ARTICLE pubs.acs.org/IECR
Optimal Design of a Rice Mill Utility System with Rice Husk Logistic Network Lim Jeng Shiun, Haslenda Hashim,* Zainuddin Abdul Manan, and Sharifah Rafidah Wan Alwi Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia ABSTRACT: Thermal energy for drying and electricity for milling operations typically comprise a significant 55% of a rice mill operating cost. Optimal design of rice mill utility system that efficiently utilizes rice husk biomass has potential to increase profitability of rice milling industry. This paper presents a mathematical approach for simultaneous optimal planning of rice husk logistic network as well as optimal design of a rice mill utility system that efficiently utilizes rice husk supplied from various locations in order to satisfy the electricity and drying requirements of the rice mill throughout the year. A mixed integer linear programming (MILP) problem was formulated to determine: (1) the optimal logistic network for the rice husk supply, (2) the economic scale of the rice husk cogeneration system, and (3) an optimal utility supply network for a series of dryers consisting of a combination of cyclonic husk furnace (CHF) and cogeneration systems. Solution to the MILP problem yields an optimal utility system configuration consisting of a specified network of rice husk logistic network, a 15 tons boiler for the cogeneration system and 8 units of CHFs to satisfy the rice mill heat and power requirements. Compared to the baseline, the optimal utility network manages to reduce 18.5% of the total rice mill annualized cost which include the costs of capital, fuel, and electricity.
processing plants excluding the utility system.5 Note that most studies on modeling and optimization of biomass-based cogeneration system have not considered rice husk logistic network. Arivalagan et al.6 made an early attempt to formulate an integrated energy optimization problem for a cogeneration system. They present an MILP model to determine the economic optimum energy-mix for a process plant. Uran7 modeled a cogeneration system for a wood processing plant into a multi period problem and identified the lowest heat capacity and the payback period for the system. Gamou et al.8 determined the optimal cogeneration system size to match the energy demand. Their work however, do not consider biomass logistic network. Caputo et al.9 considered logistic variables in formulating a model to evaluate the economics of biomass utilization in combustion and gasification plants. However, the model was not formulated into an optimization problem. An optimal combination of rice husk-based cogeneration and CHF systems can offer ample cost-saving opportunities for a rice mill to meet its electricity and drying demands. However, to maximize the economic impact of the cogeneration and CHF systems, the following constraints need to be considered: (a). Variation of Heat and Power Demands during Different Periods of the Year. The energy profile of a rice mill with a drying facility varies throughout the year. During harvesting period which typically last for 30 days in a paddy growing season, the harvested paddy will be dried to reduce its moisture content to a level that allows its quality to be preserved for storage, within 72 h of harvesting. This narrow time frame where predominantly
1. INTRODUCTION Rice milling companies often operate at marginal profit due to energy-intensive drying and milling operations that contribute to the high operating costs of rice processing. Thermal energy for drying and electricity for milling operations typically comprise a significant 55% of a rice mill operating cost.1 To effectively reduce operating costs and increase profitability while satisfying the electricity and drying needs of a rice mill at appropriate times of the year, it is vital to design an optimal rice mill utility system that efficiently utilizes rice husk as the main byproduct of rice milling and an important source of renewable energy. Rice husk typically amounts to between 22 and 24 wt % of the total paddy input. Its moderate heating value between 3000 and 3500 kcal/kg allows rice husk to be utilized in rice mill for power generation, for cogeneration system, and most extensively, for paddy drying.2 For the latter, rice husk is typically combusted in a cyclonic husk furnace (CHF) to produce hot gas for paddy dryers.3 It was reported that 20 kg of husk can generate 60 00070 000 kcal heat, enough to reduce the moisture content of 1 ton of paddy from 2014%. Rice husk has also been widely used as a fuel for cogeneration system, which must be designed with the flexibility to supply electricity for rice milling operations throughout the year, and heat for dryers during harvesting seasons (peak drying period). During the peak drying period, a cogeneration system should generate electricity for rice mill operations and provide extensive amount of thermal heat to dry paddy within 72 h of harvesting in order to preserve its quality. At other times of the year after the paddy drying season (i.e., during off-peak season), cogeneration should maximize electricity generation for rice milling. To date, many studies on optimization of rice processing revolve around rice milling operations, particularly drying.4 A few other studies focus on synthesis and optimization of rice r 2011 American Chemical Society
Received: August 5, 2010 Accepted: October 11, 2011 Revised: May 6, 2011 Published: October 11, 2011 362
dx.doi.org/10.1021/ie101667j | Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
thermal energy is being consumed in dryers is known as the peak drying period. During this period, electricity is also consumed to operate the rice mill’s machinery and appliances. The off-peak drying period covers the remaining duration of paddy growing season outside the 30-day paddy harvesting and drying periods. During the off-peak season when the dryers are not operating, energy is mainly consumed in the form of electricity. A detailed assessment must therefore be made to economically exploit the marked difference in heat and energy demands during the peak and off-peak drying periods for the proposed utility system. (b). Energy Supply Options. Several options can be considered in order to meet the heat and power demands of an integrated rice mill and drying facility. The heat demand can be fulfilled by a combination of cogeneration and CHF systems which utilize rice husk as a source of fuel. From the point of view of thermal energy requirement, CHF is an attractive option as the large amount of calorific value available from rice husk combustion can be used to satisfy the huge quantity of heat required during peak drying season. On the other hand, cogeneration can supply bulk of the electricity requirement for rice milling during off-peak season, and supply heat, as well as electricity for drying and milling operations during peak season. Without a cogeneration system, the rice mill electricity demand has to be supplied from the national grid. The efficiency and cost-effectiveness of the various energy options are vital to consider for an optimal utility systems design. (c). Rice Husk Supply Limitation. Rice husk as the main byproduct of rice milling is an important and easily accessible source of renewable energy for a rice mill. However, the amount of rice husk from a given rice mill is typically limited and may not be able to sustain the heat and power demands of energyintensive milling and drying operations. Hence, there is a need to transport and purchase rice husk from other rice mills. The limited amount of rice husk and the required transportation as well as purchasing costs are key factors to consider in the design of a rice mill utility system. Limited supply of rice husk, the rice husk cost as well as logistic cost, varying heat and power demands during different periods of the year, and various energy supply options are the key factors influencing the optimal design of a rice mill utility system. To date, no study has simultaneously explored all these factors. There is a need to develop a new systematic tool for the optimal planning and design of a utility system configuration comprising of cogeneration and CHF systems that considers the logistic network. This paper presents a mathematical approach for simultaneous optimal planning of rice husk logistic network, as well as design of a rice mill utility system that efficiently utilizes rice husk from various locations to satisfy the electricity and drying requirements of the rice mill at appropriate times of the year. To address this problem, we formulate an integrated superstructure that consists of all logistics and utility system configurations of practical interest (section 2), transform the superstructure as a mixed integer linear programming (MILP) problem, and develop an optimal solution methodology (section 3). The method is tested on a case study involving a local rice mill in Malaysia (section 4). Sensitivity analysis is performed to assess the effects of parameter changes on the utility systems design (section 5). 1.1. Problem Statement. A rice producing company planned to install a cogeneration system within its centralized drying facility which is located in one of its rice mills. The proposed drying facility is aimed to dry approximately 100 000 tons of
Figure 1. Schematics of a conventional utility system.
paddy annually. A combination of CHF and cogeneration system shall be designed to meet the heating requirements of the drying facility and also to generate power in order to meet some (if not all) of the mill’s electricity demand. This setup which includes a proper coordination for the rice husk logistic network is expected to provide ample cost-savings opportunity for the rice producing company. As mentioned earlier, the ratio between paddy to be dried (to reduce moisture content from 20% to 14%) and the required rice husk is around 50: 1. However, the moisture content of paddy collected from the field in a humid country like Malaysia can reach up to 25%. It is therefore estimated that 4,000 tons of rice husks are required to dry 100,000 tons of paddy, to give a ratio of 25: 1 between paddy to be dried and the required rice husk in the case of Malaysia. More rice husks are therefore required to be purchased and transported from other mills make up for the limited amount of rice husk being produced by a single rice mill. Given the different heating and electricity requirements during peak and off-peak drying seasons for a rice mill, a cogeneration system operating conditions, capital cost for various sizes of cogeneration system, distance between rice husk supply locations and utility system and transportation cost, the problem consists of simultaneously determining: (1) The optimal logistic network for rice husk supply (2) The economic scale of rice husk-based cogeneration system (3) The optimal utility network configuration The relevant variables to be determined are the rice husk logistic network (rice husk transported from other rice mill locations), the cogeneration system design parameters (size, rice husk consumption, steam flow rate, electricity generation) and the utility system configuration which consists of cogeneration and CHF (steam flow rate from cogeneration system and hot gas from selected CHF units). The proposed utility system’s design takes into account typical heat losses of operating equipment to the ambient. The objective is to achieve the required heat and electricity demands at the minimum total annualized cost of equipment, utilities, and logistic costs.
2. PROBLEM FORMULATION 2.1. Superstructure. Figure 1 shows the schematic of a typical rice mill utility system which utilizes rice husk as a fuel. The CHF supplies heat to a dryer system which may consist of a combination of commonly used dryers including fluidized bed dryer (FBD) and inclined bed dryer (IBD). Electricity is supplied by the national grid. 363
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
Figure 2. Integrated logistic- utility system network.
Figure 2 illustrates the proposed superstructure that consists of two substructures, namely rice husk logistic network and utility system network. Rice husk is transported from internal rice mills (represented by subset j) and external rice mills (represented by subset k). The cogeneration system, CHF, IBD and FBD are represented by b, c, i, and f respectively. The details for each substructure are explained below: (a). Rice Husk Logistic Network. Rice husk supply come from internal rice mills j, or external rice mills k at various locations. The distance between rice husk supply locations and the cogeneration facility affects the transportation cost. The internal rice mills are the rice mills owned by the company which also operates the cogeneration facility. Hence, the rice husks from the internal rice mills are free of charge. For the external rice mill, the rice husk cost must be added on top of the transportation cost. (b). Utility System. Utility system includes a cogeneration system (consisting of a boiler and a turbine), a CHF system and electricity grid. The utility system configuration highly depends on the rice mills heat as well as electricity demands within a fixed period t. High pressure steam generated in the boiler is used to drive steam turbine and convert mechanical energy to electricity. During peak drying period, the exhaust medium pressure (MP) and low pressure (LP) saturated steams from turbine are utilized to satisfy the dryers’ heating requirements. However, during offpeak drying period where there is no heat demand for dryers, cogeneration can maximize its power output and be the main source of electricity supply for the rice mill.
(c) Piping cost for the steam distribution system is assumed negligible in comparison to equipment cost. (d) No binary variables have been defined for the rice mill locations for the following reasons: (i) No limit has been imposed on the number of rice mills selected. (ii) Unlike machines or equipments that need to be purchased or installed, no cost is involved in selecting rice mills. The cost of rice husk actually depends on the transportation as well as purchased cost. 3.1. Objective Function. The objective of this study is to simultaneously synthesize an optimal structure of rice husk logistic network, as well as a rice mill utility system, which includes a cogeneration system and CHF units, aimed at meeting the rice mill heat and power demands at minimum cost. The minimum total annual cost function consists of the capital costs for cogeneration system and for CHF, and the operating costs comprising of electricity, transportation, and rice husk costs.
The annualized capital cost of equipment is given by eq 2.10 rn r e ðe 1Þ ACCOST ¼ CCOST "t ð2Þ ern 1
3. MODEL FORMULATION The proposed linear model is formulated with the following assumptions: (a) Electricity load factor variation is not considered since the machines and equipment are assumed to operate at full load. (b) The heat loads of the dryers are fixed, by assuming:
3.2. Rice Husk Demand and Supply. Equation 3 describes the relationship between rice husk demand and supply. The lefthand side of eq 3 states that rice husk supply comes from internal rice mills (j), as well as external rice mills (k). The right-hand side of the equation states that rice husk’s demand comprises of CHF (c) and cogeneration system (b), which provide the rice mill heat and power demands within a given period t.
(i) Moisture content of collected paddy and the targeted moisture contents are constant. (ii) The dryers operate at full load throughout the peak drying period. (iii) Temperatures of the dryers are uniform throughout the drying process.
∑j RHJjt
þ
∑k RHKkt ¼ ∑c RHCct
þ
∑b RHBbt " t ð3Þ
364
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
3.3. Steam Generation from Rice Husk. HP steam is generated when rice husk is burnt as fuel in the boiler. The amount of HP steam generated depends on the amount of rice husk burnt, its calorific value, the boiler efficiency and the enthalpy change across boiler as given by eq 4.
HPbt ENBBb ¼ CCRH RHBbt ηBBb " b " t
The IBD heat demand is satisfied by LP steam extracted from turbine and CHF units. HEATIit ¼ HEATLPIit þ
∑b ElecGBbt " t
ElecGt ¼
boiler is then extracted as MP and LP steam, as given by eq 5. ð5Þ
ElecDt ¼ ðBElecDt þ
ðMPbt ENMPb LPbt ENLPb Þ ηTURBb "b"t 3600
ð6Þ tions 7 and 8 show how the MP and LP steam are utilized. The MP steam extracted from turbine is directed to heat exchanger coils at the FBD to heat up the dryer air.
þ LPCt " t
þ
∑c YOCct ðElecCHFct Þ þ ∑f ElecFft
þ
∑j ElecIit Þ=1000 " t
ð16Þ
ElecOt ¼ ElecDt ElecGt " t
ð7Þ
ð17Þ
Equation 18 is used to annualize the seasonal requirement of rice husk.
The LP steam is directed to IBD heat exchanger coils or to cooling water system.
∑b LPbt ¼ ∑i LPIit
∑b YOBbt ðElecBBbt Þ
If electricity produced by cogeneration system is insufficient to meet electricity demand, the additional electricity requirement will be outsourced from the national grid. Electricity produced from cogeneration system is related to outsourced electricity as follows:
3.5. Steam Generation for FBD or Cooling Water. Equa-
∑b MPbt ¼ ∑f MPFft " t
ð15Þ
Equation 16 is used to calculate the total electricity demand for the rice mill and drying facilities.
The ideal work done by the turbine is the total enthalpy change of MP and LP steam across the extraction- condensing turbine. Considering the turbine efficiency (ηTURBb), electricity produced by the turbine is given by eq 6. ElecGBbt ¼
ð8Þ
AElecO ¼
∑t ðPeriodt ElecOt Þ ∑ Periodt " t Year
HEATMPFft ¼ MPFft ENFBD EFFFC " f " t
3.7. Rice Husk Availability. The total annual rice husk transported from each rice mill to the drying facility must not exceed the annual rice husk availability. This limitation is described by eq 19.
ð9Þ
The flow rate of LP steam, the heat transfer efficiency at IBD coil and the enthalpy change across IBD coil influence the heat supply to IBD coil by LP steam. HEATLPIit ¼ LPIit ENIBD EFFIC " i " t
ACRHJj e AARHJj " j
ð19Þ
The following constraints ensure that rice husk transported from each external rice mill to the drying facility does not exceed the specified annual rice husk availability.
ð10Þ
ACRHK k e AARHK k " k 3.6. CHF Heat Balance. The heat produced by CHF depends on the amount of rice husk fed into it, the rice husk calorific value and the efficiency of the CHF, as given by eq 11.
HEATCHFct ¼ CCRH RHCct ηCHFc " c " t
∑f HEATCFcft
þ
ð11Þ
ACRHJj ¼
∑t ðPeriodt RHJjt Þ ∑ Periodt " j Year
ð21Þ
t
∑i HEATCIcit " c " t
3.8. Selection of Cogeneration System. In this paper, an integer variable is assigned to ensure only one biomass boiler is purchased throughout the periods, as described in eq 22.
ð12Þ
∑b YPBb e 1
The dryer heat requirement of FBD is supplemented by MP steam extracted from turbine and CHF units.
∑c HEATCFcft " f " t
ð20Þ
In the rice processing industry, one season comprises of the peak period (involve drying) and off-peak period. Equation 21 represents the amount of annual rice husk transported from internal and external rice mills during both periods.
The heat produced by CHF is utilized to heat up FBD and IBD dryer air.
HEATFft ¼ HEATMPFft þ
ð18Þ
t
The heat supplied to FBD coil by MP steam is dependent on the flow rate of MP steam, the heat transfer efficiency of FBD coil and the enthalpy change across FBD coil.
HEATCHFct ¼
ð14Þ
Electricity is generated by cogeneration system.
ð4Þ
3.4. Turbine Work Output. The HP steam produced by the
HPbt ¼ MPbt þ LPbt " b " t
∑c HEATCIcit " i " t
ð13Þ
ð22Þ
3.9. Design Constraint for Cogeneration System. The hourly steam generated is governed by the boiler capacity, or 365
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
Figure 3. Case study of a local rice mill.
Table 1. Data on Operating Period item paddy drying season in a year (season/year) duration of drying period (peak period) (hour/season) duration of off-peak period(hour/season)
Table 2. Electricity Requirements of the Case Study Rice Mill value
electricity requirement (kWh)
2
section
peak-drying period
off-peak-drying period
basic operating (office +
290.0
290.0
720 3600
milling + cleaning)
the upper bound for boiler steam generation. To ensure boiler operability, a boiler must be operated above the boiler turndown ratio, which is typically 50% of the boiler maximum capacity. In eq 23, an Integer variable, YPBb is assigned to design constraints, to ensure the capacity constraint is observed for the selected boiler.
Equations 24 and 25 ensure that the MP and LP steam from the extraction-condensing turbine b are bounded by the given minimum extraction rates.
YPBb LWLPb e LPbt " b " t
ð25Þ
The CHF will only be purchased if only it operates at period t. Note that there is no limit on the number of CHF to be purchased. This constraint is formulated as eq 26. YOCct e YPCc " c " t
0
932.5
0
1502.2
290.0
the drying facility. The utility system sources of heat and power come from: (a) Cogeneration system to provide heat and electricity for dryers and rice milling equipment. (b) CHF to provide heat for process dryers which include FBD and IBD Table 1 shows that there are two six-months paddy growing seasons in a year. One growing season consists of growing and harvesting periods. Paddy must be dried to specified moisture content within 72 h after harvesting. The rice mill peak drying period of 30 days (720 h) coincides with the harvesting period. Table 2 shows that drying is not done during the off-peak period since no paddy is being harvested during the period. Table 3 gives the drying load for each dryer. Table 4 shows the annual rice husk availability from each milling location and the associated transportation and purchase costs. Note that the transportation cost vary with the distance between mills supplying rice husks and the mill consuming it. No purchase cost is imposed on the rice husk from internal rice mills. Table 5 shows the rice husk characteristics and utility equipment operating conditions for the various rice mill sections, including for dryers and cogeneration system. The annualized equipment capital cost is listed in Table 6.
ð23Þ
ð24Þ
279.7
IBD (40 units) total
YPBb LWBBb e ðHPbt Þ e YPBb UPBBb " b " t
YPBb LWMPb e MPbt " b " t
FBD (4 units)
ð26Þ
The rice husk consumption in a CHF is bounded by the given minimum and maximum values for the respective CHF unit, as described in eq 27. YOCt LWCHFc e ðRHCct Þ e YOCt UPCHFc " c " t
ð27Þ
4. CASE STUDY Figure 3 is a schematic of a proposed centralized drying facility for the rice mill under study. As shown in the figure, the potential rice husk supply network consists of 10 internal rice mills and 6 external rice mills. Rice husk is transferred from these rice mills to
5. RESULTS AND DISCUSSION The case study data was fitted into the developed MILP model and optimized using CPLEX solver of GAMS software (version 22.9) which ran on a personal computer using Microsoft Vista operating system. The solution time is 366
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
Table 3. Capacity of Dryers capacities
heat required
equipment
of dryer (tons/h)
for each dryer (MJ/h)
number of unit
required (MJ/h)
fluidized bed
18
8990
4
35 968
2
1069
40
42 760
Table 5. Rice Husk Characteristics and Utility Equipment Operating Conditions
total heat
item
value biomass
calorific value of rice husk (MJ/tons)
drier (FBD) inclined bed
cyclonic husk furnace
drier (IBD) total
13 000
78 728
efficiency (%) maximum input of feed rice husk (tons/hour)
80 1
turndown ratio (%)
50 biomass boiler
Table 4. Rice Husk Quantity and Transportation Cost section
annual available rice
transportation
purchase cost
husk (tons/year)
cost (RM/ton)
(RM/ton)
enthalpy change across boiler (MJ/tons)
720
efficiency (%)
76
turndown ratio (%)
50 condensing extraction turbine
internal rice mill 1
2375.00
20
2
3118.26
20
3
3027.33
30
4
2027.33
35
5
2375.46
50
6
2287.25
70
7
2086.47
70
8 9
4305.82 3336.70
120 230
10
2137.04
230
1
9142.50
20
5
2
7118.26
30
8
2386.00
33
8
2027.33
40
10
3345.0
enthalpy change of MP stream across FBD (MJ/tons)
2018.35
efficiency of FBD (%)
80
enthalpy change of LP stream across IBD (MJ/tons)
2244.29
efficiency of IBD (%)
80
equipment
4 6
230.66 324.36
Table 6. Annualized Capital Cost of Equipment
3
3698
66
enthalpy change across HP steam and MP steam (MJ/tons) enthalpy change across HP steam and LP steam (MJ/tons) dryers
external rice mill
5
thermal efficiency (%)
80 160
cyclonic husk furnace (per unit) cogeneration system with capacity 10 tons/h
10 10
0.063 s with 0.004287 relaxation gap. As for the model statistics, the number of constraints is 520, the number of binary variables is 60. The optimal rice-husk logistic network-utility network superstructure is shown in Figure 4. Notice from the figure that during the peak period, the optimal rice husk supply mix is selected from one internal rice mill and three external rice mills. On the other hand, the off-peak drying period consists of three internal and two external rice mills. These rice mills are selected as they are able to fulfill the rice husk demand and result in an optimal utility system at minimum cost. The optimal utility system consists of a 15 tons/hour boiler for the cogeneration system and six units of CHFs. The CHFs supply heat to all the FBDs and 16 units of IBDs. Apart from generating electricity, the cogeneration system supplies heat to the remaining IBDs. 5.1. Effect of Transportation Cost on Utility Systems Design. Figures 5 and 6 show that while the transportation cost increase by 60% as compared to the baseline value, the boiler size remains at 15 tons/h. However, as the transportation cost increment reaches 80%, a cogeneration system is no longer favorable. At this point, all the heat will be supplied by CHF and electricity will be outsourced from the national grid.
annualized capital cost (RM/year) 15 000 100 000
15 tons/h
150 000
20 tons/h
200 000
25 tons/h
250 000
30 tons/h
300 000
35 tons/h
350 000
40 tons/h
400 000
45 tons/h 50 tons/h
450 000 500 000
55 tons/h
600 000
Figure 7 shows the rice husk transport locations and the incurred overall cost which consists of rice husk cost, transportation cost, electricity cost and the annualized capital cost. Note from the chart that internal rice mill no.1 (j = 1) and no.2 (j = 2), as well as external rice mill no.1 (k = 1), are selected as the sources for rice husk supply. The figures show that within 60% of increment in transportation cost, the logistic network configuration will remain the same since the boiler size remains unchanged. Within the 60% increment of transportation cost, the overall utility system cost (consisting of a cogeneration system and CHF) is still lower in comparison to the baseline case. This is because electricity savings derived from the cogeneration system have outweighed the increase in total rice husk cost. Beyond 60% increment in transportation cost, it is no longer cost-effective to include cogeneration into the utility system. 367
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
Figure 4. Superstructure of the optimized rice husk logistic network and utility system configuration.
Figure 5. Rice husk consumption and boiler size with increase in transportation cost.
Note that, although the overall cost have exceeded the baseline case beyond the 60% increment in transportation cost, the rate of increment in the overall cost however, have become lower as the transportation cost increases up to 80%. This is due to the exclusion of cogeneration from the utility system
that subsequently requires less rice husk and lower transportation cost. 5.2. Effect of Electricity Tariff on Utility Systems Design. Figures 8 and 9 show that as electricity tariff increase from the baseline value, the boiler size, and consequently, the amount of 368
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
Figure 6. Heat and power generated in utility system with increase in transportation cost.
Figure 7. Logistic decision for rice husk transportation and overall cost with increase in transportation cost.
electricity generated is also increased. To minimize the impact of increasing electricity tariff, cogeneration has become a favorable option in fulfilling the heat and power demands. As a result, more heat loads of IBDs are supplied by the cogeneration system. Note that during the peak period more rice husks will be consumed by the utility system even though the drying load remains the same because of increased involvement of the cogeneration system. Unlike CHF which utilizes rice husk energy only as thermal energy for heating, cogeneration utilizes rice husk energy in the form of thermal and electrical energy. Consequently, cogeneration
tends to consume more rice husk than CHF to supply same amount of thermal energy. Note that, during the off-peak drying period more rice husks will be required as fuel for the cogeneration system to produce electricity. Figure 10 shows the selection of rice mills supplying the rice husks. As mentioned earlier, increased electricity tariff leads to increased rice husk demands and subsequently increased overall cost. The overall cost increase is due to the higher capital cost needed as a result of selection of a bigger capacity cogeneration system and also a higher operating cost because 369
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
Figure 8. Heat and power generated for utility system with increase in electricity tariff.
Figure 9. Rice husk consumption and boiler size versus increment in electricity cost.
of the larger amount of rice husk required. This outcome is expected since the electricity tariff increment tends to outweigh the total annualized capital cost, total rice husk cost as well as transportation cost. 5.3. Effect of Drying Load on Utility Systems Design. Table 7 shows that for the baseline case, 8 units of CHF are required to satisfy the heating requirement of dryers. With the incorporation of cogeneration into the utility system, only 6 units
of CHF are required as the remaining heat is supplemented by a 15 tons/h cogeneration system. As the drying load is increased, the number of CHF units and the cogeneration system boiler size is also increased. Figure 11 shows the cogeneration system capacity increasing with the drying load. As the drying load increases to 25%, the heat supplied by the cogeneration system remains the same, with the extra drying load being supplied by CHF. As the 370
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
Figure 10. Logistic decision for rice husk transportation and overall cost versus increment in electricity tariff.
Table 7. CHF and Cogeneration System Selection scenario
baseline
number of CHF units
8
boiler sizing (tons/h)
cogen
drying load +25%
drying load +50%
drying load +75%
drying load +100%
6
7
8
10
10
15
15
20
20
30
Figure 11. Heat and electricity generated versus increase in drying load. 371
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research
ARTICLE
drying load increases from 75% to 100%, the heat and power supplied by the cogeneration system are also increased.
ACRHJj = annual transportation of rice husk from internal rice mill j (tons/year) ACRHKk = annual transportation of rice husk from external rice mill k (tons/year) AElecO = annual electricity required from outsources (MWh) ElecGBbt = electricity generated from condensing extraction turbine b at period t (MWh) ElecDt = electricity demand at period t (MWh) ElecGt = electricity generated at period t (MWh) ElecOt = electricity required from outsources at period t (MWh) HPbt = HP steam from boiler b to condensing extraction turbine b at period t (tons/h) HEATCFcft = heat supplied by CHFc to FBDf at period t (MJ/ hour) HEATCHFct = heat supplied by CHFc at period t (MJ/hour) HEATCIcit = heat supplied by CHFc to IBDi at period t (MJ/ hour) HEATLPIit = heat supplied by LP stream at period t (MJ/hour) HEATMPFmt = heat supplied by MP stream at period t (MJ/ hour) LPbt = LP saturated steam extracted from condensing extraction turbine b period t (tons/h) LPCt = LP saturated steam extracted from condensing extraction turbine b to cooling tower at period t (tons/h) LPIit = LP saturated steam extracted from condensing extraction turbine b to IBD coil at period t (tons/h) MPbt = MP saturated steam extracted from condensing extraction turbine b at period t (tons/h) MPDt = MP saturated steam extracted from condensing extraction turbine b to dinternalstream process at period t (tons/h) MPFft = MP saturated steam extracted from condensing extraction turbine b to FBD coil at period t (tons/h) RHt = amount of rice husk consumed at period t (tons/h) RHBbt = amount of biomass feed into boiler b at period t (tons/h) RHCct = amount of rice husk feed into CHF c at period t (tons/h) RHJjt = amount of rice husk transfer from internal rice mill j at period t (tons/h) RHKkt = amount of rice husk transfer from external rice mill k at period t (tons/h)
6. CONCLUSION The design of an optimal rice mill utility system configuration with consideration of rice husk biomass logistic network is a complex problem that involves the trade-offs between various capital and operating costs for the overall rice mill utility system and its biomass fuel logistic network. By combining the economics and process data, optimization offers a comprehensive solution for the plant management to address this complex problem. A mathematical approach has been developed for simultaneous optimal planning of rice husk logistic network, as well as optimal design of a rice mill utility system that efficiently utilized rice husk from various locations, to satisfy the electricity and drying requirements of the rice mill at appropriate times of the year. A mixed integer linear programming (MILP) problem was formulated to determine the optimum cogeneration system size, the optimal rice husk logistic network as well as the optimal utility supply network. Solution to the MILP problem yielded an optimal utility system configuration consisting of a specified network of rice husk logistic network, a 15 tons boiler and 8 units of CHFs to satisfy the rice mill heat and power requirements. The method was tested on a case study involving a local rice mill in Malaysia. Sensitivity analysis was performed to assess the effects of variable changes on the utility systems design. Compared to the baseline, the optimal utility network managed to reduce 18.5% of the total rice mill annualized cost which include the costs of capital, fuel and electricity. ’ AUTHOR INFORMATION Corresponding Author
*Tel.: +607-5535478. Fax: +607-5588166. E-mail: haslenda@ cheme.utm.my.
’ ACKNOWLEDGMENT The authors would like to thank MOHE (Ministry of Higher Education) Malaysia and the company involved in this project. The work on this paper was funded by MOHE Malaysia and UTM under Vote No.Q.J13.2525.01H95). Also, the assistance provided by the company in the form of case study data as well as technical support has been instrumental in the success of this project.
Binary Variable
YOBbt = binary variable in operating biomass boiler b at period t YOCct = binary variable in operating CHF c at period t YPBb = binary variable in purchasing biomass boiler b YPCc = binary variable in purchasing CHFc
’ NOMENCLATURE
Parameter
AARHJi = annual available RH at internal rice mill j (tons/year) AARHKk = annual available RH from external rice mill k (tons/ year) BElecDt = basic electricity demand of drying equipment and office accessories at period t (kWh) CCOST = capital cost of equipment (RM) CCRH = calorific value of rice husk (MJ/tons) EFFFC = efficiency of FBD coil EFFIC = efficiency of IBD coil ElecBBbt = electricity requirement of biomass boiler and turbine b at period t (kWh) ElecCHFct = electricity requirement of CHF c at period t (kWh) ElecFft = electricity requirement of FBD at period t (kWh) ElecIit = electricity requirement of IBD at period t (kWh)
Subset
b = biomass boiler and turbine with different performance parameter capacity c = CHF with different performance parameter and capacity i = inclined bed dryer i j = rice husk from internal rice mill j k = rice husk from external rice mill k f = fluidized bed dryer f t = period Variable
ACCOSTBb = annualized capital cost of biomass boiler b (RM/ year) ACCOSTCc = annualized capital cost of CHF c (RM/year) 372
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
Industrial & Engineering Chemistry Research Electariff = electricity tariff (RM/MWh) ENBBb = enthalpy change across biomass boiler (MJ/tons) ENLPb = enthalpy change between HP and LP across condensing extraction turbine b (MJ/tons) ENMPb = enthalpy change between HP and MP across condensing extraction turbine b (MJ/tons) ENFBD = enthalpy change of MP across FBD (MJ/tons) ENIBD = enthalpy change of LP across IBD (MJ/tons) HEATFft = required heat of FBD at period t (tons/h) HEATIit = required heat of IBD at period t (tons/h) LWBBb = lower bound of biomass boiler b (tons/h) LWCHFc = lower bound of CHFc (tons/h) LWLPb = minimum extraction rate of LP stream at turbine b (tons/h) LWMPb = minimum extraction rate of MP stream at turbine b (tons/h) n = life span of equipment (year) PCRHKk = purchase cost of rice husk from external rice mill k (RM/tons) Periodt = duration of period t (hr) r = interest rate TCRHJj = transportation cost of rice husk from internal rice mill j (RM/tons) TCRHKk = transportation cost of rice husk from external rice mill k (RM/tons) UPBBb = upper bound of biomass boiler b (tons/h) UPCHFc = upper bound of CHF c (tons/h) Year = operating hours in a year (hr) ηBBb = efficiency of biomass boiler ηCHFc = efficiency of CHF ηTURBb = efficiency of condensing extraction turbine b
ARTICLE
(10) Hinman, N.; Schell, D.; Riley, J.; Bergeron, P.; Walter, P. Preliminary estimate of the cost of ethanol production for SSF technology. Appl. Biochem. Biotechnol. 1992, 34 (1), 639–649.
’ NOTE ADDED AFTER ASAP PUBLICATION After this paper was published online November 28, 2011, a correction was made to the affiliation line and to the spelling of author Sharifah Rafidah Wan Alwi's name. The corrected version was published November 30, 2011. Additional text corrections were made to this version, and a second revised version was published December 2, 2011.
’ REFERENCES (1) Operation Report; Padiberas Nasional Berhad: Selangor, 2008. (2) Nayak, P. Problems and prospects of rice mill modernization: a case study. J. Assam Univ. 1996, 1, 2228. (3) Soponronnarit, S.; Swasdisevi, T.; Wetchacama, S.; Shujinda, A.; Srisawat, B. Cyclonic rice husk furnace and its application on paddy drying. Int. Energy J. 2000, 1 (2), 67. (4) (a) Soponronnarit, S.; Prachayawarakorn, S. Optimum strategy for fluidized bed paddy drying. Drying Technol. 1994, 12 (7), 1667–1686. (b) Soponronnarit, S.; Yapha, M.; Pracbayawarakorn, S. Cross-flow fluidized bed paddy dryer: prototype and commercialization. Drying Technol. 1995, 13 (8), 2207–2216. (c) Sopornonnarit, S.; prachayawarakorn, S.; Sripawaatakul, O. Development of cross-flow fluidized bed paddy dryer. Drying Technol. 1996, 14 (10), 2397–2410. (5) (a) Phongpipatpong, M.; Douglas, P. Synthesis of rice processing plants. I. Development of simplified models. Drying Technol. 2003, 21 (9), 1595–1610. (b) Phongpipatpong, M.; Douglas, P. MINLP optimization. II. Development of simplified models. Drying Technol. 2003, 21 (9), 1611–1629. (c) Phongpipatpong, M.; Douglas, P. Sensitivity analysis. III. Development of simplified models. Drying Technol. 2003, 21 (9), 1631–1610. (6) Arivalagan, A.; Raghavendra, B.; Rao, A. Integrated energy optimization model for a cogeneration based energy supply system in the process industry. Int. J. Electr. Power Energy Syst. 1995, 17 (4), 227–233. (7) Uran, V. Optimization system for combined heat and electricity production in the wood-processing industry. Energy 2006, 31 (14), 2996–3016. (8) Gamou, S.; Yokoyama, R.; Ito, K. Optimal unit sizing of cogeneration systems in consideration of uncertain energy demands as continuous random variables. Energy Convers. Manage. 2002, 43 (912), 1349–1361. (9) Caputo, A. C.; Palumbo, M.; Pelagagge, P. M.; Scacchia, F. Economics of biomass energy utilization in combustion and gasification plants: effects of logistic variables. Biomass Bioenergy 2005, 28 (1), 35–51. 373
dx.doi.org/10.1021/ie101667j |Ind. Eng. Chem. Res. 2012, 51, 362–373
