)

UCOST represents the total utility cost as defined by eq. 6, where EXUt,pc,u is the external utility requirement of utility u at process site pc during period t, and UUCOSTu is the relevant utility cost. ∑,, ,,

(6)

LCOST is the logistic cost, which consists of several components, including TCEXRESt,i (incurred transportation cost between the resource supply location and process site), TCIMRESt,i (incurred transportation cost between two process sites at different sites) and TCPROt,i (incurred transportation cost between the process site and distribution centre). LCOST is defined by eq. 7. !∑ , , , "

(7)

CCOST refers to the annualised capital cost involved in expanding the current process capacities or installing a new process/technology at a site. CCOST is defined in eq. 8, where YEXPp,pc,z is a binary variable used in determining the expansion capacity of process p at process site pc with size z, and EXPCOSTp,,z is the corresponding annualised expansion cost. ∑,,# ,,# ,#

(8)

The final term, FCOST, is related to the annualised cost to construct (CSTCOSTpc) process complex pc at a new site. YPCpc is a binary variable used to decide whether process complex pc will be constructed at the new site. ∑


(9)

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3.2 Mass balances of resources Eq. 10 describes the balances of the input sources. RESt,pc,i is the quantity of resource i at process site pc, SGRESt,pc,p,m,i is the system-generated resource within the same process site, IMRESt,pc,i is the incoming intermediate resource from other process sites, and EXRESt,f,pc,i is the external resource. $ %,,,, ,, $ ,,, ,

,, ∀( ∀)* ∀+

(10)

Note that the intake of an external resource is governed by the availability of the corresponding resource. This constraint is formulated in eq. 11, where AVAIRESt,f,i is the available quantity of resource i at location f during period t. ,, , ∑ ,,, ∀- ∀( ∀+

(11)

Note that resources can be utilised as materials within the same process site as by-products to be sold directly from the process site, an intermediate resource to be utilised in another process site, or a product to be sent to a distribution centre, as described in following set of equations (eqs. 12-15). In these equations, INVRESt,pc,i is the quantity of resource i at process site pc during period t that shifts to period t+1 as inventory, while OPEINVpc,i is the initial inventory at the beginning of period t=1. Note that INVi is the inventory indicator used to denote the feasibility of shifting resource i into the next period. TRANSRESt,pc,pc’,i is the transhipment flow of intermediate resource i between a process site pc and another process site pc’ during period t. TRANSPROt,pc,s,i is the transhipment flow of product i between process site pc and distribution centre s during period t. ,,

∑, ,,,, , ,, .////0////1 23 23

,, .//0//1

45536 2 7892:

∑ ,,′, ,,, ∀( ∀)* + 1 ./ /////0/// ///1 ∑ ./////0/////1 92:


(12)


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,,

∑, ,,,, 1 ,,′, ./ /////0/// ///1 ./////0/////1

(13)

∑ ,,′, ,′, ∀( ∀)*′ ∀+

(14)

∑ ,,, ,, ∀( ∀? ∀+

(15)

92:

Note that only the intermediate material can be transported between two process sites. Products, such as graded rice and rice bran oil, cannot be transported between two sites, as shown in eq. 16. ∑ ,,′, !1 " ∑ ,, ′ , ∀( ∀)*∀)*′ ∀+(16) One factor that governs the quantity of product i is PRODEMt,s,i , which is the demand of product i at distribution centre s during period t and is expressed by eq. 17. Product i at distribution centre s must fulfil the production demand and must not exceed the limit defined by the product demand indicator, PDIi. @,, A ,, A @ @,, ∀( ∀? ∀+

(17)

3.3 Mass balances for a process technology The mass balance for a process technology p is computed using eqs. 18-20. PRESt,pc,p,m is the quantity of material being processed by process p at site pc under operating mode m during period t, which is then processed into a system-generated resource (SGRES t,pc,p,m,i) or a system-generated utility (SGU t,pc,p,m,u). The integration matrices, including the material composition matrix (MCMi, m, p), process resource conversion matrix (PRCMp, m, i) and process utility conversion matrix (PUCMp, m, u),


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are three important indexes of the corresponding process, and they represent the material composition, product conversion and utility generation of a particular process, respectively. ,,,, ,,, ,, ∀( ∀) ∀B ∀)* ∀+

(18)

,,, ,, %,,,, ∀( ∀) ∀B ∀)* ∀+

(19)

,,, ,, %,,,, ∀C ∀) ∀B ∀)* ∀+

(20)

The generated utility can be converted into another type of utility. For instance, the steam generated from the cogeneration system can be converted into hot air via an air radiator. The following equations represent the conversion process, where INUt,pc,p,u is the quantity of the utility resource as an input into the conversion process p, PUt,pc,p is the quantity of the processing utility resource, and SCUt,pc,p,u is the quantity of the system converted utility. The input and output of the utility-converted process are correlated with the utility composition matrix (UCMu,p) and utility-utility conversion matrix (UUCMu,p). ,,, ,,

, ∀C ∀) ∀)* ∀+

(21)

,, , ,,, ∀C ∀) ∀)* ∀+

(22)

3.4 Capacity selection The hourly throughput of a processing unit (HRPRES p,pc,t and HRPU p,pc,t) is governed by the lower and upper operating limits of the capacity, as described in eqs. 23 and 24. CAPp,z is the available capacity for each process p, and CCAPINp,pc,z is the parameter index that denotes the existing capacities of process p at process site pc, while YOEXPt,pc,p,z, is a decision variable for operating process p with an expanded size z during period t. The lowest operating capacity is constrained by the minimum



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operating level MINOPEp, which is expressed in terms of the percentage of the total capacity. For instance, the boiler of a cogeneration unit (p=6) must be operated above 50% of its total capacity to ensure its operability. !$ ,# ,,# $ ,# ,,,# " D

#

A E,, A $ ,# ,,# $ ,# ,,,# D

#

) > 8 ∀)* ∀+

(23)

!$ ,# ,,# $ ,# ,,,# " A D

#

E,, A $ ,# ,,# $ ,# ,,,# D

#

) 8 ∀)* ∀+

(24)

Note that the capacity is expressed as an hourly unit. Therefore, the PRESt,pc,p (in months) or PUt,pc,p (in months) is divided by the number operating days per month (MODp) and hours operating per day (DOHp) to obtain the hourly throughput as shown in eqs. 25 and 26. GHIJK,LM,L

E,, NOP

L POQL

GRK,LM,L

E,, NOP

L POQL

) > 8 ∀)* ∀+

) 8 ∀)* ∀+

(25) (26)

The equipment of the selected process p must be purchased prior to its operation, as shown in eq. 27. Note that YEXPp,pc,z is the decision variable to make a decision about the purchase of related technology. ,,,# S ,,# ∀) ∀)* ∀T

(27)

For each technology, only one process unit can be purchased, as constrained by eq. 28. ∑# ,,# S 1 ∀) ∀)*


(28)

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If the process site pc involved in operating the process is a to-be-built process complex, the building of the corresponding process site must be constructed first, as shown in eq. 29, where YPCpc is the decision variable of constructing the groundwork of process site pc. ,,# S ∀) ∀)* ∀T

(29)

3.5 Utility demand and supply The overall utility balance of the system is expressed by eq. 30. The left- and right-hand sides of the equation denote the utility supply and demand, respectively. The supply of utility u consists of the utility generated from the system (SGUt,pc,p,m,u), the utility converted from the system (SCUt,pc,p,u) and the external utility (EXUpc,u,t). Note that the availability of the external utility u is governed by the external utility indicator EUIu, which is the decision parameter. On the demand side, the equation consists of the utility required for each process (UDEMt,pc,p,u), the utility input for the conversion process (INUt,pc,p,u) and the excess utility (EXUt,pc,u). ∑, %,,,, ∑ ,,, ,,

∑ @ ,,, ∑ ,,, ,, ∀)* ∀C ∀+

(30)

Next, the demand for utility u at process site pc is formulated by eq. 31, where VUDp,u is the demand for utility u for each processing unit p. @,,,

∑ @, ,,, @, ,, ∀) ∀)* ∀C ∀+

(31)

3.6 Logistics The logistic cost is determined based on the transhipment flow, the distance between two locations and the unit transportation cost of resource i (UTCOSTi). The logistic cost is formulated in eqs. 32- 34. DISFPCf,pc is the distance between the



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material field f and process site pc; DISPCpc, pc’ is the distance between the process site pc and another process site pc’; and DISPCS pc, s is the distance between the process site pc and distribution centre s. , ∑, ,,, @, ∀( ∀+

(32)

, ∑,′ ,, ′ , @,′ ∀( ∀+

(33)

, ∑, ,,, @, ∀( ∀+

(34)

4.

Case study Rice Enterprise A currently operates two rice mills and three distribution centres

in Northern Malaysia. Fig. 3 shows the resource flows linking the paddy fields, process site and distribution centre. Tables 1 and 2 are the labels for the process and resource/utility, respectively. Currently, the rice mills have a combined annual drying capacity of 60,000 tonnes of paddy (i=0), which is harvested from the regional paddy fields. Rice husk (i=5) is consumed in the CHF, which supplies hot air (u=3) for the dryers. The main product of the rice mill is graded rice (i=6, 7), which is comprised of a mixture between full head rice (i=2) and broken rice (i=3). The unused broken rice (i=3) and by-products from the rice milling process, including rice husk (i=5) and rice bran (i=4), are sold directly to other industries. The electricity (u=1) demand for the two rice mills are supplied by the national grid. The graded rice produced from these two rice mills is used to satisfy the demands of the three distribution centres at different locations. The combined annual demands for these centres are 12,960 tonnes for 5% graded rice and 16,620 tonnes for 10% graded rice. Table 3 shows the detailed current product demand at each distribution centre and the corresponding product demand indicator (PDIi). The product demand indicator for the 5% graded rice and 10% graded rice is 1.1. This value indicates that the maximum quantity that can be purchased is 10% above the production demand. The enterprise plans to expand the total production capacities of its rice mills to satisfy the forecasted 100% increase in demand. The enterprise is also investigating the possibility of utilising the by-products from its rice mill by incorporating an IRE


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rice mill complex into its existing operation. These two measures can be implemented at the existing rice mill sites (locations A and B) or at some new rice mills sites (locations C and D). The map in Fig. 4 shows the geographical locations of interest, including the existing process sites, potential new sites, paddy fields, external rice mills and distribution centres. Table 4 shows the corresponding labels of the map. Table 5 shows the current process capacities of different technologies at each process site. The distance between two sites is shown in Tables A.1, A.2, A.3 and A.4. Table A.5 shows the availability of paddy (from the paddy field) and rice husk (from the external rice mills) at different locations. Note that the duration for one cycle is six months. Each cycle is divided into six monthly periods. The first two periods (t=1, t=2) are harvesting periods, where the paddy is harvested from the field and transported to the process site to be dried within three days. Fig. 5 shows an overview of the resource flow, while Fig. 6 shows the detailed superstructure for the unsolved case study. At the beginning of the resource flow, wet paddy harvested from the paddy fields is transported to the rice mill complexes at various locations (the existing site, a new site, or both) and is dried in the drying facilities. Next, the dried paddy is milled into full head rice, broken rice, rice bran and rice husk. These products can be further processed into downstream products or resources or can be sold directly to the customers. For further processing, the full head rice and broken rice are mixed at a specific ratio to produce different graded rice. Rice vermicelli can be produced from broken rice using a rice vermicelli machine. The rice bran oil is extracted from the crude rice bran to produce defatted rice bran as the by-product. Rice husk can be utilised in the CHF to generate hot air as a heat source for the drying system, or the rice husk can be used in the cogeneration system to generate heat (steam) and electricity. The exhaust steam from the cogeneration system can heat up the air, which will be used as a heat source for the drying system. Any excess steam can be supplied for use in other downstream processes. Finally, rice husk ash, which is the by-product of the combustion system (CHF and cogeneration system), can be sold to other industries to be further processed into other value-added products. By extracting the data of the process and by using resources from the literature and suppliers, an MPR table is shown in Table 6. Subsequently, the material composition matrix is assigned to denote the composition of material i in technology p, as shown in Table A.6. Next, Tables A.7 and A.8 show the process-resource



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conversion and process-utility conversion matrices for each technology related to each resource, respectively. For the process unit that functions to convert one utility into another type of utility, such as the air radiator (p=8), the input utility composition matrix and conversion coefficient are shown in Tables A.9 and A.10, respectively. Next, Table A.11 shows the variance of the utility demand of each process. Table A.12 shows the related parameters for the operating duration of each functional unit. Table A.13 shows the assignment of the inventory indicator INVi, by-product indicator BIi and product indicator PIi for the resources involved in the IRE rice mill complex (referring to Fig. 6). Among the listed resources, only the dry paddy and rice husk are allowed to shift into the next time period as inventory. The remaining resources must be processed immediately. Broken rice, rice bran, rice husk and rice husk ash are by-products that can be directly sold from the process site. Conversely, graded rice, vermicelli, defatted rice bran and rice bran oil must be transported to the distribution centre to be sold as a product. The economic data are the key decision factor for the model to select the process route to achieve the objective function to maximise profit. The economic data can be categorised into process-related costs, resource-related costs and utility costs. The process-related costs consist of the capital and unit processing costs. In this paper, the capital cost is annualised over the period of 25 years to match the time horizon of the model, which is one year. Table A.14 indicates the possible expansion of the capacity of process p at process site pc. Table A.15 shows the annualised capital cost of a process p of size z. Table A.16 shows the annualised construction cost of process site pc. Table A.17 shows the unit processing cost for a process. The unit processing cost consists of the variable costs, such as labour, supervision, maintenance and repair, supplies and overhead costs. This information is estimated based on the published data or collected from the equipment vendor or from users of the existing technology. The resource-related costs of the model consist of the unit resource cost, price and logistic cost. Table A.18 shows the unit resource cost and the price of the resources for the case study. The unit logistic cost depends on the resource type. Table A.19 shows the unit logistic cost for resource i. The utility cost UUCOSTu for each utility is shown in Table A.20.


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5

Results and discussion The case study data were fitted to the developed MILP model and optimised with

the CPLEX solver (version 12.3) from the GAMS software (version 22.9) in 0.39 seconds. On the model statistics, there are 58 constraints and 55 variables. Eight different scenarios were analysed using the model to investigate the effects of process site reconfiguration, increases in the product demand and the incorporation of the IRE rice mill complex. Table 7 provides a definition for each scenario. Scenario 1 represents the baseline case, which reflects the enterprise’s existing product demand and process technology. The optimal solution associated with the process and technology selection, product generation and logistic network for each scenario is presented and discussed in the following sections. Table 8 shows the revenue and total cost for each scenario for the optimal case. In terms of profit, most of the scenarios outperform the baseline case, except for Scenario 3. Table 9 shows the annual production rate for different products and byproducts for different scenarios, and Table A.21 shows the detailed breakdown of the seasonal production rate at different sites. Table A.22 shows the related capacity selection for the different process technologies to produce these products. The supply and demand for electricity and heat for these technologies are presented in Tables A.23 and A.24, respectively. Finally, Fig. A1 to A8 shows the logistic network linking the paddy field, external rice mills, process sites and distribution centre, along with its transhipment flow.

5.1

Effects of reconfiguring the location of process site The economic comparison of the four cases in Table 10 shows that the

reconfiguration of the process site (Scenario 2) increases the product revenue and logistic cost for all cases, leading to an increase in the site’s annual profit. Comparison 1 shows that the reconfiguration of the process site location allows the optimal location to be selected to operate processes and to fulfil the demand at the distribution centre. For the baseline case, the rice mills are located at process sites pc=1 and pc=2. By allowing for the construction of a new process site at a new site, process site pc=4 is selected to be constructed. Part of the production load of process site pc=1 is now shifted to the new site, as shown in Fig. A2. The shorter distance



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between the paddy field and process site (pc=4) and between the process site (pc=4) and distribution centre reduces the logistic cost and thus increases the overall profit from USD 920,190 (baseline case) to USD 2,049,890 at the expense of capital and construction costs. Comparisons 2 and 4 of Table 10 show that by increasing the demand, the economic advantage of reconfiguring the process site increases further. By allowing for the reallocation of the process site, the annual profit for both Comparisons 2 and 4 improves remarkably by a margin of USD 3 million. For increasing demand, more resources need to be transported to the process sites as raw material or transferred to the distribution centres as products. Therefore, shifting the processing load to a more strategic location leads to a massive savings in the logistic cost. 5.2

Effects of Increasing demand Table 11 shows that for all comparison cases, increasing the production output

increases the revenue from the sales of products and by-products. The increase in the product demand also increases the processing load and thus the capital and operating costs. As shown in Comparison 1, the existing process capacities (Scenario 1) cannot satisfy the higher product demand. Therefore, the process capacities at the existing site must be expanded (Scenario 3), as shown in Table A.22. The increase in the product demand will require a reconfiguration of the logistic network, which will ultimately affect the logistic cost. For instance, in Comparison 1, the optimal configuration for the baseline case (Scenario 1) involves transferring the wet paddy from the paddy fields (f=1, f=3 and f=4) to the process sites (pc=1 and pc =2) (see Fig. A1). As the product demand increases (Scenario 3), the amount of wet paddy harvested from these fields exceeds the demand of the rice mills. This increment causes the rice mills to transport additional paddy from other fields (f=2 and f=5) over distances longer than those required for the baseline case (see Fig. A2). As a result, the logistic cost increases. In most cases, the revenue gained is able to offset the increase in costs. However, in Comparison 1, the overall profit of USD 920,190 (Scenario 1) turns into a deficit of USD 638,200 (Scenario 3) because the revenue gained by the conventional rice mills is not sufficient to recover the extra costs. The extra cost is mainly attributed to the


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logistic cost incurred as more resources are transferred and processed in the existing rice mills, which are located at less strategic sites. In Comparison 3, even though the process locations are fixed at pc=1 and pc=2, the high revenue gained from the incorporation of the IRE rice mill complex is able to cover the high logistic cost.

5.3 Effects of IRE incorporation Table 12 shows that the incorporation of the IRE rice mill complexes markedly improve the product revenues by more than 100% for all comparison cases. This increase in revenue is accompanied by a 20% to 32% savings in utility cost at the expense of extra resource costs, processing costs and capital costs. The increase in revenue is due to the conversion of by-products from rice mills into value-added products with higher selling prices. For instance, in Comparison 1, the revenue of the existing rice mills (Scenario 1) comes from the sales of the main products (5% graded rice and 10% graded rice) and by-products (broken rice, rice bran, rice husk and rice husk ash). Scenario 4 represents the case where some of the by-products are further processed into value-added products. A comparison between Scenarios 4 with 1 shows that the quantity of by-products under Scenario 4 are significantly less, except for the case of rice husk ash because the broken rice is used to produce vermicelli, while rice bran is processed to produce rice bran oil and defatted rice bran. Sales of the value-added products and the unconverted byproducts generate a revenue of USD 19,416,000, which is a 54% improvement over Scenario 1. All comparison cases also show that despite the increase in heat and electricity demand, the utility cost is reduced. For example, in Comparison 1, even though more processes are operated in Scenario 4 than in Scenario 1, the utility cost is reduced due to the operation of the cogeneration system at the process site (pc=1 and pc=2), as shown in Table 12. As a result, more rice husk is utilised as fuel for the cogeneration system and leads to an increase in the rice husk ash, as shown in Table 9. Tables A.23 and A.24 show that the heat demands of an existing rice mill are typically supplied by the CHF, while the electricity is purchased from the national grid. By installing the cogeneration system, 97 MW and 1,448 MW of electricity are generated by process sites pc=1 and pc=2, respectively, in each season to fulfil the electricity demand of the IRE rice mill complex. For process site pc=2, steam from the



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cogeneration system is converted into hot air (1.67 x 106 MJ per season) to partially satisfy the dryers’ heat load. The installation of the cogeneration system reduces the utility bill by 28%, i.e., from USD 1,734,200 to USD 1,248,500.

6.

Conclusion The MSIRE System has been developed as a model-based generic framework for

the efficient management of multi-site IRE rice mill complexes. The case study has shown that the increment of production throughput, incorporation of the IRE rice mill complex and reconfiguration of the process site can have a strong effect on the economic performance of a rice enterprise. By simply allowing the reconfiguration of the process site, the rice enterprise increases its profit from USD 920,190 (baseline case) to USD 2,049,800 due to the reduction in logistic costs. Conversely, the incorporation of the IRE rice mill complex increases the profit to USD 5,760,000 due to the sales of value-added products and the utility cost savings. Finally, by combining all three factors, the potential maximum profit of this case is USD 10.103 million/year, which is a 998% of improvement compared to the current case. In conclusion, the MSIRE tool can assist planners in performing resource allocation for IRE rice mill complexes at multiple process sites. The MSIRE provides quantitative insights in screening the various production technology options and locations for process site reallocation. The proposed model identifies the most profitable technology combination and the corresponding sizes, along with the logistic network linking the paddy field, process site and distribution centre. Due to the generic element of the MSIRE tool, it has the potential to be applied to other industries beyond the rice industry.

Acknowledgements The authors would like to thank the MOHE (Ministry of Higher Education) of Malaysia and UTM for providing the research funding under Vote No. Q.J13.2525.01H95 to implement the project.


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Supporting information available Table A.1 to A.25 and Figure A.1 to A.8 consisted of additional information of the study. This information is available free of charge on the Internet at http://pubs.acs.org/.

Nomenclature

Acronyms IRE

integrated, resource-efficient

MILP

mixed-integer linear programming

MSIRE

multi-site integrated resource efficient

CHF

cyclonic husk furnace

S.G.

system generated

Sets f

location of paddy field and external rice mill

i

resource

m

operating mode

p

process-stage

pc

process complex at process site

s

distribution centre

t

period

u

utility

z

capacity



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Parameters AVAIRESt,f,i

quantity of available resource i at location f during period t

CAPp,z

expansion capacity of technology p with capacity z

CCAPINpc,p,z

capacity index for process p at process site pc with capacity type z

CSTCOSTpc

annualised construction cost of the process site at process site pc

DISFPCf,pc

distance between field f and process site pc

DISPCpc,pc’

distance between process site pc and another process site pc’

DISPCSpc,s

distance between process site pc and sales centre s

DOHp

daily operating hour of process p

EXPCOSTp,z

annualised cost of technology p with capacity z

MCMi,m,p

the bonding index of input material i and process p for operating mode m

MINOPEp

minimum operating level of process p

MODp

monthly operating day of process p

NOS

number of seasons

OPEINVi,pc

opening inventory of resource i at process site pc

PDIi

product demand indicator to denote the maximum production level of product i

PRCMp,m,i

output yield of product i via process p for operating mode m

PRIi

price of resource i

PRODEMi,s

demand of product i at sales centre s

PUCMp,m,u

output yield of utility u via process p for operating mode m

UCMu,m,p

the bonding index of input utility u and process p for operating mode m

UPCOSTp

unit processing cost for process p

URCOSTi

unit resource cost of resource i

UTCOSTi

unit transportation cost of resource i

UUCOSTu

unit utility cost u

UUCMp,u

conversion yield of utility u via process p

VUDp,u

utility demand of utility u per unit processing of process p


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Binary Parameters BIi

by-product indicator; 1 if resource i is a by-product; 0 otherwise

EUIu

external utility indicator; 1 if external utility u is allowed to be supplied into the system; 0 otherwise

INVi

inventory indicator; 1 if resource i can be shifted into next period as inventory; 0 otherwise

PIi

by-product indicator; 1 if resource i is a product; 0 otherwise

Positive Variables BYPROt,pc,i

amount of by-product i at process site pc during period t

BYREV

total revenue of by-product

CCOST

total capital cost

EXCESSUt,pc,u

excess amount of utility u at process site pc during period t

EXRESt,f,pc,i

transportation of external resource i between external location f and process site pc during period t

EXU t,pc,u

amount of external utility u at process site pc during period t

FCOST

total construction cost

HRPRESt,pc,p

amount of processing resource for technology p at process site pc for operating mode m during period t in one hour

HRPUt,pc,p

amount of processing resource at technology p at process site pc during period t in one hour

IMRESi,pc,t

amount of intermediate resource i at process site pc during period t

INUt,pc,p,u

amount of utility u feed into process p at process site pc during period t

INVRESi,pc,t

amount of resource i at process site pc during period t being shifted forward to period t+1 as inventory

LCOST

total transportation cost

MATt,pc,p,m,i

amount of material i feed into process p at process site pc under operating mode m during period t

PCOST

total processing cost



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PRESt,pc,p,m

amount of processing resource at technology p at process site pc under operating mode m during period t

PROt,s,i

amount of product i at sales centre s during period t

PUt,pc,p

amount of processing utility resource of technology p at process site pc during period t in one hour

RCOST

total resource cost

RESi,pc,t

resource i at process site pc during period t

REV

revenue

SCU t,pc,p,u

system converted utility u by process p at process site pc during period t

SGRESt,pc,p,m,i

system-generated resource i by process p at process site pc for operating mode m during period t

SGU t,pc,p,m,u

utility u generated by process p under operating mode m at process site pc during period t

TCEXRESt,i

transportation cost of external resource i during period t

TCIMRES t,i

transportation cost of intermediate resource i during period t

TCPRO t,i

transportation cost of product i during period t

TRANSRESt,i,pc, pc'

transportation of resource i from process site pc to process site pc' during period t

TRANSPROt,i,pc,s

transportation of product i from process site pc to sales centre s during period t

UCOST

total utility cost

UDEM t,pc,p,u

utility demand u of process p at process site pc during period t

Variable PROFIT

Overall cost

Binary Variable YEXPp,pc,z

1 if technology p is purchased with capacity z at process site pc; 0 otherwise

YOEXPt,pc,p,z

1 if technology p is operated with capacity z at process site pc during period t

YPCpc

1 if a process complex is constructed at process site pc


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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 ACS Paragon Plus Environment


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

LISTS OF TABLES

Table 1 Functional unit labels. Table 2 Resource and utility labels. Table 3 Production demand and indicator at each distribution centre. Table 4 Set assignment on the rice mill, external location and distribution centre. Table 5 Existing capacities. Table 6 MPR table: case study. Table 7 Description of the scenarios. Table 8 Profit for each scenario. Table 9 Product portfolio. Table 10 Effects of reconfiguring the process site on profit. Table 11 Effects of increasing demand on profit. Table 12 Effects of incorporating IRE processes on profit.


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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


Table 1 Functional unit labels. Technology

Set p

Dryers

1

Milling

2

Rice Packaging Machine

3

Rice Vermicelli Machine

4

Rice bran oil extraction plant

5

Cogeneration

6

CHF

7

Radiator

8



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 34 of 42

Table 2 Resource and utility labels. Resource

Resource i

Wet paddy

0

Dry paddy

1

FH rice

2

BR rice

3

Rice bran

4

Rice husk

5

5% graded rice

6

10% graded rice

7

Vermicelli

8

Defatted rice bran

9

Rice bran oil

10

Rice husk ash

11

Utility u

Electricity

1

Heat (steam)

2

Heat (hot air)

3


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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


Table 3 Production demand and indicator at each distribution centre. Product demand, PRODEMis (tonnes/month) Distribution centre

Product demand

Product Total

indicator,

Simpang

Kepala

Kuala

Empat

Batas

Lanjut

5% graded rice

260

420

400

1,080

1.1

10% graded rice

345

540

500

1,385

1.1

Total

PDIi

2,465



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 36 of 42

Table 4 Set assignment on the rice mill, external location and distribution centre. Process site, pc

Label on

Location

map

Existing site

A

Bukit Besar, Kedah

1

B

Bukit Raya, Kedah

2

C

Langgar, Kedah

D

E F G H I J K L M N O

Telok Kechai, Kedah Kampung Bukit Besar, Kedah Mukim Jerlun, Kedah Titi Keliling, Kedah Kampung Alor Bunga Sena, Kedah Kampung Teluk Kandit, Kedah

New site

External location, f Paddy Field

Rice mill of

Distribution centre, s

other company

3 4

1 2 3 4 5

Anak Bukit, Kedah

6

Kampung Kubang

7

Jelai, Kedah Mukim Kubang

8

Rotan, Kedah Simpang Empat,

1

Kedah Taman Kepala Batas,

2

Kedah Kampung Kuala

3

Lanjut, Kedah


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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


Table 5 Existing capacities. Existing capacities, CAPp,pz (tonnes/hour) Process/Technology Process site pc p 1

2

1

15.0

10.0

2

5.0

3.5

3

5.0

3.5

7

1.5

1.0



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 38 of 42

Table 6 MPR table: case study. Input

Output

Process

Composition

Material

S.G.

Material-

S.G.

Material-

Unit Output/

p

(%)

i

Resource

product

Utility

utility

Input

i

coefficient

u

coefficient

1

0.95

Tonnes/tonnes

2

0.52

Tonnes/tonnes

3

0.15

Tonnes/tonnes

4

0.10

Tonnes/tonnes

5

0.20

Tonnes/tonnes

6

1.0

1

2

100

100

95

0

1

2

Tonnes/tonnes

3-1 5

3

90

2

Tonnes/tonnes Tonnes/tonnes

3-2

7 10

3

4

100

3

5

100

4

6

100

Tonnes/tonnes 8

2

Tonnes/tonnes

9

0.5

Tonnes/tonnes

10

0.15

Tonnes/tonnes

11

0.2

Tonnes/tonnes

5

11 7 8

100 100

5

1

7,800

MJ/tonnes

2

0.23

MWh/tonnes

0.2

Tonnes/tonnes

5 u=2

0

0

1

10,600

MJ/tonnes

3

0.80

MJ/MJ


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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


Table 7 Description of the scenarios. Scenario

Reconfigure the process site

Increase in Demand

Incorporation of IRE

Scenario 1

×

×

×

Scenario 2

√

×

×

Scenario 3

×

√

×

Scenario 4

×

×

√

Scenario 5

√

√

×

Scenario 6

√

×

√

Scenario 7

×

√

√

Scenario 8

√

√

√



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

Page 40 of 42

Table 8 Profit for each scenario. Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Scenario 6

Scenario 7

Scenario 8

(USD/year)

(USD/year)

(USD/year)

(USD/year)

(USD/year)

(USD/year)

(USD/year)

(USD/year)

Revenue Product revenue

8,245,800

8,895,400

16,492,000

18,988,000

16,732,000

19,516,000

33,687,000

35,933,000

By-product revenue

4,388,800

4,734,600

8,777,600

428,040

8,902,700

231,310

139,440

487,310

4,513,900

4,868,800

9,027,800

5,137,600

9,155,300

5,066,800

9,311,000

9,390,900

227,130

244,960

454,260

332,000

460,780

326,380

591,910

599,480

Utility cost

1,734,200

1,870,200

3,468,300

1,248,500

3,517,900

1,495,500

2,369,900

2,601,600

Logistic cost

5,239,200

4,458,900

12,764,000

6,472,300

9,811,700

6,432,900

15,363,000

12,820,000

Annualised capital cost

0

83,900

192,900

465,160

179,300

389,660

543,160

755,300

Annualised construction

0

50,000

0

0

150,000

100,000

0

150,000

920,190

2,053,300

-638,200

5,760,000

2,359,900

5,902,800

6,902,900

10,103,000

Cost Resource cost Processing cost

cost

Profit


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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


Table 9 Product portfolio. Quantity (tonnes/year) Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Scenario 6

Scenario 7

Scenario 8

Product 5% Graded rice (i=6)

12,960

14,256

25,920

14,256

26,749

14,256

25,920

26,749

10% Graded rice (i=7)

16,620

17,634

33,240

18,215

32,240

18,282

33,240

33,240

12,096

12,224

22,224

22,597

2,684

2,884

4,480

5,286

805

865

1,344

1,586

1,528

68

Vermicelli (i=8) Defatted rice bran (i=9) Rice bran oil (i=10) By-product Broken rice (i=3)

5,556

6,009

11,113

53

11,299

Rice bran (i=4)

5,244

5,657

10,488

390

10,640

Rice husk (i=5)

6,992

7,542

13,.985

1,555

14,137

3,018

3,725

5,818

Rice husk ash (i=11)

1,025

1,106

2,050

2,956

2,080

2,510

5,046

4,529



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

Page 42 of 42

Table 10 Effects of the reconfiguring process site on profit. Comparison 1

Comparison 2

Comparison 3

Comparison 4

Scenario 1

Scenario 2

Comparison

Scenario 3

Scenario 5

Comparison

Scenario 4

Scenario 6

Comparison

Scenario 7

Scenario 8

Comparison

(USD/year)

(USD/year)

(%)

(USD/year)

(USD/year)

(%)

(USD/year)

(USD/year)

(%)

(USD/year)

(USD/year)

(%)

Condition Increase in demand

×

×

√

√

×

×

√

√

Incorporation of IRE

×

×

×

×

√

√

√

√

×

√

×

√

×

√

×

√

processes Reconfigure process site Revenue Product revenue

8,245,800

8,895,400

7.88

16,492,000

16,732,000

1.46

18,988,000

19,516,000

2.78

33,687,000

35,933,000

6.67

By-product revenue

4,388,800

4,734,600

7.88

8,777,600

8,902,700

1.43

428,040

231,310

-45.96

139,440

487,310

249.48

4,513,900

4,868,800

7.86

9,027,800

9,155,300

1.41

5,137,600

5,066,800

-1.38

9,311,000

9,390,900

0.86

Cost Resource cost Processing cost

227,130

244,960

7.85

454,260

460,780

1.44

332,000

326,380

-1.69

591,910

599,480

1.28

Utility cost

1,734,200

1,870,200

7.84

3,468,300

3,517,900

1.43

1,248,500

1,495,500

19.78

2,369,900

2,601,600

9.78

Logistic cost

5,239,200

4,458,900

-14.89

12,764,000

9,811,700

-23.13

6,472,300

6,432,900

-0.61

15,363,000

12,820,000

-16.55

0

87,400

192,900

179,300

-7.05

465,160

389,660

-16.23

543,160

755,300

39.06

0

50,000

0

150,000

0

100,000

0

150,000

920,190

2,049,800

-638,200

2,359,900

5,760,000

5,902,800

6,902,900

10,103,000

Annualised capital cost Annualised construction cost Profit

122.76


2.48

46.36