1 s2.0 S036031992304051X Main
1 s2.0 S036031992304051X Main
1 s2.0 S036031992304051X Main
ScienceDirect
Article history: Biogas, a renewable energy source produced by the anaerobic digestion of organic matter,
Received 23 May 2023 can reduce greenhouse gas emissions. The purification and hydrogen conversion of biogas
Received in revised form enhance its versatility as an energy carrier and reduce dependency on fossil fuels. This
25 July 2023 sustainable process fosters a cleaner future. In this study, simulation models were used to
Accepted 7 August 2023 optimize the process conditions and assess the technical feasibility of the biogas-to-
Available online 23 August 2023 hydrogen production. In addition to the anaerobic digestion process, simulation models
were also developed for biogas purification via water scrubbing and hydrogen production
Keywords: via steam methane reforming (SMR) using Aspen Plus simulation software. Simulation
Aspen plus results demonstrated that the maximum methane yield of 520 mL/gVS was achieved at an
Anaerobic digestion organic loading rate of 20 g/L, while the minimum yield of 230 mL/gVS was observed below
Bio-methane 10 g/L. Optimal anaerobic digestion temperature for maximum gas yield (548 mL/gVS) was
Hydrogen found to be 30 C, with a decrease in biogas production above 40 C. In the water scrubbing
Steam methane reforming model, maximum CO2 absorption occurred at 15 bar pressure, whereas reducing the
pressure to 5 bar significantly lowered the absorption rate. The SMR model yielded the
highest hydrogen production rate of 10.15 kg/h using purified methane (99%) as feed, but
the methane content was affected by the biogas composition.
© 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
* Corresponding author.
E-mail addresses: immanuel.selwynraj@vit.ac.in, aisraj1979@gmail.com (A.I. Selwynraj).
https://doi.org/10.1016/j.ijhydene.2023.08.096
0360-3199/© 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
212 i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 5 0 ( 2 0 2 4 ) 2 1 1 e2 2 5
biomass sources. This aids in selecting and optimizing feed- ground into small pieces (0.5 mm) using a 2 hp electric grinder.
stocks for biogas plants, facilitating efficient and informed Then, the RS was dried at 80 C for 4 h in an oven to remove
decision-making. Additionally, the model's simplified nature excess moisture. The elemental analysis of RS was performed
broadens possibilities for optimizing processes, designing using a PerkinElmer analyzer (2400 CHNS/O Series). From the
systems, and making informed choices in the realm of biogas elemental analysis, it was observed that in RS, the composi-
production. The novelty of stoichiometry-based model is uti- tion was C: 38.2%, O: 51.5%, H: 7.94%, S: 0.92%, and N: 0.72%.
lizing ultimate analysis values, represents a pioneering For cow dung, the composition was found to be C: 34.4%, O:
advancement in the field of biogas yield prediction. This 57.14%, H: 5.2%, S: 6.04%, and N:1.7%, based on the elemental
ground-breaking approach holds tremendous promise for the analysis. Consequently, the proximate analysis (PA) of RS
biogas industry, enabling more empowers researchers and showed Moisture (M): 10.3%, volatile matter (VS): 60.85%, fixed
practitioners to make informed choices and contributing to carbon (FC): 21.2%, and ash: 6.1%. On the other hand, for cow
the sustainable utilization of biomass resources for renewable dung, the PA revealed M: 6.45%, VS: 55.4%, FC: 13.4%, and ash:
energy production. 26.3% [16]. Rice straw (RS) on its own has a high carbon-to-
nitrogen (C/N) ratio of 53, making it unsuitable for anaerobic
1.1. Proximate and ultimate analysis of biomass digestion. Methanogenic bacteria need a sufficient amount of
nitrogen to produce biogas effectively. However, when RS is
The composition of ash, volatile solids (VS) and moisture (M) is combined with cow dung, the nitrogen content of the mixture
calculated using proximity analysis (PA) with ASTM E1755-01, increases, allowing the bacteria to function properly and
ASTM E872-82, and ASTM D4442-16 standards, respectively produce biogas. Additionally, the different degradation rates
[15e17]. The FC content in the biomass is calculated using of the two materials prevent overloading of the biogas digester
Equation (1). [19,20].
Fixed carbon ¼ 100- (%M biomass þ %ash biomass þ 2.2. Anaerobic digestion process
% VS biomass) (1)
The rice straw and cow dung mixture is placed in three 2L
The elemental composition of organic matter impacts the airtight customized anaerobic batch reactors, with a hydraulic
energy output and effective usage of biomass resources for retention time set to be 12 days at atmospheric temperature
biofuel production. The ultimate analysis (UA) is used to (32 C±2 C). To achieve organic loadings of 8 g/L, 15 g/L, and
calculate the elemental composition of biomasses, including 18 g/L in the digesters, the cow dung and rice straw are mixed
Carbon (C), Hydrogen (H2), Oxygen (O2), Chlorine (Cl2), Sulphur in the proportions of CD:RS at 10.9:7.1, 22.2:12.8, and
(S) and Nitrogen (N2). However, the UA needs very costly in- 33.4:16.6 g, respectively. The biogas produced from the
struments and experts with extensive training. The PA needs digester is measured using the water displacement method.
common laboratory tools and may be carried out by any Each day, the pH values are monitored, and NaOH (0.01 N) and
qualified technician. The correlations with an absolute error HCl (0.01 N) solutions are added to the digester to control the
of 3.21% (C), 4.79% (H), and 3.40% (N) and a bias error of 0.21% desired pH level (6e7.5) [21]. The digesters are agitated for
(C), 0.15% (H), and 0.41% (N) proved to be the most accurate in 15 min per day using a mechanical agitator to ensure proper
determining the elemental composition of any biomass mixing of substrates and to prevent the accumulation of
resource via proximity analysis [18]. The correlations are organic content under the digester.
represented in equations (2)e(4), where C, H, O, VS and FC are
represented in mass % on a dry basis. 2.3. Description of the process simulation model
Carbon ¼ 0.634 FC þ 0.455 VS (wt. %) (2) The proposed simulation model is divided into three process
flowsheets: (i) Biogas generation from biomass in AD, (ii) Up-
Hydrogen ¼ 0.052 FC þ 0.062 VS (wt. %) (3) grade of the biogas into purified methane (Water scrubbing),
(iii) Hydrogen production from biogas through steam methane
Oxygen ¼ 0.304 FC þ 0.476 VS (wt. %) (4) reforming.
4a b 2c þ 3d 4a þ b 2c 3d 4a b þ 2c þ 3d
Ca Hb Oc Nd þ H2 O / CH4 þ CO2 þ dNH3 (7)
4 8 8
3. N2 þ 3H2 / 2 NH3
4aþb2c3d
22:4 4. C þ O2 / CO2
CH4 yield ¼ 8
(8) 5. C þ 2H2 / CH4
12a þ b þ 16c þ 14d
6. 2C þ 2H2O / CH4 þ CO2
3. Simulation results and discussion Based on the feed material, the reaction's limiting reactant
varies. The limiting reactants of reactions 2,3 and 6 are S, N2
3.1. Process simulation model of AD and C, respectively. The limiting reactant for reaction 1 is O2
when the hydrogen and oxygen mass ratio (H2/O2) is greater
The PSM of AD is shown in Fig. 2. The AD is modelled by than 0.25 because of low degradability. Generally, forest resi-
means of two distinct reactors, namely the RYIELD reactor (B1) dues (wood) fall under this category. On the other hand, sub-
and RSTOIC (B2). The B1 block allows the substrate to be strates with an H2/O2 ratio of less than 0.25 often use H2 as a
broken down into its constituent elements, whereas block B2 limiting reactant. Since ASPEN is insensitive to atom
transforms the elemental components into the different balancing, careful consideration of atom balance and mass
gaseous compounds found in the biogas. The input parame- balance was made when making yield computations for the
ters of these blocks and their roles in plant AD are present in RYIELD reactor. The data from the UA are used to determine
Table 1. Since the plant AD handles solids, the SOLIDS prop- the yield distribution. The following formulas are used to
erty method is used to create the PSM. The conventional and determine the yield distribution, where X-is the ultimate
non-conventional (C) stream classes are used in the model. analysis value and m-mass.
The inlet feedstock is considered an NC stream because the
biomass's physical properties are not present in the aspen Mass of H2 ¼ (1 e XMoisture)*XH*mFeed
standard library. Other elements such as C, H2, N2, CH4, CO,
H2O, S and NH3 are represented as conventional streams. Mass of O2 ¼ (1 e XMoisture)*XO*mFeed
Furthermore, fixed carbon (FC) and ash content in the biomass
are also defined as NC streams. Mass N2 ¼ (1 e XMoisture)*XN*mFeed
The present PSM does not include biological activities that
are known to occur during AD when compared to ADM1 used Mass of H2O ¼ XMoisture*mFeed
by Ref. [10]. However, the function of the yield reactor and the
hydrolysis stage is similar. The RYIELD (B1) reactor de- Mass of S ¼ (1 e XMoisture)*XS*mFeed
composes the complex physical structure of biomass, which
ASPEN defines as NC, into conventional elemental compo- Mass of C ¼ (1 e XMoisture)*XC*mFeed
nents (C, H, and O) and NC components (FC and Ash). The
stoichiometric reaction of volatile matter is defined in the Mass of ASH ¼ (1 e XMoisture)*XASH*mFeed
RSTOIC reactor to produce biogas. Non-reacted ash and FC
content, along with excessive water leave the reactor as
digestate. The AD modelling is made easier by the following
stoichiometric reactions. 3.1.1. Model validation
The volume of methane generated from different biodegrad-
1. 2H2 þ O2 / 2H2O able waste is validated against the theoretical approaches and
2. S þ H2 / H2S different experimental results. Prediction methods tend to
underestimate or overestimate the amount of biogas gener- feed and the extent of the stoichiometric reaction are the two
ated from a substrate, assuming that the complete trans- most important aspects of PSM that influence simulated re-
formation of volatile materials results in theoretical biogas. sults. However, the elemental composition of the substrates
Although the precise methane yield value is nearly impossible cannot be modified when using a single-feed AD system.
to predict because AD involves complex biochemical and Nonetheless, the selection of substrates for anaerobic co-
biological processes. The main objective of the present PSM is digestion for biogas enhancement can be based on the ratio
to prevent a significant overestimation of the methane yield. of carbon to nitrogen (C: N). The optimum C: N ratio for AD is
Table 2 lists the various organic materials and managed between 20:1 and 30:1 [34]. The inclusion of carbon-rich co-
data scenarios that were used to create PSM to compute the digestion materials in animal manure could enhance the C: N
discrepancies between other experimental data and PSM. ratio and methane generation. For instance, the C: N ratio of
Following are descriptions of each case: swine (6:1) and dairy manures (9:1) can be increased by
CASE I: The substrate used was cow dung, and the organic incorporating food processing leftovers or crop residues such
loading rate was 0.33 L/day. The produced biogas yield was as rice and oat straw (C: N ratio 48: 1).
353.5 L/kgvs/day [27]. The UA analysis of the substrates was In this study, anaerobic co-digestion of pig manure and rice
based on Fajobi et al. (2022) [28]. straw were simulated using PA and UA analysis (case 4). The
CASE II: The substrate used was municipal solid waste utilization of pig manure as a feedstock material in anaerobic
(MSW), and the organic loading rate was 3gvs/L/day. The pro- digestion (AD) technology has seen significant development.
duced biogas yield was 0.54 m3/kgvs/day [29]. The UA analysis Previous studies have indicated that the mesophilic condi-
of the substrates was based on Salwa Khamis et al. (2019) [30]. tioned AD process of pig manure can achieve a biogas pro-
CASE III: The substrate used was pig manure (PM) and the duction rate of up to 5 m3/(m3$d). Nevertheless, pig manure
organic loading rate was 230.4gvs/day. The produced biogas possesses distinctive attributes such as elevated nitrogen (N)
yield was 0.269 m3/kgvs/day [31]. The UA analysis of the sub- content, lower carbon (C) content, and an imbalanced C/N
strates was based on Smith, Ekpo, and Ross (2020) [32]. ratio, which hinder its potential as a valuable feed source for
CASE 1V: Anaerobic co-digestion, the substrates were pig biogas production [33,35,36]. When pig manure is used as the
manure (PM) and rice straw (RS). The organic loading rate was sole substrate for AD, it can lead to ammonia suppression,
100g/day (53%-PM and 47%-RS). The produced biogas yield which negatively affects biogas production and reduces
was 342.35 mL/gvs [33]. decomposition efficiency. However, the concept of anaerobic
Table 2 presents the validation of different case studies co-digestion has recently emerged as a promising solution to
with experimental and theoretical results from Rajendran improve the efficiency and effectiveness of AD. On the other
et al. (2014) Aspen PSM. The lowest and highest differences hand, rice straw contains a significant amount of carbohy-
found using the various PSM validations were 0.32% for case 3 drates and has a high carbon-to-nitrogen (C/N) ratio, which
and 6.6% for case 4. In case I, the current PSM predicts biogas can also hinder the growth and metabolism of methanogens
production of 0.348 m3/kgvs/day, which is much closer to the responsible for biogas production due to acid accumulation.
experimental results Fajobi et al. (2022) when compared to the Therefore, by adjusting the quantities of straw and pig
PSM model developed by Rajendran et al. (2014). Also, the PSM manure during co-fermentation, an appropriate C/N ratio can
doesn't consider how much organic matter is lost because be achieved [37,38]. Moreover, in India, rice production yields
microorganisms consume it. about 110 million metric tons annually, generating approxi-
mately 170 million metric tons of rice straw [39]. With their
3.2. Sensitivity analysis elemental composition, the C:N ratio is attained at about 28:1
and the rate of methane yield was 471.5 mL/gvs, when the
Fig. 3 shows the comparison of bio-methane potential (BMP) of molar fraction of CH4 and CO2 is about 54% and 39%, respec-
experimental, theoretical and ADM1-based PSM with the tively. Dennehy et al. (2018) also investigated the effect of co-
present model. The present model exhibits good consistency digestion with pig manure and food waste in AD and found
with experimental results. The chemical composition of the that the maximum methane yield was 333 mL/gvs. The
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 5 0 ( 2 0 2 4 ) 2 1 1 e2 2 5 217
% of differencea
1.4%
5.4%
0.3%
6.6%
Present study (PS) results
348.369 L/kgvs/day
0.270 m3/kgvs/day
0.57 m3/kgvs/day
320.25 mL/gvs
0.473 m3/kgvs/day
0.268 m3/kgvs/day
365.83L/kgvs/day
0.450 m3/kgvs/day
0.53 m3/kgvs/day
rate of extent is less than or equal to 0.5, the rate of CH4 yield
342.35 mL/gvs
230.4 gvs/day
3gvs/L/day
0.33 L/day
100 g/day
a
I
degradation and gas production. However, the OLR of 10g 3.2.2. Effect of temperature on biogas production
maintains an optimal balance between substrate supply and In the study of impact of temperature on biogas production
system efficiency, ensuring efficient substrate utilization and using a simulation model, a 3 L volume with an organic loading
promoting higher gas yields. These factors, including volume, rate of 20 g was examined at different temperature levels: 25 C,
retention time, substrate availability, and organic loading 30 C, 40 C, and 50 C, resulting in methane production mea-
rate, play crucial roles in influencing gas production and surements of 520 mL/gVS, 548 mL/gVS, 510 mL/gVS, and 515
optimizing biogas systems. According to the experimental mL/gVS, respectively (30 C > 25 C > 40 C > 50 C). Increasing
findings of Zhang et al. [41], it was observed that biogas pro- the temperature from 25 C to 30 C led to higher biogas pro-
duction is considerably low when the OLR falls below 10 g/L. duction, likely due to temperature-driven changes in reaction
Additionally, the cumulative biogas production rate at an OLR rates and increased molecular collision frequency. However,
of 25 g/L was found to be significantly lower compared to an further increasing the temperature from 30 C to 40 C resulted
OLR of 20 g/L. Likewise, the simulation model demonstrated in a decrease in biogas production, possibly influenced by fac-
that the highest biogas yield was achieved at an OLR of 20 g/L, tors such as vapor pressure and reduced gas solubility.
while the minimum gas yield was observed when the OLR was Comparing temperatures of 40 C and 50 C showed a slight
maintained below 10 g/L. The results obtained from the Aspen increase in biogas production, potentially related to the solu-
simulation closely align with the experimental findings, bility characteristics of gas components. Controlling tempera-
revealing a high degree of agreement between the two. An ture within an optimal range is critical for maximizing biogas
illustration of effect of OLR on gas yield shown in Fig. 8S (a) production efficiency in the simulation model, as factors such
(Supplementary material). as reaction rates and gas solubility can impact biogas
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 5 0 ( 2 0 2 4 ) 2 1 1 e2 2 5 219
generation. Similarly, Membere et al. [42] conducted a study on hydronium ions. This reaction is important in the context of
the influence of temperature on biogas production using four biogas purification because carbon dioxide is a common im-
temperature levels: 25 C, 35 C, 45 C, and 55 C. The results purity in biogas and must be removed to increase the energy
showed that the highest gas yield of 639 mL/gVS was achieved content of the gas. equation (10) shows the reaction of
at 35 C, while the lowest gas yield of 501 mL/gVS was observed hydrogen sulfide with water to form sulfurous acid. Sulfurous
at 55 C. This study confirmed that temperatures within the acid is another weak acid that can dissociate into bisulfite and
range of 30e35 C enhance biogas production compared to hydrogen ions, which can further react with water to form
higher or lower temperatures. The rate of a chemical reaction is more bisulfite and hydronium ions. Like carbon dioxide,
determined by its activation energy, while temperature in- hydrogen sulfide is a common impurity in biogas that must be
fluences the average kinetic energy of molecules. Higher tem- removed to prevent corrosion and fouling of equipment and to
peratures increase the reaction rate as more molecules gain reduce harmful emissions.
sufficient energy to react. The equilibrium composition of a As the majority of the components employed in this
reaction depends on its Gibbs free energy, which decreases simulation environment are a polar mixture of pressure up to
with rising temperature, shifting the equilibrium towards 15 bars, the NRTL thermodynamic model is chosen that in-
products with lower Gibbs free energy. In biogas production, fluence the results. The major component blocks employed in
increasing temperature favors methane over carbon dioxide, the simulation process and inlet feeds are described as
leading to a higher methane content in the biogas [43]. An follows:
illustration of effect of temperature on gas yield shown in Pump (B1): Used to increase the pressure of water up to
Fig. 8S (b) (Supplementary material). 15 bar for better contra flow between water and biogas. The
discharge pressure from block B1 is 15 bar and pump effi-
3.3. Water scrubbing ciency is assumed as 75%.
Multistage compressor (B2): The three-stage compressor with
The water scrubbing is an absorption process since CO2 has a an intercooler is employed to increase the biogas pressure to
high solubility in pressurized water (10e25 bar) than CH4 [44]. 15 bar from the atmospheric condition. The temperature of
The flow sheet of biogas purification by pressurized water is the outlet stream from block B2 is set to be 50 C.
shown in Fig. 5. This study examines the absorbance of CO2 in CO2 separation column (B3): This block allows biogas and
water and the enhancement of the absorbance process using pressurized water in contraflow to dissolve CO2. This is a
ASPEN plus. The process chemistry behind the water scrub- two-stage process, where bioCH4 is produced in stage 1 (top)
bing process was described in equations (9) and (10). and regenerated water in stage 2 (bottom). The calculation
mode is rate-based and the internal structure of a column
CO2 þ H2O / H2CO3 (9) (Dimensions: 7 m height and 0.35 m diameter) is packed
type.
H2S þ H2O / H2SO3 (10)
3.3.1. Inlet streams
equation (9) describes the reaction of carbon dioxide with Biogas (50% CH4þ 50% CO2) at 1 bar and 25 C and the feed rate
water to form carbonic acid. Carbonic acid is a weak acid that is 1 cum/hr (case IV).
can dissociate into bicarbonate and hydrogen ions, which can Water supply: 1 bar and 25 C and the feed rate (0.25e0.75
further react with water to form more bicarbonate and cum/hr)
3.3.2. Sensitivity analysis physical and chemical adsorption techniques could enhance
In order to attain the maximum CO2 absorbance, the water biogas purification [45]. They suggested that the calcium hy-
pressure (3e30 bar), feed rate (0.25e0.75 cum/hr) and biogas droxide (Ca (OH)2) solution could be the best option for
composition are varied. According to the simulation results, removing CO2 from biogas. However, using chemical agents is
while maintaining the biogas composition of 50:50 (CH4: CO2) not economically feasible, and the byproducts of these pro-
and water pressure at 15 bar at 0.50 cum/hr, the rate of CO2 cesses may be harmful to the environment.
absorption is about 99%. Furthermore, for the same inlet In addition, the biogas composition with higher methane
condition, by varying the pressure to 10 bar and 5 bars, the rate concentration (65% CH4 and 35% CO2) showed better CO2 ab-
of CO2 absorption was 98 and 89%, respectively (Fig. 6(a)). sorption in water scrubbing even at low pressure (3 bar)
To find the influence of water addition in the process, the (Fig. 6(d)). Nowadays, modern pretreatment techniques are
flow rate of water increased by about 0.75cum/hr for the same employed to produce biogas with higher methane concen-
biogas composition and flow rate. There is no significant tration through AD [46,47]. All the process stream specifica-
improvement in CO2 absorption by increasing the flow rate of tions and output results are shown in Table 3.
water from 0.5 cum/hr (Fig. 6(b)). Nonetheless, there is a 2%
decrement in absorption when the water flow rate is reduced 3.3.3. Model validation
to 0.25cum/hr. The simulation model is validated with experimental data
While varying the biogas composition as 30% CH4 and 70% from a pilot-scale water scrubbing plant, located at “The
CO2 at 15 bar pressure, only 40e50% of CO2 is absorbed, and a Centre for Rural Development and Technology”, IIT, Delhi [23].
change in pressure does not result in a significant improve- The pilot scale plant was operated with the following pa-
ment. It is concluded that the biogas containing a low con- rameters: Column height: 3 m, diameter: 0.15 m, biogas flow
centration of (<50%) CH4 cannot be effectively purified in the rate: 20 m3/h, solvent flowrate: 4.4 m3/h, Biogas feed: 60% CH4
water scrubbing method (Fig. 6(c)). For such conditions, the and 40% CO2 and pressure:10 bar. The output of the stream
Fig. 6 e Variation in CO2 absorption based on the composition of biogas and pressure change, (a) CH4/CO2/H2O: 0.474/0.474/
499 kg/h; (b) CH4/CO2/H2O: 0.474/0.474/748.5 kg/h; (c) CH4/CO2/H2O: 0.418/0.627/0.249 kg/h; (d) CH4/CO2/H2O: 0.540/0.291/
0.748.5 kg/h.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 5 0 ( 2 0 2 4 ) 2 1 1 e2 2 5 221
from the plant contains 95% CH4 and 5% CO2. The present Reformer Unit: High-pressure steam (3e25 bar) is mixed
model was set to simulate the same operating conditions. The with biogas at temperatures ranging from 700 to 1000 C in the
results show that the volume of CH4 and CO2 at the outlet RSTOIC (B6) reactor. This reaction produces H2, CO, and a
stream was 95.6% and 4.08%, respectively. The difference be- small amount CO2. Some CH4 escapes, but it is captured and
tween the simulation and experimental results was 0.6%. converted into an inert element in the RGibbs (B7) reactor. The
RGibbs reactor acts as a filter, removing excess CH4 from the
3.4. Steam methane reforming process.
High Temperature Water gas shift Unit (HT-WGS): The HT-
In this study, the Gibbs free energy minimization method is WGS unit utilizes the Rstoic reactor as its gas shift reactor.
used to determine the equilibrium composition and temper- Operating at high temperatures (350e450 C) and pressure
ature under distinct thermodynamic conditions. For the (16e20 bar), its main purpose is to convert CO and H2O into
simulation model of steam methane reforming (SMR), the CO2 and H2. By adjusting temperature, pressure, and biogas
following elements were considered: H2O, carbon monoxide properties, the HT-WGS unit ensures efficient conversion of
(CO), carbon dioxide (CO2), CH4 (99% purified from biogas), and CO and H2O into CO2 and H2 within the Rstoic reactor.
air (N2/O2:79/21). In this simulation study, the Peng-Robinson Low Temperature Water Gas Shift Unit (LT-WGS): The reason
equation of state with modified Huron-vidal mixing rules for employing the LT-WGS step after HT-WGS in the SMR
(PRMHV2) property method is chosen. It is applicable for polar process is to enhance the overall CO conversion and reduce
and non-polar components and processes operating at high CO levels to meet the desired specifications. The HT-WGS
pressure and temperature. The Aspen Plus flow sheet of SMR reaction alone may not achieve sufficient CO conversion,
for H2 production is shown in Fig. 7 and unit blocks, stream and the LT-WGS step helps to further drive the conversion
input and specifications are shown in Table 4. towards CO2 and H2. By cascading the HT-WGS and LT-WGS
The SMR simulation process consists of four main stages: units, the overall efficiency of CO removal is improved, lead-
The Reformer Unit, High Temperature Water Gas Shift (HT- ing to higher purity hydrogen production.
WGS) Unit, Low Temperature Water Gas Shift (LT-WGS) Unit, Tail Gas Combustion Unit: The outlet stream from the LT-WGS
and Tailgas Combustion Unit. unit, mixed with water vapor, is transferred to the flash column
FC and Component
38 C. The moisture-free gases then pass through the pressure
swing absorber (PSA) to separate H2 from other gases, forming
0.75 and CO
0.75 and CO
0.8 and CH4
1 and CH4
1 and CO
the tail gas. The tail gas, containing trace amounts of CH4 and
1 and H2
other gases, is burned in the tailgas combustion unit with excess
air or oxygen. This minimizes the release of unreacted CH4 and
e
e
e
CO, potent greenhouse gases, into the atmosphere. The com-
bustion of the tail gas generates heat energy that can be recov-
ered and utilized within the SMR process, improving overall
CH4þ2O2/CO2þ2H2O
energy efficiency and sustainability.
CH4þH2O/3CH2þCO
CO þ H2O/ H2þCO2
CO þ H2O/ H2þCO2
COþ0.5O2/CO2
H2þ0.5O2/H2O
respectively, at 1 bar and 25 C, are fed to the model. The mixture
of CH4 and H2O at 16 bar and 909 C is maintained before entering
the mainstream reformer unit. The CH4 reacts with water
(fractional conversion of 0.8) and produces H2 and CO. The
produced syngas is fed to HT-WGS (413 C), where CO reacts with
e
e
e
e
reduced temperature (210 C). The outlet from the LT-WGS unit
is mixed with CH4, CO, H2O, CO2 and H2 at different molar con-
centrations at high pressure (16 bar). To separate H2 from the
syngas, Pressure swing adsorption (PSA) unit is used. When the
split fraction is set as 79%, the H2 yield is 10.16 kg/h. The tail gas
from the PAS unit contains volatile gases, including CH4 (0.128%)
and H2 (0.35%). Since CH4 is a greenhouse gas, further treatment
is required before it leaves the atmosphere. The tail gas could be
To model the reforming of biogas
Role
To
To
To
To
7.04 kg/h, 4.23 kg/h, and 1.24 kg/h, respectively. These results
highlight the significant impact of the biogas composition on
hydrogen conversion, suggesting that purified methane is
crucial for achieving optimal hydrogen conversion. Addition-
ally, the pressure was varied to 5 bar, 10 bar, and 20 bar, resulting
Aspen Module
RSTOIC
RSTOIC
RSTOIC
RGIBBS
Heater
FLASH
Valve
hydrogen production rates of 4.26 kg/h, 6.92 kg/h, 8.4 kg/h, and
10.64 kg/h, respectively. This sensitivity analysis demonstrates
the importance of considering various factors in hydrogen pro-
duction from biogas. The composition of the biogas feed, as well
Tail gas combustion unit
HTWGS
LTWGS
PSA
3.5. Comparison of experimental and simulation results occurred at 10 g/L. The optimal temperature range for gas
production was 30e35 C, with a yield of 548 mL/gVS,
To evaluate the efficiency of the process simulation model, decreasing significantly beyond 40 C. Biogas purification
the results from the simulation were compared with those involved pressure ranging from 3 to 15 bar and varying biogas
obtained from a laboratory-scale batch anaerobic digester compositions (50% CH4:50% CO2, 65% CH4:35% CO2, and 30%
with a 2L volume. The gas yield from the simulation model CH4:70% CO2), with 99% CO2 absorption at 15 bar and 50%
was determined for organic loading rates of 8g, 15g, and 18g, CH4:50% CO2 composition. Notably, 10 bar pressure achieved
resulting in yields of 242 mL/gVS, 436 mL/gVS, and 492 mL/ 99% CO2 absorption with a 65% CH4:35% CO2 composition. In
gVS, respectively. Conversely, the experimental gas yields at hydrogen production from biogas, using feed streams with 99%,
the same organic loading rates were recorded as 212 mL/gVS, 75%, 50%, and 40% methane yielded hydrogen production rates
396 mL/gVS, and 458 mL/gVS. Based on the comparison of of 10.15 kg/h, 7.04 kg/h, 4.23 kg/h, and 1.24 kg/h, respectively.
simulation and experimental results, it was observed that the The highest hydrogen production of 10.94 kg/h occurred at
maximum difference between the two was 12%, while the 20 bar and 1000 C, and beyond 900 C and 16 bars, the hydrogen
minimum difference was 7%. This indicates the reliability of yield was not significant. Simulation and experimental results
the developed simulation model. Additionally, the uncer- showed a maximum difference of 12% and a minimum differ-
tainty between simulation and experimental data was found ence of 7%. The simulation results provide a base model for
to be highest when the organic loading rate was set to be less studying anaerobic digestion in a simulated environment,
than 10%. This might be attributed to the poor organic loading exploring biogas production rates, organic loading rate effects,
rate, which leads to insufficient nutrient supply to microor- and temperature influences. Additionally, the model identifies
ganisms responsible for biogas production. Nonetheless, In optimal conditions for biogas purification and hydrogen pro-
the simulation model, it is challenging to fully represent the duction via steam methane reforming. These insights are
natural variability and complexity of microbial activities that valuable for further research and process optimization in
occur during anaerobic digestion. However, continuous ef- renewable energy production.
forts should be made to refine the model to better replicate the
real-world anaerobic digestion process and improve its accu-
racy in predicting biogas production. Data availability
2019;10(2):181e91. https://doi.org/10.1080/
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