Int. J. Biol. Sci. International Jou 2014 International Journal of

Int. J. Biol. Sci.
2014
International Journal of Biological Sciences (IJBS)
ISSN: 2313-3740
3740 (Online)
http://www.dnetrw.com
Vol. 01, No. 03,, p. 18-35, 2014
Dynamic Network for Research Works
RESEARCH PAPER
OPEN ACCESS
Optimizing Biogas Production From Cow Manure Using Genetic Algorithm &
Power Generation By CHP In Agricultural Farms In IRAN..
Mostafa Kamalinasaba, Professor. Alireza Vakilib
Departmant of Animal Scie
Science
nce Faculty of Agriculture, Ferdowsi University of
MashhadInternational Campus,
Campus Iran, P.O. Box 9177948974
bDepartmant of Animal Science Faculty of Agriculture, Ferd
Ferdowsi
owsi University of Mashhad,
Mashhad Iran,
P.O. Box 9177948974, [email protected]
a
Keywords: Genetic
enetic algorithm, optimization, Biogas, CHP system
system, Economic model
Abstract
The optimization of biogas production with respect to external influences and various process disturbances
is essential for efficient plant operation. However, the
the optimization of such plants is a challenging issue
due the underlying nonlinear and complex digestion processes. One approach to solving this problem is to
use the flexibility and power of computational intelligence methods such as Genetic Algorithms (G
(GAs).
The present study utilizes GA as tools for simulating and optimizing of biogas production process.
Considering the effect of digester operational parameters, such as temperature (T), total solids (TS),
volatile fatty acid (VFA), pH and A/TIC
A/TIC-ratio (amount
ount of Acids (A) compared to Total Inorganic Carbon
(TIC)), the
he optimal amount of biogas was converged to be 53910 cubic meters per month
month. In order to reach
the main goals on the energy problems, it is important to study and analyze the distributed CHP plant
pl
for
agricultural companies and farms. This paper also describes a feasibility study of a biogas CHP plant in a
cow farm in Iran. With the developed model, it is specified that using 53,910 cubic meters of biogas, an
internal combustion engine with elec
electrical
trical power of 375 kW can be operated continuously. Obtained
results illustrated how the utilization of gaseous product from cow farm effluent (biogas) as fuel for heat
and power generation can reduce primary energy consumption and its associated costs.
Corresponding Author’s Email: [email protected]
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Nomenclature
CHP
Thermal rated capacity of biogas CHP
Hr
system(kW)
Qmax
Maximum heat demand of the farm in a
sample year(kW)
CHP
Electrical rated capacity of biogas CHP
Cr
system (kW)
Nomenclature
CCON
Annual
energy
cost
of
the
conventional system ($)
CBGCHp
Energy cost of biogas CHP system($)
η CHP
th
Thermal efficiencies of the CHP system
C CON
η CHP
e
Electrical efficiencies of the CHP system
C CON
BG
Amount of biogas needed to supply the
farm's loads(m3)
Thermal energy demand of the farm per
month (kWh)
C CON
Input energy needed to supply thermal
loads per month(kWh)
Heating value of biogas(kWh/m3)
C BGCHP
Energy produced by biogas per month
(kWh/month)
Amount of biogas produced from livestock
wastes (m3/month).
Thermal energy that should be supplied by
gas oil(kWh)
Gas oil’s heating value (kWh/liter)
C BGCHP
HA
Enin
HRB
G
EnBG
BGAD
Eno
HRG
S
Fuel
Inv
C CON
Maint
Ele
Gasl
Inv
C BGCHP
Maint
Ele
C BGCHP
Gasl
Elesell
C BGCHP
TC
TB
Amount of gas oil required (Liter/month)
EAi
Pele
Gasoili
Esell
EA
Ebuy
CSR
Operation hours
The total amount of electricity generated
during one month by the CHP system
(kWh)
Electricity sold to the grid (kWh)
The electricity demand of the Farm (kWh)
Electricity purchased from the grid (kWh)
Cost Saving Ratio
VD
The volume of digester
h
CHP
E total
1
2014
PGasl
Pelesell
CSave
Paybac
k
CHD
Investment cost for conventional
system(including the costs related to
purchasing power meter and heating
equipment) ($)
Annual
maintenance
cost
of
conventional system($)
Annual costs of the electricity
purchased for conventional system($)
Annual costs of gas oil consumption
for the conventional system($)
Investment costs of biogas CHP with
a storage tank (including the costs of
purchasing
equipment
and
accessories required for installation)
($)
Annual maintenance cost of biogas
CHP system($)
Grid electricity cost for the biogas
CHP system($)
Gas oil cost for the biogas CHP
system($)
Benefit of selling electricity to the grid
for biogas CHP system($)
Life time of conventional system
(year)
Life time of biogas CHP system (year)
Animal farm’s electricity demand in
ith month(kWh)
Utility electricity price ($/kWh)
Demand of gas oil of the farm in ith
month(Liter)
Gas oil price ($/Liter)
Electricity buyback price
Energy cost saving($)
Payback period (year)
Investment cost of digester ($/m3)
Introduction
Genetic Algorithms (GAs) were mainly developed in the 1970sas an optimization toolbox,
although some work had already beendone in the field of evolutionary computation. In 1967,
Bagley (Bagley J.D. 1967)introduced the words ‘‘genetic algorithm” and published the
firstapplication of GAs. However, the first main works related to Gasare attributed to Holland
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(Holland J.H. 1975.) and De Jong (De Jong K.A. 1975), in 1975. In the1980s,
Grefenstette(Grefenstette J.J. 1986), Baker (Baker J.E. 1987) and Goldberg (Goldberg D.E.1989)
contributed tosignificant advancements in GAs. Ref. (Goldberg D.E. 1989) presents a good
pictureof the state of art in 1989. A more complete history of GAs andother evaluative
computation methods is given in (Back T. et al. 1997).However, the interest in and the utilization
of GAs in the field biogas production is more recent. H. Abu Qdais et al.developed a diagnostic
model based on genetic algorithm (GA) to simulate the production of biogas from the digester of
biogas plant in Jordan. The study demonstrated that GA is useful tools for simulating and
optimizing the biogas production from biogas digester under various operational
conditions(Abu H. et al. 2009)
Reducing fossil fuels and sustainable development issues have prompted researchers to
achieve renewable and less polluting shapes of energy. Nowadays, increase of organic waste is
an important problem. If this organic waste isn’t properly accumulated, it can become a serious
environmental issue. Quality care and bringing the waste in anaerobic digestion process is an
effective solution for this problem. Anaerobic digestion is the fermentation of organic material
without the presence of oxygen to produce biogas. The resulting biogas is used for production of
heat, electricity or both of them (Combined Heat and Power - CHP). Also, utilizing biogas in
engines, compared to fossil fuels, avoids any additional greenhouse gas emission. Due to organic
nature of the components of biogas, burning it in a gas engine for power generation emits the
same amount of CO2 into the atmosphere as the originally absorbed during the process of
photosynthesis in the natural CO2 cycle (Roberto CH. 2007).Growth and concentration of the
livestock industry create opportunities for the proper disposal of the large quantities of manures
generated at dairy, swine, and poultry farms. Pollutants from unmanaged livestock wastes can
degrade the environment, and methane emitted from decomposing manure may contribute to
global climate change. Such a management system can not only provide pollution prevention,
but also convert a manure problem into a new profit center. Economic evaluations and case
studies of these management systems indicate that the anaerobic digestion (AD) of livestock
manures is a commercially available bioconversion technology with considerable potential for
providing profitable byproducts including a cost-effective renewable fuel for livestock
production operations. More than two decades of research has provided much information
about how manure can be converted into an energy source (Lusk P. 1998). The most common
biomass technologies for animal manures are combustion, anaerobic digestion, and composting.
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Dry manure is produced by feedlots and livestock corrals, where the manure is collected and
removed only once or twice a year. Manure that is scraped or flushed on a more frequent
schedule can also be separated, stacked, and allowed to dry. Dry manure is typically defined as
having moisture content of less than 30%. Dry manure can be composted or used as fuel for
biomass energy generation systems. Wet manure is typically associated with larger and more
recent dairy operations that house their milking cows in free stall barns and use a flush system
for manure collection. The combination of free stall barns and manure flushing collects all of the
milking cows’ manures with every milking cycle, e.g., two or three times a day. The manure is
significantly diluted by addition of the flush water, but after separation of some of the flush
water, the slurry is an excellent fuel for biomass energy conversion through anaerobic digestion
technology (Beck
R.W. 2003).Various literatures havebeen reported on experimental and
operational research on CHP systems, particularly the biogas CHP system. In the European
Union the leading countries in biogas energy field are Germany, Denmark, Austria and Sweden.
For instance, Germany has increased its number of biogas-based power plants from 1,050
installations in 2,000 to 6,000 installations in 2010, i.e. by almost 20% increase per year (Wojciech
M. et al. 2011). England produces more than 108 tons of organic materials including food waste,
sewage sludge and energy crops per year, which can be used for producing biogas. This country
plans to produce 0.8 terawatt hours (TWh) of electricity per year from biogas through an
anaerobic digestion process in combined heat and power (CHP) plants (Defra. 2009). Arias (Dos
Pinos. 2009) studied the technical and economic feasibility of electricity generation with biogas
in Costa Rica,they indicated that the technical feasibility to install a low cost, integrated biogas
system for manure management is a reality, including generating electricity with biogas. In
Austria as well as in Germany, the rules and procedures of the renewable segment are currently
subject to fundamental changes (ENERGY.GOV. 2010,Razbani O. et al. 2011,CENERGY. 2012) to
fully exploit the market-driven potential of biogasCHP technologies.The use of biogas in internal
combustion engines dated back to Second World War when thousands of vehicles ran by sewage
gas in Europe. In 1942, garbage collection trucks with diesel engines were operated using
purified and compressed sewer gas in Zurich, Switzerland (EBA. 2011). In 1981 an effort has
been made to use biogas in a converted diesel engine to SI engine by D.J. Hickson. He
experienced 35% less power compared to diesel and 40% less compared to gasoline fuel (Thring
R.H. 1985). In that year another research was done by S. Neyeloff and W.W. Cunkel. They used a
CFR engine and ran it with simulated biogas in different compression ratios. They reach to
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compression ratio of 15:1 for optimal solution (NeyeloffS, Cunkel W. 1981). V. Deri and G.
Mancini converted a diesel engine to dual fuel and experienced more stable combustion in lean
mixture because of using diesel pilot to ignite the mixture. However, the control strategy for
separate control of the air-gas mixture and pilot fuel became too complex (Deri V, Mancini G.
1990).
2
Anaerobic Digestion Process
An anaerobic digestion system typically comprises of the following components: A solids
removal process to remove suspended solids and other indigestible or dangerous materials in
the waste which is usually achieved using one or more of the following technologies: a screw
press, belt press, centrifuge, lamella separator or dissolved air flotation system. The pretreatment
tank corrects the PH of the waste and acts as a buffer tank to continuous process reactor tanks
(Brent A. 2008). A post treatment stage to remove sludge produced in the anaerobic tank or to
further polish the organic strength of the waste. The biogas produced is then stored is a suitable
vessel and consumed in a CHP system. There are a wide range of anaerobic technologies
available to digest waste, from simple lagoons to up flow anaerobic sludge blanket and
expanded bed reactor systems. Simple technologies tend to require higher hydraulic retention
times, which require more space for larger reactor tanks. However the advantages include that
they are more robust and less expensive. The most suitable technology is dependent on the
composition and volume of the waste to be treated and space restriction on site. The volume and
methane content of the gas produced is dependent on the digestibility of the waste and the
reactor used to digest the waste. The digestibility and levels of methane produced can be
determined through laboratory testing with various suitable technologies (Brent A. 2008).
Manure is a valuable source of energy. Farm animals are responsible for almost a fifth of the
pollution causing global warming according to the UN report “LLS”, and methane released from
manure is 21 times more harmful to the ozone layer than CO2. However, processed correctly,
manure also represents a huge potential for sustainable energy and fertilizer production, while
at the same time, reducing greenhouse gas emissions up to 85%. Advanced biogas production
technology results in a fuel that is efficient and environmentally friendly. A mixture of methane
(CH4) and carbon dioxide (CO2) provides sufficient calorific value to operate biogas cogeneration
plants (Giraldi D. et al. 2011). The gas produced by anaerobic digestion is usually more than 60%
methane, and some plants with state of the art facilities have the potential to produce biogas
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with concentration of methane up to 95% (Dos Pinos. 2009). Biogas combustion temperature is
about 700 °C (gas oil combustion temperature is 350 °C and petroleum is 500 °C) and the flame
temperature of it is about 870 ° C.
Biogas Plant Description
3
The farm evaluated in this study is located in Iran, which has 3055 dairy cows. The total surface
area of this farm is about 35,000 square meters. The building was built in the 1990s, was wellinstalled and previously equipped with a conventional boiler. The principal heat demand of the
farm was for space heating and for supply of hot water. Parameters measured that are
considered essential in developing an economic model for the biogas CHP system include fuel
consumed, electrical and thermal power output.
Methodology
4
The main objective of this paper is to use the Genetic algorithm as a tool for optimization of
methane biogas from a digester of biogas plant. The biogas production model (] Subramani T,
Nallathambi M. 2012) is employed in optimizing the production of methane from the reactor by
using the Genetic algorithm tool (GA).
After that, a mathematical model is developed within the commercial software package of
MATLAB (Hongbo RN. et al. 2008).The model is developed by fitting polynomial equations to
heat and electrical power, in order to assess the technical and economic feasibility ofbiogas CHP
plant (biogas coming from the cow manure). Biogas fuelled CHP plant is the core of the
developed waste-to-energy plan.
4.1
Energy Audit
Analyzing the electricity consumption indicates that the farm electricity demand is about 55.082
MWh/month.As shown in Table 1 the electrical and thermal power consumptions of biogas
production unit are 14.6 kW and 24.9 kW, respectively. The maximum demands of electrical and
thermal energy for each month are illustrated in figure 1.
Table 1: The amount of power consumed by biogas plant's equipment
Consumers
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Power (kW)
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Mixers
13.5
Pump
0.6
Air compressor
0.5
Total electric power consumption
14.6
Heat for heating food
16
Heat loss through the bioreactor's wall
8.9
Total power consumption for heating
24.9
Maximum electrical power
850
110
750
100
650
90
550
450
80
350
70
250
Electrical power (kW)
Thermal power (kW)
Maximum thermal power
60
150
50
50
Month
Figure 1: Monthly Maximum electrical and thermal power requirements of the farm
5
Description of Model Using Genetic Algorithm
In this paper the aim is to obtain the optimal combination of the digester operational parameters
for maximum methane production; the biogas production model (Brent A. 2008) was integrated
with a Genetic algorithm (GA) model. GA is a biologically inspired computational model that
imitates the natural processes of evolution and adaptation to exhibit a complex computational
behavior (Holland JH. 1975,Goldberg DE. 1989). This computational model does not require
prior knowledge of the solution space. This feature limits the designer and makes problems
more complex and insufficiently understood. In engineering design, the computational model
using the genetic algorithm differs fundamentally from using the traditional method. In the
traditional approach the design problem is modeled as mathematical problem and use
mathematical solution technique, but in the genetic algorithm no need to separate the modeling
and solution parts (Abu H. et al. 2009,Gen MC. 1997).To achieve the optimum amounts of biogas
produced, the model was utilized in calculating the values of the fitness function for the GA
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routine available in MATLAB Genetic Algorithm toolbox. The GA model was run with
constraints with a population size of 100 and crossover fraction equals to 0.85. This has resulted
in the production of biogas up to 53,910 cubic meters per month. After that, sensitivity analysis
on the model is performed to understand the influence of the key parameters on the
enhancement of the optimization. For biogas calculations model, temperature (T), total solids
(TS), volatile fatty acid (VFA), pH on the biogas and A/TIC-ratio amount of Acids (A) compared
to Total Inorganic Carbon (TIC) are important factors. It was indicate that changing the
constraints of the GA problem has an important effect on the enhancement of the optimization.
Lower and upper boundaries were changed iteratively and the values of fitness function were
studied according to the corresponding changes. Values of the best combination of biogas
parameters are also summarized in Table 2. These values were selected according to the
maximum amount of biogas production as obtained by GA optimization process. Figure2shows
themaximum and average amounts of biogas produced in each iteration.
Table 2: Optimal values of digester operational parameters for maximum biogas production as
determined by GA optimization process.
Parameter
Optimal value
Temperature (◦C)
36
Total solids(mg/l)
7.94
Volatile fatty acid (VFA)
(mg/l)
1349.71
pH
7.06
A/TIC
0.5
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Figure 2:: Maximum and average amounts of biogas produced in each iteration
6
Sensitivity Analysis
Sensitivity analysis improves understanding the influence of key parameters on the biogas
production model. Temperature (T), pH, total so
solid
lid (TS) and volatile fatty acid (VFA) are
significant factors that show optimum production of biogas. In this study, sensitivity analysis
has been performed on pH and volatile fatty acid (VFA).The biogas production changes with pH
and VFA changes have been
n appraised for a sample cow farm. It should be mentioned that, in
this analysis temperature (T), total solids (TS) and A/TIC
A/TIC-ratio and other parameters affecting
the biogas production rate are considered constant.
Among many factors that effect on the biogas
biogas production pH and VFA are selected in this
sensitivity analysis. In order to determine the effect of these two parameters, it is essential to
perform a sensitive analysis based on simultaneous changes in pH and VFA. So in this part, pH
changes from 6 to 7.5 and VFA changes from 800 to 1350, and biogas production changes are
calculated. ATC, temperature and TS is considered constant.Figure 3 have shown the intuitive
results that biogas production is quite sensitive to pH and VFA.
Figure 3:: biogas chan
changes with pH and VFA changes
From figure 3 it can be observed that, given a fixed TS, biogas production rate would begin to
rise when pH and VFAare increased. The maximum value of biogas occurs at the point of 7.4
and 1345 (mg/l) for pH and VFA,, respectivel
respectively. It is not surprising that increases in pH and VFA
result in corresponding increase in biogas rate.
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Economic Assessment And Description The Developed Model
However, cogeneration systems for commercial or institutional applications benefit from the
thermal/electrical load variety in the multiple loads served, decreasing theneed for storage.
Based on the magnitude of the electrical and thermal loads, whether they match or not, and the
operating strategy adopted, the cogenerationsystem may have to be run at part-load conditions,
the surplusenergy (electricity or heat) may have to be stored or sold, andshortages may have to
be made up by purchasing electricity (orheat) from other sources such as the electrical grid (or a
boilerplant). The extra heat produced can be stored in a thermal storagedevice such as a water
tank, while extra electricity can be stored inelectrical storage devices such as batteries or
capacitors. In addition,the performance of a cogeneration system may be dependenton
fluctuating electricity prices, making cogeneration systemsfinancially attractive in periods of
high electricity prices (Bios. 2013).Figure 4 illustrates the power generation and heat recovery
unit with a biogas CHP plant.Data required for modeling biogas CHP plant is summarized in
Table 3.
Figure 4: Producing electricity and heat in a CHP plant using biogas from livestock waste (Bios. 2013)
Table 3: Data assumptions for the analysis (Gen MC. 1997, Bios. 2013, TeymouriHamzehkolaei F,
Sattari S. 2011)
Item
Biogas CHP and equipment
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Assumed data
Investment costs ( $/kW)
Annual maintenance costs ($/kWh)
Electrical Efficiency (%)
Thermal Efficiency (%)
Lifetime (year)
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Quantity
(200-500)
(0.003-0.007)
(20-25)
(60-65)
20
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Electricity meter's cost $)
12000-15000
average Initial cost of boiler (boiler,
burner, accessories equipment ($/ thermal
8-10
Cost of buying electricity meters, kilowatt))
20
heating equipment and annual
Boiler lifetime (year)
70
maintenance cost
2000-3000
Boiler Efficiency (%)
Annual maintenance costs of conventional
systems ($)
Biogas digester
number of dairy cows
Digester volume (cubic meters)
Biogas produced (cubic meters per hour)
Investment cost in a cubic meter of
digester volume ($/m3)
Annual maintenance costs ($)
3055
443
75
(100-200)
5000-6000
Energy prices and specifications Electricity purchasing price ($/kWh)
Electricity Buy-back price ($/kWh)
Gas oil purchasing price ($/liter)
Heating value of gas oil (kWh/liter)
Real interest rate (%)
Heating value of biogas (kWh/m3)
Others
0.025
0.05
0.15
11
10
6
Economy is one of the most important considerations for the development of biogas CHP
systems. The economic assessment gives information on how the economic resources
(investments, fuels, etc.) are used to meet the customer requirements. It is known that biogas
CHP system has usually higher initial investment and lower running cost compared with the
conventional energy systems, which serves the electricity load by utility grid and thermal load
by gas boiler.The proposed model can investigate the validity of the system from both economic
and technical aspects(TeymouriHamzehkolaei F, Sattari S. 2011). In the developed model,
electrical and thermal rated capacity of the system are determined using (1) and (2):
CHP
Hr
CHP
Cr
=
=
Q max
η CHP
th
(1)
H
CHP
CHP
r
e
CHP
th
×η
η
(2)
Since the biogas CHP system operates based on heat led, it should provide thermal load at
first and electricity is the secondary product. The total amount of energy that is required for all
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thermal loads and the amount of biogas needed to supply this energy can be calculated using (3)
and (4):
Enin =
HA
η CHP
th
BG =
(3)
Enin
HRBG
(4)
If the amount of biogas produced by anaerobic digestion of the cow manure cannot provide all
the needs of the animal farm, other fuels should be used to provide thermal loads. Since gas oil
is used as input fuel in most Iran's farms, it is considered as alternative fuel for biogas in this
paper. Energy produced by biogas per month, denoted by EnBG (kWh/month), is obtained by (5):
En BG = BG AD × HR BG
(5)
Thermal energy that should be supplied by gas oil, denoted by Eno (kWh), is calculated from (6),
and the amount of gas oil required is obtained from (7):
Eno = Enin − EnBG
Fuel =
(6)
Eno
HR GS
(7)
With respect to thermal loads of the farm and thermal rated capacity of the biogas CHP system,
the operation hours of the CHP system in one month, denoted by h, is determined as follows:
HA
h=
H
CHP
r
(8)
The total amount of electricity generated during one month by the CHP system, denoted by
CHP
E total (kWh), is calculated according to the rated electrical capacity and hours of operation of the
system:
CHP
CHP
E total = C r × h
(9)
If the amount of generated electricity by the CHP system exceeds the demand of users, the extra
electric power can be delivered to the grid as shown in (10). Otherwise, when the generated
electric power is less than the demand, the grid can support the lack of electricity as illustrated in
(11):
CHP
E sell = E total − EA
if
CHP
if
Ebuy = EA − E total
CHP
E total > EA
EA > E CHP
total
(10)
(11)
In economic evaluation, an important index, i.e. cost saving ratio or profitability index, is
employed. It expresses the profitability of the system and is defined as the ratio of total energy
cost difference between the biogas CHP system and the conventional system to the annual
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energy cost of the conventional system, as illustrated in (12) (Giraldi D. et al.
2011,TeymouriHamzehkolaei F, Sattari S. 2011):
CCON − CBGCHP
) ×100
CSR = (
CCON
(12)
I
Inv
×
CCON = (C CON
1− (
Maint
Ele
) + C CON
+ C CON
+ C Gasl
CON
1
(1+ I )TC
(13)
I
Inv
C BGCHP = (C BGCHP ×
)
1− (
1
(1+ I )TB
Ele
Gasl
Elesell
) + C Maint
BGCHP + C BGCHP + C BGCHP − C BGCHP
)
(14)
System investment cost of conventional system and biogas CHP plant is included in terms of
annualized capital cost in (13) and (14). This is the process of spreading the initial cost of a
system (based on accounting the time value of money) across the lifetime of that system. The
process of annualizing capital cost is similar to pay off the capital cost of a system by a loan at a
special interest of discount rate (I) over the life time of that system. The result of this process is a
future value cost or constant annual cost of the capital (Giraldi D. et al. 2011). Since, selling
electricity to the grid has economic benefit, it is considered as a negative cost in the objective
function of the model.
Annual operation cost of the conventional system for purchasing electricity and gas oil are
described in (15) and (16), respectively. The total cost of electricity purchased for the
conventional system is calculated by the annual amount of electricity purchased multiplied by
the utility electricity price. The fuel cost is calculated by the cumulative fuel consumption for the
gas boiler multiplied by the fuel rate.
12
Ele
C CON = (∑i =1 EAi ) × P ele
(15)
12
Gasl
C CON = (∑ i =1Gasoili ) × P Gasl
(16)
The CHP system's fuel cost is calculated considering the amount of gas oil consumed by the
CHP system, in addition to biogas, multiplied by the gas oil price as illustrated in (17):
12
C BGCHP = (∑i =1 Fueli ) × P Gasl
Gasl
(17)
As mentioned before, when the amount of generated electricity by biogas CHP system exceeds
the demand of users ( E CHP
total > EA ), the surplus electricity can be delivered back to the grid.The
income from selling electricity back to grid is described by (18). It is calculated with total amount
of electricity sold to the grid multiplied by the electricity buyback price.
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12
C BGCHP = (∑i =1 E selli ) × P elesell
Elesell
(18)
However when the electricity demand exceeds the amount of electricity generated by biogas
CHP plant ( E CHP
total < EA ), the electric utility can provide electricity shortage as illustrated in (19):
12
C BGCHP = (∑i =1 E buy i ) × P ele
Ele
(19)
Another important economical index is energy cost saving, denoted by C Save , which is defined
as the difference between annual energy costs of conventional system and biogas CHP system as
follows:
Ele
Ele
Gasl
Elesell
CSave = (C CON
+ C Gasl
CON - (C BGCHP + C BGCHP - C BGCHP ))
(20)
Eventually, Payback period (year) is obtained from the ratio of biogas CHP’s installation cost
toenergy cost saving.
Inv
C
Payback = ( BGCHP )
C Save
8
(21)
Simulation Results
With the developed model and data obtained from the animal farm, it is specified that using
53,910 cubic meters of biogas per month (produced by the waste to energy conversion system),
an internal combustion engine with electrical power of 375 kW can be operated continuously.
Also, using the proposed economic model, annual cost saving C Save , system profitability index or
cost saving ratio (CSR), and payback period are calculated as 96764 $, 73.96% and 22 months,
respectively, without considering greenhouse gas reduction.
Ele
Ele
Gasl
Elesell
CSave = (C CON
+ C Gasl
CON - (C BGCHP + C BGCHP - C BGCHP )) = (16525+67419-(0+37276- 50096))=96764
CCON − CBGCHP
88858 − 23141
CSR = (
) ×100 = (
) ×100 = 73.958%
CCON
88858
Inv
179455
C
Payback = ( BGCHP ) = (
) = 1.85 year = 1.85 × 12 = 22Month
96763
C Save
Inv
Inv
CHP
C BGCHP = (C CHP × C r ) + (V D ×CHD ) = (260 × 375) + (443 ×185) = 179455($)
Monthly electrical energy produced, electricity sold to the grid, gas oil purchased and cost
saving are shown in table 4. The amount of electricity sold to the grid in different months is
shown in figure 5.
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Table 4:: Electricity produced, electricity sold to grid, gas oil purchased and cost savings
Month
Electricity
produced (kWh)
January
February
March
195120
192290
130260
April
123060
May
116120
June
108180
July
August
September
Electricity sold to
grid (kWh)
Demands of
Biogas(m3)
153360
155402
75204
Gasoil
purchased (Liter)
Cost
savings ($)
130080
128190
86837
41547
40517
17960
9843.7
9864.6
7074.5
65028
82043
15345
6728.4
54368
77413
12820
6372.2
43452
72123
9934.2
5998.3
106700
108680
125760
38252
41720
69600
71131
72453
83840
9393.1
10115
16325
5846
5958.8
6877.8
October
156240
102240
104160
27409
8072.8
November
December
171600
183120
120480
137040
114400
122080
32995
37184
8721.6
9282.9
Electricity Sold to Grid (kWh)
160000
140000
120000
100000
80000
60000
40000
20000
0
Month
Figure 5:: Monthly electrical energy sold to the grid
The biogas CHP system provid
provides
es total electrical demands of the animal farm and biogas
production unit. Additionally, in each month, excess electricity is delivered to the grid, as shown
in table 4,, which increases the system's profitability. In this modeling, monthly production of
biogas
ogas is considered to be constant. As 53,910 cubic meters of biogas per month cannot provide
all thermal and electrical energy requirements, some surplus input energy is needed, which
should be provided by gasoil as indicated in Table 4.
9
Conclusion
In this
is study a mathematical model based on genetic algorithm wasdeveloped to simulate the
production of biogas from the digesterof biogas plant in Iran.Integrationthe
Iran.
the biogas production
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Int. J. Biol. Sci.
2014
model with a GA modelresulted in identification ofthe optimal operational digester parameters
that lead to increasethe production of biogas up to 53,910 cubic meters per month.The
operational conditions that resulted in the optimal methane production were determined as
temperature at 36 ◦C, TS 7.94%, VFA 1349.71, pH 7.06 and A/TIC 0.5.Using sensitivity analysis
on the model the influence of the key parameters on the enhancement of the optimization is
determined. It is demonstrated that for biogas calculations model, temperature (T), total solids
(TS), volatile fatty acid (VFA), pH on the biogas and A/TIC-ratio are important factors. It was
indicate that changing the constraints of the GA problem has a direct effect on the enhancement
of the optimization. Lower and upper boundaries were changed iteratively and the values of
fitness function were studied according to the corresponding changes and shows with analytical
figures.
Finally, economic assessment shows that, cogeneration systems for commercial or
institutional applications benefit from the thermal/electrical load variety in the multiple loads
served, decreasing theneed for storage. With the developed model and data obtained from the
animal farm, it is specified that using 53,910 cubic meters of biogas per month (produced by the
waste to energy conversion system), an internal combustion engine with electrical power of 375
kW can be operated continuously. Also, using the proposed economic model, annual cost
saving, system profitability index or cost saving ratio (CSR), and payback period are calculated
as 96764 $, 73.96% and 22 months, respectively, without considering greenhouse gas reduction.
10
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