Design and Process Simulation of a Small Scale Waste-To-Energy Bioreactor
Noor Ezlin Ahmad Basri
Sharom Md Zain
Transformation of organic waste to energy is a complex process
which involves hundreds of possible intermediate compounds and reactions. Simulation
process can provide suitable assessment of a bioconversion system before its
actual fabrication and commercialization. In addition, process simulation is
a suitable tool for optimization of technical factors (e.g., bioreactor configuration
and conversion units design) and economical factors (e.g., throughput, production
cost, revenue and gross margin). In this study, biodegradation process of a
small-scale anaerobic bioreactor was simulated using SuperPro Designer®.
Process simulation flow sheet, materials registration and process reactions
were conducted during model setup. Next, model validation was carried out by
comparison between the outputs and actual data achieved during experimental
phase. Results show the appreciable agreement between predicted and actual data
due to appropriate process definition during simulation. Two different bioreactor
configurations were investigated and efficiency and performance of each pattern
was tested based on biogas production rate, methane content in biogas and biochemical
and chemical oxygen demands (BOD and COD) removal efficiencies. Finally, economical
analysis was performed in the model which indicates, biogas production from
organic waste in a single small-scale bioreactor is a promising method for renewable
Received: September 11, 2012;
Accepted: November 14, 2012;
Published: January 10, 2013
For future world stability, investigations on alternative energy are significant.
Renewable energy sources (mainly organic waste materials to energy) likely will
become one of the most attractive substitutes in the near future (Kothari
et al., 2010). Transformation of organic waste to energy is the product
of anaerobic digestion. Anaerobic digestion is a biological process of converting
separated biodegradable solid wastes into methane gas (Malakahmad
et al., 2011). Methane is sourced from decomposition of organic material,
such as cattle slurry, food scraps and sewage (Malakahmad
et al., 2008). It has similar thermal characteristics to natural
gas and, once the quality is upgraded it can be injected into the gas grid.
Methane gas can be utilized as a renewable energy source to supply heat and
electricity. In addition, waste-to-energy transformation is been recognized
as an important and vital element in any Integrated Solid Waste Management (ISWM)
plan (Malakahmad et al., 2010).
In general, anaerobic digestion is considered to occur in (1) liquefaction,
(2) acid formation and (3) methane formation stages. Organic wastes consist
of complex organic polymers such as proteins, fats, carbohydrates, cellulose,
lignin, etc., some of which are in the form of insoluble solids. In liquefaction
stage this organic polymers are broken down by extracellular enzymes produced
by hydrolytic bacteria, and dissolves in water. The monomeric compounds released
by the hydrolytic break down due to bacterial action in liquefaction stage are
further converted to acetate, H2 and CO2 by the acetogenic
bacteria in acid forming stage. The products of the acid forming stage are finally
converted to energy in form of CH4 and other end products in the
methane formation stage. Numerous of possible intermediate compounds and reactions
are existed in anaerobic digestion of organic materials. Therefore, process
design and simulation can be applied to control the overall process conditions
and to investigate the optimum level of important factors such as temperature,
moisture content, pressure, Organic Loading Rate (OLR), pH, etc. The best range
of these important factors can be found by running the simulated process several
times to achieve the best efficiency of system before its actual design, fabrication
End products can be utilized by process improvement and renovating manufacturing
operation. Process simulation is an important tool in this venture (Zeinali
et al., 2009). The benefits of simulation for bioprocess improvement,
assessment and expand have been realized previously (Evans
and Field, 1986; Cooney et al., 1988; Petrides,
1994). Basically, process development is shortened by application of process
models and simulators. They allow comparison of process alternatives on a consistent
basis so that a large number of ideas can be synthesized and analyzed interactively
in a short time. In addition, study of occurrence interaction between upstream
and downstream processes will be possible through an integrated simulation (Petrides
et al., 2002; Thomas, 2003). They provide some
tasks such as; represent the entire process on the computer, perform material
and energy balances, estimate the size of equipment, estimate the cycle time
of the process and perform cost analysis. SuperPro Designer®
is Windows-based software which can be used to design and analyze unit operations
for water and wastewater treatment, air pollution control and industrial applications.
By combination of manufacturing and environmental operations in the SuperPro
Designer®, the user is able to design and assess manufacturing
of the product and decide on treatment methods, pollution prevention and waste
minimization approaches, at the same time. Application of SuperPro Designer®
has been reported for process simulation in production of polyhydroxyalkanoates
(Akiyama et al., 2003), monitoring of biopharmaceutical
facility (Toumi et al., 2010; Pedrites
et al., 2011), fuel ethanol production (Kwiatkowski
et al., 2006) and biodiesel production costs analysis (Haas
et al., 2006).
The aim of this study is to design, simulate and optimize production of renewable
energy from biodegradable waste in a laboratory scale bioreactor using SuperPro
MATERIALS AND METHODS
Process details: The first step in building a simulation model is collection
of information about the process. In this study, a laboratory-scale anaerobic
baffled bioreactor shown in Fig. 1 with a total working volume
of 75 L was studied for process design and simulation. An influent tank was
used for mixing and feeding of the materials into the bioreactor. An effluent
tank was utilized for collection of feedstock from the reactor.
||Laboratory scale anaerobic bioreactor, 1: Influent tank, 2:
Bioreactor, 3: Effluent tank, 4: Wet gas meter
A gas collector was provided for collection, calculation and analysis on the
amount of biogas. The dimension of the laboratory-scale treatment unit was 75,
42 cm height and 27 cm depth. The first compartment of a four-chamber unit was
bigger while the three following compartments had identical volume.
Model setup: The model was built step-by-step and functionality of each
part was checked. Then, registration of materials was carried out. Next, the
flow diagram (Fig. 2) was developed by putting together the
required unit procedures and joining them with material flow streams. Operations
were then added to unit procedures and their operating conditions and performance
parameters were specified. To enhance the best result from the software, the
bioreactor, were divided into four anaerobic digesters in accordance to number
of compartments is actual bioreactor. This is due to the fact that the bioreactor
used in this study has four compartments and each of the compartments has a
role as an anaerobic digester.
Registration of mixture and processes definition: Conceptual design
of the bioconversion process was constructed using laboratory and technical
data. Firstly, the raw materials which were kitchen waste, sewage sludge and
water, were mixed in (P-1/MX-101) to achieve influent real characteristics.
Then, the influent proceeded into Anaerobic Digester (P-2/AD-101). The produced
gas in each anaerobic digester was sent to a gas collector (P-6/MX-102) and
the produced slurry in P-2/AD-101, P-3/AD-102 and P-4/AD-103 were used as influent
for P-3/AD-102, P-4/AD-103 and, P-5/AD-104, respectively. Table
1 shows characteristics of the mixture.
|| Base case simulation flow sheet
|| Characteristics of the mixture
The operation in P-2/AD-101 was about the degradation of organic polymers such
as protein, fat, carbohydrate, cellulose and lignin by hydrolytic bacteria.
The hydrolysis reaction in this stage was designed to convert fat into long-chain
fatty acids, carbohydrate into simple sugars and protein into amino acids. The
liquefaction of cellulose and other complex compounds to simple monomers can
be the rate-limiting step in anaerobic digestion. Therefore, the unique modification
of the bioreactor could solve this problem and hence, the digestion of these
organic complexes was done in a short time. Thus, this reconfiguration was considered
in simulations and volume of unit P-2/AD-101 was designed two times larger than
other anaerobic digesters. Stream S-105 was included the simple soluble, organic
components which were formed easily available to any acid producing bacteria.
The units P-3/AD-102 and P-4/AD-104 were responsible for degradation of monomeric
components, which were produced in the first step (liquefaction). Volatile fatty
acids which were produced as the end-products of liquefaction, transformed into
short-chain alcohols (ethyl, propel and butyl alcohol) and short-chain acids
(acetic, propionic and lactic acid). Carbon dioxide, ammonia, hydrogen sulfide
and hydrogen gas which were also liberated during complex compounds catabolism,
was included in the material that registered in units P-3/AD-102 and P-4/AD-104.
Finally, methane formation stage was designed in unit P-5/AD-105. The methanogenic
bacteria use short-chain alcohols and acids and protein, carbon dioxide and
hydrogen gas to produce methane. So, the balance between these materials happened
in P-5/AD-105 and the produced methane was transferred to gas collector (P-6/MX-102)
through gas collection lines. Figure 3 illustrates pathway
leading to the production of methane and carbon dioxide from the anaerobic digestion
of the organic fraction of organic waste (Tchobanoglous et
RESULTS AND DISCUSSION
Model validation: The computer model was designed and developed based
on laboratory scale anaerobic bioreactor to estimate the biogas production rate,
methane content in biogas and characteristics of effluent. Initially, Model
outputs were compared with those achieved through experiments. After validation,
users are able to change the model components and process parameters to evaluate
the bioreactor performance. This will help to optimize all important conditions
in bioreactor to attain best biogas production rate before fabrication and commercialization.
The Operation Gantt Chart for the process is shown in Fig. 4.
|| Methane and carbon dioxide production path
|| Equipment occupancy chart in the zero-waste system
The process time for certain operations are dependent upon operations of other
procedure. Hence, the duration of this slave operation is set to follow to the
duration of the master operation using the Master-Slave Relationship function
of Super Pro Designer.
Biogas production rate: The gas content of each digester was monitored
in the model and the produced amounts were compared with experimental results
achieved in the laboratory (Table 2).
Results indicate appreciable agreement between predicted and experimental values.
Biogas production rate was increased toward the bioreactor last compartments.
During preliminary stage at first digester, materials are gone through liquefaction
stage. The liquefaction of polymers to simple monomers can be rate-limiting
step in digestion as this bacterial action is much slower in stage one to compare
to further two stages, which are acid formation and methane formation (Barber
and Stuckey, 1999).
|| The produced biogas characteristics in each compartment of
|VS: Volatile solid, nd: Not detected
Production of more methane in final digesters is due to higher activity of
methane bacteria. Methane bacteria can only use limited number of substrate
for the formation of methane which are CO2+H2, formate,
acetate, methanol, methylamines and carbon monoxide (Tchobanoglous
et al., 1993). Higher production of methane in final digester is
due to rector configuration and assigning methane formation reaction in last
digester. The compliance between predicted and experimental values indicates
appropriate allocation of each anaerobic reaction in the model.
Effect of configuration change on reactor performance: Performance of
the bioreactor in biogas production and effluent removal efficiency was investigated
by variation of compartments volume. Therefore, two configurations were simulated
in the model which in the first configuration all four digesters had same volume
while for second one, first digester volume was made double. Then, performance
of both configurations was investigated in term of BOD and COD removal efficiencies
as well as biogas production rate and content of methane in biogas. As shown
in Table 3, more favorable results were obtained for second
configuration as this physical modification provided longer solids retention
time and superior performance compare to the reactor with similar size compartments.
The larger compartment in the reactor acted as a natural filter and provided
superior solids retention for the small particles.
Cost analysis: Cost analysis and project economic evaluation play an
important role in any project installation and/or development. Interests on
venture in biogas productions depend on profitability of investment. In bigger
scale, building a new plant is a major capital expenditure and a lengthy process.
To make a decision, management must have information on capital investment required
and time to complete the facility (Petrides et al.,
2010). To outsource the production, cost analysis is important and use as
a basis for negotiation with contract manufacturers. Table 4
shows the economic evaluation results of the laboratory scale anaerobic bioreactor
for production of biogas.
Fixed capital investment was estimated based on total equipment cost using
various multipliers, some of which are equipment specific (e.g., installation
cost, maintenance, microbes supply and gas purification) while others are plant
specific (e.g., cost of piping).
|| Effect of configuration change on bioreactor performance
|| Key economic evaluation results
Key assumptions for the economic evaluations include: (1) a new bioreactor
will be fabricated and dedicated to the client of this product; (2) total methane
yield of bioreactor is 0.26 m3 CH4 kg-1 VS
and (3) 860 m3 (615 kg) methane will be produced per year. For a
small-scale bioreactor, the total capital investment was assumed to be RM 2000.
Based on the results, unit production cost is RM 0.49 kg-1 of product
and market value of the product was estimated to be RM 2 kg-1 which
results in revenue of RM 930 year-1 and gross margin of 75.5%. This
indicates biogas production from organic waste in a single small-scale bioreactor
is a promising method for renewable energy generation.
Design and process simulation of a small-scale anaerobic bioreactor was done
using SuperPro Designer®.
The system was able to successfully simulate complex intermediate reactions
of anaerobic digestion system for production of biogas as appreciable agreement
between predicted and actual results was achieved. Results reveal the bioreactor
with configuration of doubled-size first compartment is able to produce more
biogas and methane which was 0.318 m3 kg-1 VS and 58%,
respectively compare to configuration with four same size compartments which
produced 0.295 m3 kg-1 VS of biogas contains 56% methane.
Cost analysis results show revenue of RM 930 year-1 and gross margin
of 75.5%. Therefore, the results obtained from this study are useful in proposing
waste-to-energy systems to enterprises which are dealing with organic feed stocks
1: Akiyama, M., T. Tsuge and Y. Doi, 2003. Environmental life cycle comparison of polyhydroxyalkanoates produced from renewable carbon resources by bacterial fermentation. Polym. Degrad. Stab., 80: 183-194.
2: Barber, W.P. and D.C. Stuckey, 1999. The use of the Anaerobic Baffled Reactor (ABR) for wastewater treatment: A review. Water Res., 33: 1559-1578.
3: Cooney, C.L., D. Petrides, M. Barrera and L. Evans, 1988. Computer-Aided Design of a Biochemical Process. In: The Impact of Chemistry on Biotechnology, Phillips, M., S.P. Shoemaker, R.D. Middlekauff and R.M. Ottenbrite (Eds.). Oxford University Press, UK., pp: 39-61.
4: Evans, L.B. and R.P. Field, 1986. Requirements of a biotechnology process simulator. Proceedings of the 3rd World Congress of Chemical Engineers, September 21-25, 1986, Tokyo, Japan, pp: 890-893.
5: Haas, M.J., A.J. McAloon, W.C. Yee and T.A. Foglia, 2006. A process model to estimate biodiesel production costs. Bioresour. Technol., 97: 671-678.
CrossRef | Direct Link |
6: Kothari, R., V.V. Tyagi and A. Pathak, 2010. Waste-to-energy: A way from renewable energy sources to sustainable development. Renew. Sustainable Energy Rev., 14: 3164-3170.
7: Kwiatkowski, J.R., A.J. McAloon, F. Taylor and D.B. Johnston, 2006. Modeling the process and costs of fuel ethanol production by the corn dry-grind process. Ind. Crops Prod., 23: 288-296.
8: Malakahmad, A., N.E.A. Basri and S.M. Zain, 2008. An application of anaerobic baffled reactor to produce biogas from kitchen waste. WIT Trans. Ecol. Environ., 109: 655-664.
Direct Link |
9: Malakahmad, A., A.B.N. Ezlin and M.Z. Shahrom, 2011. Study on performance of a modified anaerobic baffled reactor to treat high strength wastewater. J. Applied Sci., 11: 1449-1452.
CrossRef | Direct Link |
10: Malakahmad, A., M. Z.Z.B.C.M. Nasir, S.R.M. Kutty and M.H. Isa, 2010. Solid waste characterization and recycling potential for University technology PETRONAS academic buildings. Am. J. Environ. Sci., 6: 422-427.
11: Pedrites, D., C. Siletti, J. Jimenez, P. Psathas and Y. Mannion, 2011. Optimizing and the design of operation of fill-finish facilities using process simulation and scheduling tools. Pharm. Eng., 31: 1-10.
12: Petrides, D.P., 1994. Biopro designer: An advanced computing environment for modeling and design of integrated biochemical processes. Comput. Chem. Eng., 18: S621-S625.
13: Petrides, D.P., A. Koulouris and P.T. Lagonikos, 2002. The role of process simulation in pharmaceutical process development and product commercialization. Pharm. Eng., 22: 1-8.
Direct Link |
14: Petrides, D., A. Koulouris, C. Siletti, J. Jimenez and P. Lagonikos, 2010. The Role of Simulation and Scheduling Tools in the Development and Manufacturing of Active Pharmaceutical Ingredients. In: Chemical Engineering in the Pharmaceutical Industry: R and D to Manufacturing, Ende, D.J. (Ed.). John Wiley and Sons, USA.
15: Tchobanoglous, G., H. Theisen and S.A. Vigil, 1993. Integrated Solid Waste Management-Engineering Principles and Management Issues. McGraw-Hill, New York, USA.
16: Thomas, C.J., 2003. A design approach to biotech process simulations. BioProcess Int.,
17: Toumi, A., C. Jurgen, C. Jungo, B.A. Maier, V. Papavasileiou and D.P. Petrides, 2010. Design and optimization of large scale biopharmaceutical facility using process simulation and scheduling tools. Pharm. Eng., 30: 1-9.
18: Zeinali, E., A. Soltani, S. Galeshi and S.A.R.M. Naeeni, 2009. Estimates of nitrate leaching from wheat fields in gorgan, of Iran. Res. J. Environ. Sci., 3: 645-655.
CrossRef | Direct Link |