CHEMICAL ENGINEERING TRANSACTIONS
VOL. 57, 2017
A publication of
The Italian Association
of Chemical Engineering
Online at www.aidic.it/cet
Guest Editors: Sauro Pierucci, Jiří Jaromír Klemeš, Laura Piazza, Serafim Bakalis
Copyright © 2017, AIDIC Servizi S.r.l.
ISBN 978-88-95608- 48-8; ISSN 2283-9216
Optimizing the Exergetic Efficiency of a Pressurized Water
Process for Bio Gas Cleaning
Jan C. Schöneberger*, Armin Fricke
Chemstations Europe GmbH, Berlin, Germany
js@chemstations.eu
A pressurized water process for bio gas cleaning is optimized by means of its exergetic efficiency. The exergy
consumption of this process is a suitable measure as it weights electricity and steam consumption and
considers the increase of physical and chemical exergy of the treated bio gas stream. In this paper the steps
applied for modeling the process and for calculating the exergy of the streams are presented and an outlook
on the optimal operation conditions is given.
1. Introduction
About 30 % of the gross electricity production (GEP) in Germany was generated from renewable sources in
2015 (Lentz et al 2016). While electricity from biomass is, with approximately 8 % of GEP, far behind the wind
energy (approx. 15 % of GEP), it has the considerable advantage of being much easier to store. In fact, bio
methane from bio gas is the only renewable energy source with a world scale storage facility - the European
natural gas grid. However, only 8 % of the generated bio gas was upgraded to bio methane in Germany 2015
and thus used as green gas (Scheftelowitz et al. 2015). One reason for this relatively small figure is that the
upgrading process requires capital and operational expenditures (CAPEX/OPEX) in an amount that allow an
economically feasible operation only for larger plants. And up to now most of the plants operated in Germany
are decentralized and of relative small size.
The advantage of decentralized bio gas production is that the transport ways of the biomass are kept short.
Thus, it is important to have high efficient and low cost bio gas upgrade processes. One process that fits into
these needs is the pressurized water process (Starr et al. 2012). A feature of this process is that it only
requires water and no additional chemicals for the treatment of the bio gas. Consequently, the highest
environmental impact of this process comes from the electricity consumption and the fresh water consumption
(Ryckebosch et al. 2011).
2. Process Model
The evaluated process is a three-stage compression pressurized water process with an intermediate flash for
methane recovery, an air stripper and a bio filter for hydrogen sulfide conversion. The process flowsheet is
depicted in Figure 1.
2.1 Process Conditions
Composition and properties of the stream that is coming from the fermenter are given in Table 1. The air
which is used for stripping is taken in at ambient conditions (25°C, 1.013 bar abs) and considered to be dry.
Saturated steam of 1.1 bar abs is used for water make up. This should reflect the exergy requirements for
providing water with sufficient purity. Cooling water is assumed to be available at ambient conditions, so that it
has a physical exergy of zero. This means it is supplied with 25°C. Pressure drop in the heat exchangers is
not considered.
DOI: 10.3303/CET1757077
Please cite this article as: Schoeneberger J., Fricke A., 2017, Optimizing the exergetic efficiency of a pressurized water process for bio gas
cleaning, Chemical Engineering Transactions, 57, 457-462 DOI: 10.3303/CET1757077
457
Table 1: Feed conditions
Temperature Pressure Std. Volume Rate N2 O2 CH4 CO2 H2S H2O
40 °C 1.0 bar abs 300 m³/h balance - 60 mol% 36 mol% 300 mol ppm 3 mol %
To be fed to the natural gas grid the treated bio gas must have a methane concentration of at least 96 mol%
and a hydrogen sulfide content of maximal 20 mol ppm.
With the reference values of the design variables shown in Table 2 these conditions are met. The table further
more gives the bounds of these variables during the optimization.
Table 2: Design variables of the reference process with bounds for the optimization.
Name Unit Op/Stream Value Unit Lower Bound Upper Bound
Intermediate Pressure U 1 4 bar abs. 1.1 15
Absorber Pressure U 4 9 bar abs. U2 25
Gas Feed Temperature U 5 30 °C 30 80
Liquid Feed Temperature U18 30 °C 30 80
Stripping Air Flowrate S 13 250 stdVm³/h 10 1000
Cycle Water Flowrate U 14 85 stdLm³/h 5 500
Purge Ratio U 11 1% - 1% 10%
Intermediate Pressure 2 U 20 4 bar abs. U1 20
The biogas coming from the fermenter [1] is compressed in a first compressor unit (1) and then cooled down
to a temperature 5 K above its dew point temperature in the heat exchanger (2). Stream [3] is then mixed with
the stream [11] which mainly contains methane recovered from the rich water leaving the scrubber (6). The
pressure of the gas mixture is increased to the scrubber pressure in the second (units (20) and (21)) and the
third (units (4) and (5)) compression stage with an intermediate cooling down to 5 K above the dew point in
heat exchanger (21). All compressors are modelled with an adiabatic efficiency of 50 %.
Figure 1: Pressurized Water Process modelled in CHEMCAD 7 (CC7)
The gas temperature at scrubber inlet is controlled with the heat exchanger (5). The regenerated and cooled
scrubbing water [7] is entering the scrubber column (6) at the top. The scrubber is represented by 20
equilibrium stages with efficiencies as given in Section 1.3. The conditioned bio gas [8] is leaving the process
from the top of the scrubber column. The rich water [8]/[9] is fed to a flash vessel (8) where methane is
recovered due to a pressure reduction. However, depending on the pressure level of the first compressor, a
certain amount of water, carbon dioxide, and hydrogen sulfide (stream [11]) is recycled together with the
recovered methane to the inlet of the second compressor (20).
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The remaining rich water [12] is given to the top of the stripper column (9) where a part of the absorbed gases
is removed with air which is fed at the bottom of the column (stream [14]). Ambient air [13] is transported to
the stripper column with a blower (10). The stripper (9) is operated at a slight overpressure of 100 mbar (g).
This pressure is required to allow the vapor stream [15] to pass the bio-filter (19) before it is send to the stack.
A part of the regenerated scrubbing water is purged in unit (11) to avoid an accumulation of components in the
recycle stream. Make up water [20] is mixed to the regenerated water [17] to fill up water losses from the
scrubber, the stripper, and the purge. Steam condensate is used as make-up water to guaranty a sufficient
cycle water quality.
The water is brought back to scrubbing pressure with the recycle pump (17) modelled with an adiabatic
efficiency of 70%. The inlet nozzle is assumed to be in a height of 5 meter above the pump. The heat
exchanger (18) is used to control the inlet temperature of the scrubbing water.
Controller (7) is used to set the pressure of the flash vessel equal to the outlet pressure of the first compressor
while the controllers (15) and (16) are used together with the stream reference (14) to adjust the balance of
the plant. The exergy of all streams is calculated in the VBA unit operation (99) as described in Chapter 2.
2.2 Selection of the Thermodynamic Model
The process conditions range from a temperature of 20 °C to 80 °C in the absorber and up to 600 °C behind
the compressors, considering the extreme compression ratio of 25:1, while the pressure of all process streams
is within 1 bar abs and 25 bar abs. At these conditions nitrogen, oxygen, methane, and carbon dioxide are in a
supercritical state (CO2 above 31 °C). Solubilities of supercritical gas in water are commonly modelled with
Henry’s law.
However, to calculate consistent values for enthalpy and entropy in all phases which also reflect that at the
phase equilibrium the Gibbs enthalpy of the mixture reaches a minimum this approach is not adequate
(Schöneberger 2016). The phase equilibrium must be calculated with an iso-fugacity approach (eq. (2)) where
the fugacity coefficient 𝜑𝑖 is calculated with the same equation of state which is used for the calculation of
enthalpy and entropy.
Thus, an equation of state (EoS) is required which accurately predicts the solubility of the involved
components in water and gives acceptable values for the liquid heat capacity and the heat of vaporization. To
solve the first issue gE mixing rules have been introduced for EoS by Huron and Vidal (1979). Theses mixing
rules have been further developed by Holderbaum and Gmehling (1991) to give the Predictive Soave Redlich
Kwong EoS (PSRK) which makes use of the group contribution gE model UNIFAC. The problem of the bad
values for liquid enthalpies was then tackled by the successor of PSRK, the Volume Translated Peng
Robinson EoS (VTPR). For VTPR the Twu alpha function is introduced which contains three additional
parameters that can be used to fit vapor pressures and liquid heat capacities simultaneously (Ahlers and
Gmehling 2001).
With VTPR an accurate equation of state is available in a process simulation environment to consistently
calculate physical and chemical exergies of streams in thermal separation processes or absorption processes
respectively. The BIP matrix for the main-group interaction is 100% full when using the data from Schmid et al.
(2012) and the Twu parameters of all involved components can be found in the Dortmund Data Bank (DDB).
In Figure 2 Henry coefficients from literature are compared with coefficients calculated by VTPR. The VTPR
results are quite accurate and only for Oxygen a higher solubility is predicted compared to the data given by
Perry et al. (1999). This fact is negligible here, as only a small amount of oxygen is present in the gas
streams.
Figure 2: Comparison of the VTPR model (lines) with literature data from Perry (x-marks) and DDB (o-marks).
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Carbon dioxide and hydrogen sulfide both dissociate in water. This can be taken into account by using an
electrolyte model. However, the most commonly used electrolyte model, eNRTL from Chen and Evans (1986),
is a gE model and thus its application is followed by the same consistency problems regarding the exergy
calculation as discussed above for Henrys law. Furthermore, as most gE models it is not explicitly made to
handle supercritical components.
In order to judge the impact of the dissociation and the cross-effect of the HS- ions and the HCO3- ions (other
ions occur only in very small amounts) on the vapor pressure of the electrolyte solution the gas phase
compositions calculated with eNRTL are compared to those calculated by VTPR. For this purpose, the gas
stream coming from the fermenter (see Table 1) is set into equilibrium with water at different temperatures and
a pressure of 10 bar abs while the calculated gas phase composition is recorded. The results are shown in
Figure 3.
Figure 3: Impact of a) H2S and b) CO2 dissociation on vapor pressure of the electrolyte solution using eNRTL
and VTPR models
There are considerable deviations of up to 0.5 mol % for CO2 and 20 mol ppm for H2S. The differences shrink
with an increasing temperature. However, these deviations have to be accepted as there is currently no EoS
available in a process simulation environment that has electrolyte capabilities. A summary of published
models and current developments in this direction is given by Kontogeorgis and Folas (2010).
2.3 Modelling of the Separation Columns
The components of the bio gas stream are absorbed in water with different velocities. Therefore, it is not
sufficient to model the separation columns with equilibrium stages. To judge the efficiency of absorption and
desorption of the different components the columns are modelled with a mass transfer approach in a first step.
For this rate based column model the diameter of the absorber is set to 0.8 m with 5 m height of 250Y
Mellapack packing. The stripper is modelled with 1 m diameter and 10 m of the same packing. For the
reference conditions these diameters refer to approximately 60% flooding. The resulting efficiency profiles are
given in Figure 4.
Figure 4: Efficiency profiles for the absorber (left) and the stripper (right) column.
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Hydrogen sulfide is absorbed with the highest efficiency while methane is removed with the highest efficiency
in the stripper.
Using the mass transfer model within an optimization procedure bears some problems because on the one
hand the calculation takes more time than an equilibrium calculation and on the other hand columns can flood
what leads to a non-converging flowsheet. Therefore, the Murphree efficiencies are extracted from the mass
transfer calculation and used in an equilibrium stage model.
3. Exergy Analysis
As stated by Ryckebosch et al. (2011) the highest environmental impact of the pressurized water process
comes from its electricity consumption and its fresh water consumption. Both can be evaluated simultaneously
by applying an exergy analysis. The exergetic benefit of the process is then the raise of exergy from the
fermenter gas to the product gas with natural gas quality. The gas raises in physical exergy because of the
compression and in chemical exergy because of its increasing Methane content. The exergetic efficiency of
the process here is defined as the exergy of its product (stream 8) divided by all exergy inputs, which are the
electricity consumptions of the pump and the compressors, the fermenter gas (stream 1), the air (stream 13),
and the make-up steam (stream 19).
3.1 Calculation of the Stream Exergies
Physical and chemical exergy of the process steams are considered while the streams exergy is defined as
the sum of both. The physical exergy rate �̇�𝑃ℎ of a stream can be calculated with Eq(1), where �̇� is the
enthalpy rate, �̇� is the entropy rate, 𝑣 is the vapor fraction, 𝑥 is the liquid phase composition and 𝑦 is the
vapour phase composition. The index 0 refers to the ambient conditions.
�̇�𝑃ℎ = �̇�(𝑇, 𝑃, 𝑣, 𝑥, 𝑦) − �̇�(𝑇0, 𝑃0, 𝑣0, 𝑥0, 𝑦0) − 𝑇0[�̇�(𝑇, 𝑃, 𝑣, 𝑥, 𝑦) − �̇�(𝑇0, 𝑃0, 𝑣0, 𝑥0, 𝑦0)] (1)
Eq(1) already suggests that flash calculations have to be performed at ambient conditions. This is done for all
the streams in the VBA unit operation (99).
A reference environment is required to calculate the chemical exergy of a stream. The chemical exergy is then
the work that theoretically can be extracted from an isothermal and isobaric reaction chamber where the
stream components are converted at ambient conditions to components which exist in the environment and
expanded (or compressed) to the related environment concentration, see e.g. Bejan et al. (1995).
In this work the environment defined by Diederichsen (1991) is used. However, the tabulated values for
chemical exergies of the specific components 𝑒𝑖
𝐶ℎ referring to this environment cannot be directly copied as
Diederichsen used a different reference state for the entropy. The recalculated values which use the reference
state for entropy in CHEMCAD are given in Table 3.
Table 3: Specific chemical exergies of the components recalculated with the CHEMCAD reference entropy.
Component i N2 O2 CH4 CO2 H2S H2O(g) H2O(l)
𝑒𝑖
𝐶ℎ [kJ/mol] 0.743 4.967 824.098 16.109 732.767 8.577 0.017
Neglecting heat of mixing and non-ideal entropy of mixing leads to Eq(2) for the calculation of the specific
chemical exergy of one phase of a stream. Eq(2) shows the calculation for the liquid phase.
𝑒𝐶ℎ = ∑ 𝑥0,𝑖 𝑒𝑖
𝐶ℎ + 𝑅𝑇0
𝑁𝑢𝑚. 𝑜𝑓 𝑐𝑜𝑚𝑝.
𝑖=1
𝑥0,𝑖 𝑙𝑛(𝑥0,𝑖 ) (2)
The specific chemical exergies of both phases are weighted with respect to the vapor fraction at ambient
conditions 𝑣0 and multiplied with the overall flow rate to give the chemical exergy rate �̇�𝐶ℎ.
3.2 Results for the Reference Process
The reference process conditions lead to an exergetic efficiency of 87.2 %. The constraints are more than
fulfilled with a Methane concentration of 96.9 mol% and a Hydrogen Sulfide content of less than 1 mol ppm.
Furthermore, only two of the three available compression stages have been utilized. This gives space for
optimization as presented in the next chapter.
4. Process Optimization
The maximization of the exergetic efficiency of the process leads to a nonlinear constraint optimization
problem with eight design variables. Different approaches can be followed now to solve this problem. A
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comparison of suitable approaches for flowsheet optimization with and without external tools can be found in
Schöneberger and Fricke (2017). Students from all over the world are called to submit their solution to the
problem in the Process Simulation Cup 2016 (PSC 2016). The final winner was determined in February 2017.
A detailed analysis of the so determined optimum is given at the conference. However, some trends can be
pointed out so far:
- A small purge ratio leads to lower exergy losses and thus to a higher efficiency
- The cycle rate, the absorber pressure and the air flow rate are highly coupled regarding their effect
on the product purity
- The compressor duty of the intermediate compression stages can be minimized almost
independently
5. Conclusions
A considerable amount of modeling work and the application of model simplifications are required before an
optimization of the exergetic efficiency of a process is possible. However, once the work is done the
optimization delivers not only better operation conditions but also improves the operational background of the
process and reveals the dependencies of the design variables, the constraints, and the objective function.
Acknowledgments
The authors thank the engineers of DMT Environmental Technology BV for the friendly revision of the process
flowsheet and all the students that submitted solutions to the problem.
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