Next Article in Journal
Parameter Optimization of an Absorption Heat Exchanger with Large Temperature Difference
Next Article in Special Issue
Bioactivities of Waste Cork and Phloem Fractions of Quercus cerris Bark
Previous Article in Journal
Analysis of Dielectric Attached on Sweep Frequency Microwave Heating Uniformity
Previous Article in Special Issue
An Experimental Investigation of Hydrogen Production through Biomass Electrolysis
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Novel Landfill-Gas-to-Biomethane Route Using a Gas–Liquid Membrane Contactor for Decarbonation/Desulfurization and Selexol Absorption for Siloxane Removal

by
Guilherme Pereira da Cunha
,
José Luiz de Medeiros
* and
Ofélia de Queiroz F. Araújo
Escola de Química, Federal University of Rio de Janeiro, CT, E, Ilha do Fundão, Rio de Janeiro 21941-909, RJ, Brazil
*
Author to whom correspondence should be addressed.
Processes 2024, 12(8), 1667; https://doi.org/10.3390/pr12081667
Submission received: 26 June 2024 / Revised: 4 August 2024 / Accepted: 6 August 2024 / Published: 8 August 2024
(This article belongs to the Special Issue Sustainability Use of Wood/Wood Residues and Other Bioenergy Sources)

Abstract

:
A new landfill-gas-to-biomethane process prescribing decarbonation/desulfurization via gas–liquid membrane contactors and siloxane absorption using Selexol are presented in this study. Firstly, an extension for an HYSYS simulator was developed as a steady-state gas–liquid contactor model featuring: (a) a hollow-fiber membrane contactor for countercurrent/parallel contacts; (b) liquid/vapor mass/energy/momentum balances; (c) CO2/H2S/CH4/water fugacity-driven bidirectional transmembrane transfers; (d) temperature changes from transmembrane heat/mass transfers, phase change, and compressibility effects; and (e) external heat transfer. Secondly, contactor batteries using a countercurrent contact and parallel contact were simulated for selective landfill-gas decarbonation/desulfurization with water. Several separation methods were applied in the new process: (a) a water solvent gas–liquid contactor battery for adiabatic landfill-gas decarbonation/desulfurization; (b) water regeneration via high-pressure strippers, reducing the compression power for CO2 exportation; and (c) siloxane absorption with Selexol. The results show that the usual isothermal/isobaric contactor simplification is unrealistic at industrial scales. The process converts water-saturated landfill-gas (CH4 = 55.7%mol, CO2 = 40%mol, H2S = 150 ppm-mol, and Siloxanes = 2.14 ppm-mol) to biomethane with specifications of CH4MIN = 85%mol, CO2MAX = 3%mol, H2SMAX = 10 mg/Nm3, and SiloxanesMAX = 0.03 mg/Nm3. This work demonstrates that the new model can be validated with bench-scale literature data and used in industrial-scale batteries with the same hydrodynamics. Once calibrated, the model becomes economically valuable since it can: (i) predict industrial contactor battery performance under scale-up/scale-down conditions; (ii) detect process faults, membrane leakages, and wetting; and (iii) be used for process troubleshooting.

1. Introduction

Landfills spontaneously release gases that entail sanitary, safety, and environmental issues, such as odors, combustion/explosion risks, and greenhouse gas emissions [1]. These gases are generically known as landfill-gas. Landfill-gas is generated by the anaerobic degradation of organic wastes in landfills and typically contains methane (CH4) (30–65 %mol), carbon dioxide (CO2) (25–47 %mol), hydrogen sulfide (H2S) (30–500 ppm-mol), saturation water, and trace silicon compounds (0.3–36 ppm-mass dry basis). Landfill-gas may also contain nitrogen/oxygen from air, ammonia, and hydrogen [2]. The greenhouse warming potential of CH4 is ≈21 times that of its CO2 counterpart and landfill-gas releases are responsible for ≈17% of worldwide CH4 emissions [3]. Moreover, landfill-gas can be converted into biomethane, so that sustainable landfills can be designed to simultaneously avoid CH4 emissions while exporting biomethane [4] for use as, for example, household fuel-gas, renewable electricity generation [5], vehicular fuel-gas [6], and as a natural gas (NG) substitute [7].
The landfill-gas-to-biomethane process can accelerate energy transition to a low-carbon economy while ensuring energy supply, since solar photovoltaic and wind power energy are naturally intermittent [8] and are in the early development stage [9]. In this case, biomethane is delivered to pipeline networks, as well as associated and non-associated NGs [10], and unconventional NGs from shale-gas [11]. Another possibility is the landfill-gas-to-wire process, which consists of direct electricity generation dismissing purification/transportation [12].
Efficient landfill-gas recovery depends on several factors, such as the coating process, gas drainage, and leachate management [13]. In a capped landfill, landfill-gas is collected and processed to create NGs or biomethane specifications, i.e., via CO2/H2S removal, dehydration, and siloxane removal [14]. The landfill-gas-to-biomethane process starts with decarbonation, which increases the heating value, reduces the transportation volume, and minimizes CO2 emissions from combustion [15]. Desulfurization is the next step aiming at reducing H2S corrosiveness, toxicity, and acid rain potential [16]. Lastly, dehydration is conducted to avoid gas hydrates and condensation in pipelines [17].
Siloxanes are organosilicon compounds inexistent in nature containing Si-O-Si bonds and methyl groups attached to silicon atoms [18]. Landfill-gas siloxanes result from the decomposition of wastes containing silicon compounds (e.g., paints, coats/waxes, and shampoos/cosmetics) [19]. Siloxanes must be removed from landfill-gas because their oxidation in combustion engines creates SiO2 deposits on metallic surfaces causing abrasion. In gas turbines, SiO2 deposits cause blade erosion [20]. Table 1 shows typical landfill-gas siloxanes.
After purification, biomethane is compressed for injection in NG grids [21], while the removed CO2 can be compressed and pipeline transported to oil fields as an enhanced oil recovery (EOR) agent generating revenue that compensates carbon capture and storage (CCS) costs [22]. Since the CO2 source in the landfill is mostly biomass originated from photosynthesis, landfill-gas processing with the CO2-to-EOR process creates bioenergy with CCS; i.e., a BECCS system [23]. BECCS systems combine carbon-neutral bioenergy generation with CCS in suitable geological formations [24], yielding negative CO2 emissions and continuous net CO2 drainage from the atmosphere [25].

1.1. Landfill-Gas Decarbonation: Advantages of Gas–Liquid Membrane Contactors

Different approaches of decarbonation technologies to convert landfill-gas into biomethane are compared, and gas–liquid membrane contactors’ (GLMCs’) advantages are presented to clarify why they are a feasible landfill-gas decarbonation technology.
Common separation technologies for landfill-gas decarbonation present some disadvantages. Firstly, pressure swing adsorption [26] entails a biomethane purity recovery tradeoff [21], and H2S/water should be removed beforehand [2]. Moreover, the operation is intermittent due to adsorbent regeneration demanding control and maintenance. Secondly, packed-column chemical absorption [27] presents several hydraulic issues, such as foaming, flooding, entrainment, obligatory gravity alignment, etc. [28]. Thirdly, membrane-permeation [29] dismisses solvent handling and is gravity indifferent, modular, and recommended for high CO2 fugacity streams [30]. In spite of this, membrane-permeation is limited by permeability-selectivity tradeoff [31] and entails compression costs to generate a driving force [32].
A different concept is the new technology known as gas–liquid membrane contactors (GLMCs), a hybrid of chemical absorption and membrane-permeation that combines the high selectivity of the former with the gravity indifference, modularity, phase segregation, and high transfer area of the latter, without the respective drawbacks [33]. Several positive attributes recommend GLMCs for landfill-gas decarbonation (and desulfurization), such as modularity, linear scale-up, independent control of flowrates [34], gravity indifference, no hydraulic issues, high CO2/CH4 selectivity entailing low CH4 losses [35], and high transfer area per shell [36] due to the high packing density [37]. Compared to packed-column chemical absorption, GLMC size and weight reductions reach 70% and 66%, respectively [38]. GLMC design can prescribe gas (V) and solvent (L) flows in parallel (co-current) contact [39] or in countercurrent contact [40]. V can flow inside hollow-fiber membranes (HFMs) [41] or in the shell [42] and vice versa for L. GLMCs can also work as CO2 strippers for solvent regeneration, albeit they would require a stripping gas in this case, such as nitrogen [43]. A complete description of GLMC principles can be found elsewhere [44].

1.2. GLMC Modeling for Landfill-Gas/Biogas Decarbonation

The literature presents several GLMC modeling approaches for gas decarbonation with varying simplifications. A common simplification involves zero pressure-drop and isothermal operation with feeds/products at the same temperature, as was shown in the one-dimensional (1D) mass balance of Teplyakov et al. [45]. Belaissaoui et al. [46] also developed a simplified isothermal 1D mass transfer model for CO2 physical absorption with pressurized water neglecting heat effects and pressure-drop. Belaissaoui and Favre [47] used the same isothermal 1D mass transfer model with HFM-side and shell-side pressure-drop calculations via Hagen–Poiseuille and Happel equations, respectively. The common simplifying assumptions of Teplyakov et al. [45], Belaissaoui et al. [46], and Belaissaoui and Favre [47] correspond to neglect convective solvent-gas heat transfer and absorption/phase-change thermal effects, consequently overestimating CO2 solubility in the solvent. Gas absorption is always exothermic, i.e., isothermal absorption is unrealistic. Moreover, in Teplyakov et al. [45], Belaissaoui et al. [46], and Belaissaoui and Favre [47], H2O L → V transfer and phase-change thermal effects were also neglected. In a similar context, Fougerit et al. [48] approached isothermal 1D GLMC modeling using OpenFOAM software to investigate decarbonation of CO2-CH4 mixtures.
Li et al. [49] studied GLMC landfill-gas decarbonation with aqueous-Selexol solving the two-dimensional (2D) mass transfer partial differential equations via the finite element method with COMSOL software. However, ideal gas behavior was assumed for P ≥ 12 bar and Selexol has high viscosity and is not recommended for GLMC HFMs. Previously, Li et al. [50] used aqueous K2CO3 for GLMC chemical absorption. Tantikhajorngosol et al. [51] investigated simultaneous isothermal transfers of CO2 (40 %mol) and H2S (500 ppm-mol) to pressurized water using compressibility factors for real gas behavior. Nakhjiri and Heydarinasab [52] compared GLMC decarbonation performances of CO2-CH4 mixtures with aqueous ethylenediamine, aqueous-2-(1-piperazinyl)-ethylamine, and aqueous potassium sarcosinate. All these exothermic absorptions were simulated via COMSOL isothermal modeling assuming ideal gas behavior without pressure drops.
As shown above, typical literature GLMC models for landfill-gas/biogas decarbonation present one or more simplifications, such as (i) isothermal operation; (ii) ideal gas behavior at moderate/high pressures; (iii) negligible H2O L → V transfer and solvent losses; (iv) negligible CH4 V → L transfer; and (v) the Henry’s law for CO2 interfacial vapor–liquid equilibrium (VLE). Such simplifications entail unrealistic results for industrial conditions. Isothermal operations may be valid only for some small-scale low-loading physical absorption. Moreover, bidirectional water transfer (L → V or V → L) cannot be neglected since raw landfill-gas is usually water saturated and the solvent is aqueous [53]. Therefore, complete and thermodynamically rigorous GLMC modeling is rare in the literature. An example is presented by de Medeiros et al. [54] for high-pressure NG decarbonation with aqueous-monoethanolamine-methyldiethanolamine (aqueous-MEA-MDEA) adopting an acid-gas/water/MEA/MDEA reactive VLE [55] and assuming high-pressure V and L compressible flows with full thermodynamics via the Peng–Robinson equation-of-state (PR-EOS) and 1D V and L mass/energy/momentum balances [54].
Machine learning techniques typically use a large amount of data to train black-box models to perform reliable and realistic predictions [56]. In principle, these techniques could also be employed to predict GLMC CO2 absorption [57] with aqueous solvents [58]. However, these models basically rely on a heavy load of information for extensive training in a statistical context and completely ignore mass/energy/momentum conservation and thermodynamics. Consequently, if not sufficiently trained over prohibitively extensive databases of initial/final temperatures/pressures/compositions, they can generate distorted predictions in deterministic processes totally driven by physical principles like distillation columns, heat exchangers, direct-contact columns, chemical reactors, and GLMC separations.

1.3. GLMC Modeling in Process Simulators

Simulation is an important tool for the analysis, monitoring, fault detection, troubleshooting, and design of chemical processes, as evaluations can be performed analytically with high precision, eliminating unnecessary time-consuming and costly experiments [59]. GLMC modeling for professional process simulators is important, because: (i) the model can take advantage of vast numbers of accurate thermodynamic/transport frameworks available in simulators; and (ii) the integration of a GLMC battery with the process flowsheet is immediately performed in the simulator, accelerating industrial design, process analysis, and economic evaluations. Despite this, process simulator GLMC studies are scarce in the literature and still constitute relevant scientific challenges [60].
Hoff et al. [61] developed a GLMC model for flue gas and high-pressure NG decarbonation with mass/energy/momentum balances. The VLE was modeled via Henry’s law with activity coefficients to account for liquid non-ideality. The model was validated with lab-scale experiments. Hoff and Svendsen [62] improved the previous thermodynamic model [61] to investigate the low-pressure decarbonation of offshore gas-turbine flue gas with the SINTEF/NTNU/CO2SIM simulator, and high-pressure NG decarbonation with the process simulator Protreat.
Quek et al. [63] studied high-pressure NG decarbonation with a 2D adiabatic GLMC CO2 transfer model in gPROMS. Quek et al. [64] developed a more complete GLMC model for gPROMS admitting CO2 transfer only, HFM pore-wetting prediction, PR-EOS gas behavior, water evaporation via Raoult’s law, hydrocarbon loading via Henry’s law, simplified energy balance, and no pressure drop. The model was validated against lab-scale and pilot-scale experiments. Quek et al. [65] employed the previous gPROMS model [64] for high-pressure NG decarbonation via the GLMC model evincing heat savings at the expense of inefficient CO2 abatement.
Kerber and Repke [66] studied biogas purification with pressurized water and a flat-sheet membrane GLMC model, considering solvent evaporation and isothermal operation. Villeneuve et al. [67] developed an Aspen Modeler GLMC 1D adiabatic multicomponent transfer model for the comparison of GLMC and packed columns for NG decarbonation with aqueous ammonia considering ideal gas behavior and e-NRTL for the liquid phase. Villeneuve et al. [68] used an older [67] GLMC model to investigate the impact of water condensation on aqueous MEA NG decarbonation.
Usman et al. [69] studied high-pressure pre-combustion GLMC decarbonation using ionic-liquid 1-butyl-3-methylimidazolium tricyanomethanide with a MATLAB 1D transfer model based on resistance-in-series approaches. Posteriorly Usman et al. [70] integrated the MATLAB code with an Aspen-HYSYS simulator via Cape-Open resources, retaining isothermal behavior and Henry’s law VLEs.
Recently, McQuillan et al. [71] developed a one-dimensional distributed GLMC model for the Aspen Custom Modeler to evaluate potassium glycinate as a solvent for direct air capture, but the model presents simplifications, such as isothermal modeling, Henry’s law VLE, no energy balances, and no pressure-drop calculation.
A more efficient way to develop new unit operation models while maintaining access to Aspen-HYSYS rigorous thermodynamic resources is by creating HYSYS unit operation extensions (UOEs) [72]. However, the literature lacks works on GLMC HYSYS modeling, but there are exceptions for CO2-rich NG high-pressure CO2 removal. In da Cunha et al.’s study [73], a GLMC-UOE-1 considered just CO2 transfer, without energy balances and pressure-drop calculations. Posteriorly, da Cunha et al. [74] developed a more complete GLMC-UOE model with multicomponent bidirectional transfer and mass/energy/momentum balances for gas and solvent flows, provided on an HYSYS thermodynamic basis. This study was conducted in offshore high-pressure conditions for the decarbonation of CO2-rich NG via GLMC with aqueous-amines. However, since landfill-gas decarbonation occurs under milder conditions, the literature lacks a specific GLMC-UOE model developed for its specific hydrodynamic conditions.

1.4. Siloxane Removal from Landfill-Gas: The Advantages of Selexol Absorption

Several studies on landfill-gas sweetening consider pressurized-water absorption, but it was shown [75] that water absorption is inefficient for siloxane removal. Läntelä et al. [76] studied landfill-gas sweetening in a water-absorption column evincing a 16.6% efficiency of siloxane removal as follows: TMS/D5 was significantly removed, while L2/L3/D3 was enriched, and L4/L5/D4 remained invariant.
The literature indicates Dimethyl-Ether-Polyethylene-Glycol (DEPG)—Selexol—as a promising siloxane absorbent from landfill-gas, with successful applications [75]. Ryckebosch et al. [77] suggest DEPG also for CO2/H2S removal from landfill-gas, as is performed for synthesis-gas purification [78]. However, DEPG absorption is an expensive technology, i.e., it is not advisable to use DEPG absorption for full landfill-gas purification. Belaissaoui and Favre [47] suggest siloxane and H2S DEPG removal prior to landfill-gas decarbonation with water. However, in this case, the higher H2S content will unnecessarily compete with trace siloxanes. Moreover, Faiz et al. [79] already proved pressurized-water reliability for H2S removal and Li et al. [80] approved pressurized-water packed columns for biogas decarbonation and suggested its replacement by GLMC.
Given these facts, and since landfill-gas is similar to biogas, the present work proposes a new landfill-gas-to-biomethane route; namely: GLMC CO2/H2S removal with pressurized water, followed by siloxane removal via a finishing DEPG-absorption column requiring a low DEPG circulation rate. GLMC CO2/H2S water absorption and siloxane DEPG absorption are both modeled via the HYSYS Acid-Gas Physical-Solvents Thermodynamic Package based on the PC-SAFT equation-of-state [81].

1.5. The Present Work

As shown in Section 1.2, the literature presents recent studies on GLMC modeling for landfill-gas decarbonation, but all bear modeling deficiencies or scope limitations. Moreover, process simulation GLMC studies are still scarce (Section 1.3). A remarkable literature gap is the absence of complete GLMC models with multicomponent mass/energy/momentum balances, sustained by an adequate thermodynamic framework for vapor–liquid equilibrium and reliable predictions of thermodynamic/transport properties.
The present work discloses a novel GLMC HYSYS unit operation extension—GLMC-UOE—developed for the steady-state simulation of multicomponent bidirectional (V → L, L → V) mass/heat transmembrane transfers using a fugacity-difference driving force, rigorous VLE, and thermodynamic modeling via the HYSYS Acid-Gas Physical-Solvents Package and rigorous energy balances taking into account chemical/phase-change/compressibility temperature effects, pressure drops, and non-isothermal operations. GLMC-UOE-simulated CO2/H2S removal from landfill-gas with pressurized water and can handle GLMC countercurrent and parallel contacts generating composition/temperature/pressure profiles. It is demonstrated that the new HYSYS-based contactor model can be validated with bench-scale literature data and used in industrial-scale batteries with the same module hydrodynamics. Moreover, once calibrated, the model becomes economically valuable since it can: (i) predict the performance of industrial contactor batteries under scale-up/scale-down conditions; (ii) detect process faults, membrane leakages, and wetting; and (iii) be used for process troubleshooting.
The literature presents several incomplete landfill-gas/biogas purification studies, as most of them ignore H2S and siloxane removal and/or CH4/H2O bidirectional transfers and/or temperature/pressure changes. In addition, the proposed landfill-gas-to-biomethane route adopts novel intensified operations to reduce space requirements and to improve energy efficiency. As shown in Section 1.4, the selection of an adequate solvent for siloxane removal is a critical step for biomethane specification. Since Selexol is considered efficient, albeit expensive, this process proposes CO2 and H2S removal prior to Selexol absorption for siloxane removal, to reduce the amount of Selexol required. This work proved that Selexol absorption is a promising choice for siloxane removal.
In summary, a novel and complete waste-to-energy landfill-gas-to-biomethane process was solved in Aspen-HYSYS 10.0 considering: (i) an intensified non-isothermal GLMC battery with pressurized water (T = 15 °C, P = 7 bar) for CO2/H2S removal with CO2/H2S/CH4/H2O bidirectional transmembrane transfers and heat effect predictions; (ii) intensified CO2/H2S stripping at P = 30 bar, reducing costs for CO2-to-EOR (P = 300 bar) compression; and (iii) multicomponent siloxane removal using a DEPG absorption (T = 15 °C, P = 7 bar) column.

2. Methods

GLMC-UOE modeling and the landfill-gas-to-biomethane process are addressed.

2.1. GLMC-UOE Development

GLMC-UOE was created using Visual-Basic 6.0 with the embedded HYSYS-Type Library that offers runtime commands to access HYSYS. An external dynamic link library (DLL) file was generated by code compilation. An extension definition file (EDF) was also developed with the Aspen-HYSYS Extension View Editor for DLL linkage to HYSYS and to create user-interface windows for setting GLMC conditions during the simulations in HYSYS PFD. After registering the extension, GLMC-UOE becomes available in a model palette and can access all HYSYS stream/property calculation resources.
GLMC-UOE considers 1D steady-state L and V axial flows for the simulation of GLMC batteries of NM paralleled modules. After being entered into the GLMC module, both L and V can become two-phase There are two models for different L/V contacts: (i) the countercurrent-contact distributed GLMC model (GLMC-CCC-D); and (ii) parallel-contact (co-current) distributed GLMC model (GLMC-PC-D). Both models are built for landfill-gas decarbonation/desulfurization with physical-solvent pressurized water and involve axially discretizing a GLMC module as a succession of M elements in Figure 1a,b, where the countercurrent V/L streams (arrows) are valid for GLMC-CCC-D (Figure 1a) and the parallel V/L streams for GLMC-PC-D (Figure 1b).
Besides the aforesaid concepts, GLMC distributed model assumptions comprise: (i) L/V countercurrent or parallel contacts; (ii) V on the HFM side and L on the shell side; (iii) 1D axial two-phase plug flow for L/V mass/energy/momentum balances; (iv) rigorous VLEs and thermodynamic and transport property calculations via the Aspen-HYSYS Acid-Gas Physical-Solvents Package (PC-SAFT equation-of-state); (v) multicomponent system with CO2/CH4/H2S/H2O and the siloxanes in Table 1 (except L3/L5/TMS; number of components nc = 11), where CO2/CH4/H2S/H2O are the only species for which bidirectional transfers (V → L, L → V) are considered, since siloxanes are heavy species that practically do not transfer to water; (vi) L/V pressure drop; (vii) L/V outlet composition/temperature/pressure calculations; (viii) adiabatic GLMC modules (external heat transfer coefficient UE = 0) with convective transmembrane heat transfer (internal heat transfer coefficient UI ≠ 0); (ix) L/V absorption/compressibility/phase-change heat effects; (x) distributed transmembrane heat and species transfer model using respective driving forces log-mean temperature difference and log-mean species fugacity differences; (xi) direction of positive heat/mass transfers: V → L; (xii) countercurrent GLMC module with discretizationas a cascade of M small countercurrent GLMC elements (Figure 1a) solved with simultaneous corrections [82] Newton–Raphson iterations; (xiii) parallel GLMC module discretized as a cascade of M small parallel GLMC elements (Figure 1b) sequentially solved via element Newton–Raphson iterations; and (xiv) GLMC dependent variables are nc × 1 vectors L ¯ n , V ¯ n of component flowrates (mol/s) that leave element n (n = 1…M) (for the entire battery) as well as the temperatures/pressures L ¯ n , V ¯ n , T L n , T V n , P L n , P V n (n = 1…M). Interfacial heat and mass transfer fluxes for each element were eliminated from the phenomenological relationships in terms of driving forces. The specifications were battery size, GLMC module geometry, and the two nc × 1 vectors of component feeds (mol/s) and their temperatures/pressures, L ¯ 0 , V ¯ M + 1 , T L 0 , T V M + 1 , P L 0 , P V M + 1 for GLMC-CCC-D, and L ¯ 0 , V ¯ 0 , T L 0 , T V 0 , P L 0 , P V 0 for GLMC-PC-D.

2.1.1. Element Mass Balances

For each GLMC element, n (n = 1…M), the transmembrane mass transfer of species k (k = 1…nc) was calculated with the log-mean differences of species k fugacities ( Δ f ^ k , n LM , bar) as the driving force [73] (asymptotically correct as M increases). Supposed constant, the transmembrane species mass transfer coefficients ( Π k , mol/(s.bar.m2), k = 1…nc) were calibrated to adjust transmembrane mass transfer rates for element n ( N k , n , mol/s) as performed by de Medeiros et al. [54], except for the non-transferable siloxanes, where Π k = 0 ,   N k , n = 0 . The GLMC module heat/mass transfer area was A G L M C , while the element transfer area was A G L M C / M . Thus, the single-module separation capacity was pre-defined [44]. In order to determined separation targets, the battery transfer area can be increased (or decreased) with the increase (or decrease) in NM in the GLMC-UOE parameter window. The calibration of mass transfer coefficients, Π k , consists of adjusting CO2/CH4/H2S/H2O transmembrane transfers with GLMC experimental data (Appendix A, Appendix B, Appendix C and Appendix D), as in da Cunha et al. [74]. The set of transferable species is {TS} ≡ {CO2, CH4, H2S, H2O}. Set { T S + } contains species with N k , n > 0 (V → L transfer), while set { T S } contains those with N k , n < 0 (L → V transfer), i.e., { T S } = { T S + } { T S } . The transference direction of a transferable component has to be updated for the elements in the axial direction, so that for k { T S } , f ^ k , n V > f ^ k , n L k { T S + } ; otherwise, k { T S } . In the landfill-gas context, normally { T S + } = {CO2, H2S, CH4} and { T S } = {H2O}. The model does not take into account membrane pore wetting directly, i.e., pore wetting is superseded by the calibration of Π k , k { T S } by combining GLMC-UOE with experimental GLMC data. Once calibrated, Π k , k { T S } can be supposed as invariant to relatively small changes in design and/or hydrodynamics. Moreover, calibrated Π k , k { T S } is invariant for greater (lower) V values with a constant V/L ratio and the same feed temperature/pressure and module geometry, where the module number, NM, increases or decreases proportionally to V in order to maintain module hydrodynamics [74].
Equations (1a)–(1d) and (2a)–(2c) represent the multicomponent mass balances in element n (n = 1…M), where Equations (1c) and (2a) apply to GLMC-CCC-D and Equations (1d) and (2b) for GLMC-PC-D, and f ^ k , n V , f ^ k , n L are species k respective fugacities (bar) in V/L streams that leave n. In Equations (1a)–(2c), the species transfer rates ( N k , n , mol/s) are eliminated using the right-hand sides of Equations (1a)–(1d). Consequently, the mass balances reduce to 2nc element equations presented in Equations (2a) (or (2b)) and (2c). Equation (3) represents the resulting 2nc × 1 vector of mass balances for element n (in GLMC-CCC-D or GLMC-PC-D), whose dependent variables are the element 2nc + 4 outlet variables ( L ¯ n , V ¯ n , T L n , T V n , P L n , P V n ) [82].
N k , n = Π k N M A G L M C / M Δ f ^ k , n L M ( k { T S } , n = 1 M )
N k , n = 0 ( k { T S } , n = 1 M )
Δ f ^ k , n L M = ( f ^ k , n V f ^ k , n 1 L ) ( f ^ k , n + 1 V f ^ k , n L ) ln ( f ^ k , n V f ^ k , n 1 L ) ln ( f ^ k , n + 1 V f ^ k , n L ) ( k { T S } , n = 1 M , G L M C C C C D )
Δ f ^ k , n L M = ( f ^ k , n 1 V f ^ k , n 1 L ) ( f ^ k , n V f ^ k , n L ) ln ( f ^ k , n 1 V f ^ k , n 1 L ) ln ( f ^ k , n V f ^ k , n L ) ( k { T S } , n = 1 M , G L M C P C D )
V k , n + N k , n V k , n + 1 = 0 ( k = 1 n c , n = 1 M , G L M C C C C D )
V k , n + N k , n V k , n 1 = 0 ( k = 1 n c , n = 1 M , G L M C P C D )
L k , n 1 + N k , n L k , n = 0 ( k = 1 n c , n = 1 M )
F ¯ n M B = 0 ¯ ( n = 1 M )

2.1.2. Element Energy Balances

V → L is the positive transfer direction, as previously stated. For element n (n = 1…M), energy balances are presented for V/L streams—of the entire battery—considering the energy transport of V/L inlet/outlet streams, transmembrane convective heat transfer, and the transmembrane energy transfer coupled to the transfer of species k presented by the species k transfer rate in element n times k partial molar enthalpy at origin. The V/L partial molar enthalpies (kJ/mol) of k at origin in element n, < H ¯ k , n V > , < H ¯ k , n L > , are approximated by the arithmetic means of respective inlet/outlet partial molar enthalpies ( < H ¯ k , n V > = ( H ¯ k , n + 1 V + H ¯ k , n V ) / 2 for GLMC-CCC-D, < H ¯ k , n V > = ( H ¯ k , n 1 V + H ¯ k , n V ) / 2 for GLMC-PC-D, and < H ¯ k , n L > = ( H ¯ k , n 1 L + H ¯ k , n L ) / 2 ), which becomes asymptotically correct as M → ∞. V energy balance is expressed via Equation (5a) for GLMC-CCC-D and via Equation (5b) for GLMC-PC-D, while L energy balance is expressed via Equation (5c) for GLMC-CCC-D and via Equation (5d) for GLMC-PC-D, where H ¯ V n , H ¯ L n (kJ/mol) are V/L molar enthalpies leaving element n, and 1 is an nc × 1 vector of ones. Since, partial molar enthalpies are not available in the HYSYS (only molar enthalpies are), they have to be calculated via Equations (4a) and (4b) using mole fraction ( Y k , X k ) derivatives of molar enthalpies numerically generated with finite differences, as in da Cunha et al. [74].
H ¯ k V = H ¯ V i = 1 n c H ¯ V Y i T , P , Y j i + H ¯ V Y k T , P , Y j k ( Y k = V k / 1 ¯ T V ¯ )
H ¯ k L = H ¯ L i = 1 n c H ¯ L X i T , P , X j i + H ¯ L X k T , P , X j k ( X k = L k / 1 ¯ T L ¯ )
Element n transmembrane heat transfer is presented with the transmembrane log-mean temperature difference for element n ( Δ T n L M in Equation (6a) for GLMC-CCC-D, or in Equation (6b) for GLMC-PC-D, also asymptotically correct as M → ∞), the internal heat transfer coefficient U I (kW/m2.K), and N M A G L M C / M the element n battery internal transfer area. For GLMC-CCC-D [74], Equation (6a) is substituted in Equations (5a) and (5c), reducing the energy-balance equations of element n to only Equations (5a) and (5c). Analogously, for GLMC-PC-D [54], Equation (6b) is substituted in Equations (5b) and (5d). In both cases, the resulting 2 × 1 vector of element n energy balances is written as F ¯ n E B = 0 ¯ in Equation (7). The 2nc + 4 outlet variables of element n ( L ¯ n , V ¯ n , T L n , T V n , P L n , P V n ) are the dependent variables of F ¯ n E B = 0 ¯ .
( 1 ¯ T V ¯ n + 1 ) H ¯ V n + 1 ( 1 ¯ T V ¯ n ) H ¯ V n k { T S + } N k H ¯ k , n + 1 V + H ¯ k , n V 2 k { T S } N k H ¯ k , n 1 L + H ¯ k , n L 2 U I N M A G L M C Δ T n L M M = 0
( 1 ¯ T V ¯ n 1 ) H ¯ V n 1 ( 1 ¯ T V ¯ n ) H ¯ V n k { T S + } N k , n H ¯ k , n 1 V + H ¯ k , n V 2 k { T S } N k , n H ¯ k , n 1 L + H ¯ k , n L 2 U I N M A G L M C Δ T n L M M = 0
( 1 ¯ T L ¯ n 1 ) H ¯ L n 1 ( 1 ¯ T L ¯ n ) H ¯ L n + k { T S + } N k H ¯ k , n + 1 V + H ¯ k , n V 2 + k { T S } N k H ¯ k , n 1 L + H ¯ k , n L 2 + U I N M A G L M C Δ T n L M M = 0
( 1 ¯ T L ¯ n 1 ) H ¯ L n 1 ( 1 ¯ T L ¯ n ) H ¯ L n + k { T S + } N k H ¯ k , n 1 V + H ¯ k , n V 2 + k { T S } N k H ¯ k , n 1 L + H ¯ k , n L 2 + U I N M A G L M C Δ T n L M M = 0
Δ T n L M = T V n T L n 1 T V n + 1 T L n ln ( T V n T L n 1 ) ln ( T V n + 1 T L n ) ( G L M C C C C D )
Δ T n L M = T V n T L n T V n 1 T L n 1 ln ( T V n T L n ) ln ( T V n 1 T L n 1 ) ( G L M C P C D )
F ¯ n E B = 0 ¯ ( n = 1 M )

2.1.3. Element Pressure Drop

The element n pressure-drop calculation aims at determining its outlet pressures P L n , P V n . To calculate hydraulic diameters and flow-section areas, HFMs are perceived as rigid with external and internal diameters, d o , d i . The HFM bundle is distributed with HFM centers on an equilateral triangular lattice in a GLMC shell (Figure 2). Hence, the center–center distance, pHF, of adjacent HFMs is constant, and SFREE defines in each triangle the free flow area. In Figure 2a, simple reasoning shows that the number of triangles (NTRI) is asymptotically presented in Equation (8) for N H F HFMs per shell. Since the entire shell transversal section (diameter D) is covered by the triangular lattice without triangle superposition, Equation (9) and Equation (10), respectively, hold for pHF and SFREE. With A C S S (m2) as the shell-side flow section in Equation (11)—which asymptotically ( N H F ) equals NTRI*SFREE—and with HPS as the shell-side hydraulic perimeter, one can see that the shell-side hydraulic diameter, d H S (m), in the second term of Equation (12) is also the hydraulic diameter for SFREE in the last term of Equation (12).
N T R I = 2 N H F 2 N H F
N T R I 3 4 p H F 2 = π D 2 4 p H F = π D 2 / 4 ( 3 / 2 ) ( N H F N H F )
S F R E E = 3 4 p H F 2 ( 1 / 2 ) π d o 2 4
A C S S = π D 2 4 N H F π d o 2 4
d H S = 4 A C S S H P S = 4 S F R E E π d o / 2 = d o 2 3 π p H F d o 2 1
Equation (13) presents the HFM-side hydraulic diameter, d H H F (m), while HFM-side flow-section A C S H F (m2) and HFM outlet gas velocity v V n (m/s) of element n, with ρ ¯ V n (mol/m3) as the gas molar density, follow in Equation (14) and Equation (15), respectively. The HFM-side pressure-drop (head-loss) for element n, h V n H F (Pa), is presented by the Hagen–Poiseuille equation in Equation (16a) for GLMC-CCC-D and in Equation (16b) for GLMC-PC-D, where the property inlet/outlet arithmetic means are used and Z M (m) is the module length. The shell-side pressure-drop across element n, h L n S (Pa), is calculated via the Happel equation (Equation (17)) with the support of Equation (18), Equation (19), and Equation (20), respectively determining the Kozeny factor, κ [83], the outlet liquid velocity, v L n , and the packing ratio, φ [83], where ρ ¯ L n (mol/m3) represents the outlet L mol density. Dynamic viscosities μ V , μ L in Equations (16a), (16b), and (17) are approximated by the viscosity of the predominant phase (gas for V and liquid for L), since V and L streams can become (or not) two phase at an axial position in the GLMC. The GLMC outlet V/L absolute pressures of element n, P V n , P L n (bar), are calculated via Equation (21a) for GLMC-CCC-D, Equation (21b) for GLMC-PC-D, and Equation (22).
d H H F = d i
A C S H F = N H F π d i 2 / 4
v V n = 1 ¯ T V ¯ n ρ ¯ V n N M A C S H F
h V n H F = 32 ( Z M / M ) d H H F 2 v V n + 1 + v V n 2 μ V n + 1 + μ V n 2 ( G L M C C C C D )
h V n H F = 32 ( Z M / M ) d H H F 2 v V n 1 + v V n 2 μ V n 1 + μ V n 2 ( G L M C P C D )
h L n S = 16 κ ( Z M / M ) d H S 2 μ L n 1 + μ L n 2 v L n 1 + v L n 2 φ 2 1 φ 2
κ = 150 φ 4 314.44 φ 3 + 241.67 φ 2 83.039 φ + 15.97
v L n = 1 ¯ T L ¯ n ρ ¯ L n N M A C S S
φ = N H F d o 2 / D 2
P V n + 1 P V n 10 5 h V n H F = 0 ( G L M C C C C D )
P V n 1 P V n 10 5 h V n H F = 0 ( G L M C P C D )
P L n 1 P L n 10 5 h L n S = 0
Equations (8)–(16a) and (16b) can be substituted into Equation (21a) for GLMC-CCC-D or into Equation (21b) for GLMC-PC-D, and Equations (17)–(20) can be substituted into Equation (22), so that the set of pressure-drop equations is reduced to Equations (21a) or (21b) and Equation (22). For element n, the final 2 × 1 vector of pressure-drop equations is expressed by Equation (23). Dependent variables of F ¯ n P D are the 2nc + 4 outlet variables L ¯ n , V ¯ n , T L n , T V n , P L n , P V n of element n.
F ¯ n P D = 0 ¯
For element n, the vector η ¯ n of 2nc + 4 dependent variables and the vector R ¯ n of 2nc + 4 independent equation residues—either for GLMC-CCC-D or for GLMC-PC-D—are presented in Equation (24) for n = 1, …, M. Equation (25) represents the complete vector of (2nc + 4)*M GLMC dependent variables ( η ¯ ) and the complete vector of (2nc + 4)*M GLMC residues ( R ¯ ).
η ¯ n = L ¯ n T L n P L n V ¯ n T V n P V n , R ¯ n = F ¯ n M B F ¯ n E B F ¯ n P D = 0 ¯ ( n = 1 M )
R ¯ ( η ¯ ) = R ¯ 1 R ¯ 2 R ¯ M = 0 ¯ , η ¯ = η ¯ 1 η ¯ 2 η ¯ M

2.1.4. Algorithm to Solve the Countercurrent GLMC Model (GLMC-CCC-D)

The GLMC-CCC-D system of (2nc + 4)*M residues is solved for the (2nc + 4)*M variables in Equation (25) using a cascade Newton–Raphson method known as the simultaneous corrections method [82] originally developed for countercurrent multistage cascades [74]. In this method, iterations occur simultaneously over all elements in order to solve Equations (24) and (25) until convergence. This rather involved and rigorous method will be used to solve GLMC-CCC-D for CO2/H2S removal from landfill-gas using partially analytical and partially numerical Jacobian matrices. It was developed elsewhere [74] and will not be further explained here. Figure 3 presents an algorithm flowchart for solving countercurrent-contact GLMCs (GLMC-CCC-D) (Figure 3a).

2.1.5. Algorithm to Solve the Parallel-Contact GLMC (GLMC-PC-D)

Despite the fact that the system in Equation (24) seems to be the same for GLMC-CCC-D and GLMC-PC-D models, the truth is that there are important differences between them because GLMC-CCC-D is a boundary value problem, while GLMC-PC-D is an initial value problem, which means that the GLMC-PC-D elements can be sequentially solved. As shown by da Cunha et al. [74], to solve GLMC-CCC-D, Newton–Raphson iterations throughout the entire cascade are necessary, while to solve GLMC-PC-D, it is only necessary to conduct Newton–Raphson iterations to solve Equation (24) until convergence for each element, n, sequentially starting from element 1 and terminating at element M. The algorithm for GLMC-PC-D is shown in Equation (26). Figure 3 presents a flowchart of the algorithm for solving a parallel-contact GLMC (GLMC-PC-D) (Figure 3b).
E n t e r F e e d D a t a : L ¯ 0 , V ¯ 0 , T L 0 , T V 0 , P L 0 , P V 0 C r e a t e I n i t i a l E s t i m a t e f o r η ¯ 1 : η ¯ 1 ( 0 ) ( e . g . , η ¯ 1 ( 0 ) = [ L ¯ 0 T L 0 P L 0 V ¯ 0 T V 0 P V 0 ] T ) F o r n = 1 M W i t h η ¯ n ( 0 ) S o l v e R ¯ n = 0 ¯ f o r η ¯ n v i a N e w t o n R a p h s o n M e t h o d I f n < M C r e a t e I n i t i a l E s t i m a t e f o r η ¯ n + 1 : η ¯ n + 1 ( 0 ) ( e . g . , η ¯ n + 1 ( 0 ) = η ¯ n ) E n d
Since GLMC-PC-D elements are small parallel-contact contactors, the elements’ logarithmic mean driving forces, Equations (1d) and (6b), are asymptotically equal to more palatable arithmetic mean driving forces, Equation (27a) and Equation (27b), respectively. That is, Equations (27a) and (27b) can replace Equations (1d) and (6b), respectively.
Δ f ^ k , n L M = ( f ^ k , n 1 V f ^ k , n 1 L ) + ( f ^ k , n V f ^ k , n L ) 2 ( k { T S } , n = 1 M , G L M C P C D )
Δ T n L M = T V n T L n + T V n 1 T L n 1 2 ( n = 1 M , G L M C P C D )

2.1.6. GLMC-UOE Validation

Appendix A, Appendix B, Appendix C and Appendix D report GLMC-UOE validation against the literature data. Appendix A validates GLMC-PC-D via the reproduction of the results of de Medeiros et al. [54] for NG decarbonation with aqueous-MEA-MDEA through a parallel-contact GLMC. Appendix B asymptotically validates an adiabatic GLMC-PC-D for NG decarbonation with aqueous-MEA-MDEA against HYSYS P-H flash. The principle here is that a parallel-contact GLMC-PC-D with a large transfer area asymptotically approaches the response of an adiabatic pressure–enthalpy (P-H) flash with the same parallel feeds. Appendix C asymptotically validates the adiabatic GLMC-CCC-D for NG decarbonation with aqueous-MEA-MDEA against an HYSYS absorption column, because an adiabatic countercurrent-contact GLMC-CCC-D with a large transfer area asymptotically approaches the response of a large adiabatic absorption column with the same countercurrent feed. Appendix D validates the adiabatic GLMC-CCC-D model via the reproduction of the adiabatic countercurrent GLMC results of Belaissaoui and Favre [47] for biogas decarbonation with pressurized water.
In this work, the proposed landfill-gas-to-biomethane route adopts the GLMC modules of Belaissaoui and Favre [47] with a pressurized-water solvent for landfill-gas decarbonation/desulfurization, i.e., the same modules, solvent (T = 15 °C, P = 7 bar), and H2O/CO2 capture-ratio were used. The used GLMC mass transfer coefficients were those obtained from Appendix D for GLMC-CCC-D validation. Since Belaissaoui and Favre [47] adopt isothermal GLMC modeling—which precludes obtaining the GLMC internal heat transfer coefficient, UI, for landfill-gas decarbonation/desulfurization with pressurized water—UI was estimated using an ad hoc asymptotic procedure in Appendix E.

2.2. Landfill-Gas Composition

Landfill-gas CH4/CO2/H2S/H2O compositions (Table 2) were obtained from Läntelä et al. [76]. Regarding the siloxanes (Table 1), the predominant species in landfill-gas are usually D4/D5 [84], and the lack of L3/L5/TMS compositions is not uncommon [20]. Thus, the present work contemplates seven siloxanes: L2/L4/L6/D3/D4/D5/D6 (all in the HYSYS Library). The landfill-gas from landfill LF-1 [20] was used for defining L2/L4/L6/D3/D4/D5/D6 contents (Table 1).

2.3. Landfill-Gas-to-Biomethane Simulation Assumptions

Table 2 shows the simulation assumptions for the proposed landfill-gas-to-biomethane process.

2.4. Landfill-Gas-to-Biomethane Process

The proposed GLMC-based industrial-scale landfill-gas-to-biomethane process is described in this section. Figure 4 shows a block diagram with the interconnection between the process units, which are detailed in the following subsections.

2.4.1. Landfill-Gas Pre-Processing and Compression

Landfill-gas is collected via sufficiently deep landfill wells avoiding air penetration [1], such that O2/N2 contents are negligible. Landfill-gas pre-purification removes solid/liquid particulates.
Water-saturated landfill-gas in atmospheric conditions is compressed to P = 7 bar via 2-staged intercooled compression (Figure 5). In each compression stage, the temperature should not surpass 150 °C, otherwise compressors can be damaged. Intercoolers cool down the gas to 40 °C with cooling-water (CW), whose operating conditions are defined in Table 2. Knock-out vessel aqueous condensates are collected for further treatment (out of scope). Compressed landfill-gas feeds the GLMC battery for decarbonization/desulfurization.

2.4.2. Intensified GLMC Decarbonation/Desulfurization with Pressurized Water

CO2/H2S removal from landfill-gas was performed (Figure 6) in an intensified GLMC battery with NM = 333 modules in countercurrent contact with pressurized water (P = 7 bar, T = 15 °C). The pressure should be higher than atmospheric pressure to increase CO2/H2S fugacities in the landfill-gas, improving the mass transfer driving force. The low solvent temperature (15 °C) improves CO2/H2S solubility [47]. The decarbonized/desulfurized landfill-gas was subjected to siloxane removal, while rich-water was pumped to another intensified operation, namely, a high-pressure CO2 stripper. The rich-water was firstly preheated with hot, lean-water from the stripper reboiler in a thermal-integration water–water heat exchangers, where Δ T Approach = 10   ° C . Unlike ethanolamines, water is insensitive to high temperatures, allowing water regeneration via high pressure (P = 30 bar) CO2/H2S stripping. This drastically reduces CO2 compression costs, but operations above 30 bar are not advisable as column construction costs would be prohibitively higher [82]. The reboiler operated at 233.8 °C and was heated by saturated medium-pressure steam (MPS). The stripper top gas was water-saturated CO2 with some H2S (P = 30 bar) at a total reflux condenser temperature of 40 °C. The lean-water returned to the GLMC battery after cooling with chilled-water (ChW) to T = 15 °C. Chilled-water was produced via a propane refrigeration cycle, whose simulation is not in the scope of the present study. Some excess water (from landfill-gas) was collected, i.e., water make-up was unnecessary.
Water-saturated CO2 from the stripper was compressed from P = 30 bar to P = 150.3 bar via 2-staged intercooled compression (Figure 7). Curiously, knock-out vessels were not necessary, since water vapor is stabilized by high-pressure CO2 at T = 40 °C after cooling with CW. At P = 150.3 bar (T = 40 °C), CO2 becomes supercritical, requiring the pump to reach P = 300 bar for EOR exportation.

2.4.3. Siloxane Removal via DEPG Absorption

Decarbonated/desulfurized landfill-gas from the GLMC passes through DEPG absorption for siloxane removal at P = 7 bar and T = 15 °C (Figure 8). The low temperature favors siloxanes solubility in DEPG. Landfill-gas feeds through the absorber bottom while lean-DEPG is fed at the top. The top gas is biomethane with less than 0.03 mgSiloxanes/Nm3, the limit of capstone microturbines [19]. Rich DEPG passes through heat exchangers for preheating with hot lean DEPG from the DEPG stripper reboiler ( Δ T Approach = 10   ° C ). Rich-DEPG depressurizes to P = 1.17 bar to feed the atmospheric DEPG stripper, whose reboiler operates at T ≤ 175 °C to avoid DEPG thermal decomposition [17]. The total reflux condenser operates at T = 88.7 °C to favor the release of siloxanes in the top gas, which also contains CO2/H2O/H2S/CH4. This stream is cooled down to T = 40 °C, condensing some water in a knock-out vessel. The final residual-gas is flared to avoid CH4 and siloxane emissions. Lean-DEPG is pumped and cooled down to T = 15 °C with chilled-water before returning to the absorber. DEPG make-up is negligible.

3. Landfill-Gas-to-Biomethane Process: Results and Discussion

The process analysis results of the new landfill-gas-to-biomethane process are presented in this section. The design data of all the equipment are shown in Supplement S1 in the Supplementary Materials (reference [88] was used to design heat-exchangers), with TAGs defined in Figure 5, Figure 6, Figure 7 and Figure 8.

3.1. Landfill-Gas Decarbonation/Desulfurization Results

Initially, landfill-gas GLMC decarbonation/desulfurization was simulated separately with a battery of NM = 333 GLMC-CCC-D modules (discretized with M = 5 elements) and with a battery of NM = 333 GLMC-PC-D modules (discretized with M = 100 elements) for comparison using the same capture-ratio ( CR = 443.41 kg H 2 O / kg CO 2 ) and the same compressed landfill-gas (P = 7 bar, T = 40 °C). Table 3 presents V/L inlet/outlet component fugacities for GLMC-CCC-D and GLMC-PC-D, while Table 4 shows inlet/outlet streams and CO2/H2S %Recoveries evincing that, with the same transfer area and capture-ratio, only the GLMC-CCC-D battery can achieve CH4/CO2/H2S biomethane specifications (Table 2). The reason is the higher fugacity-difference driving force in the countercurrent GLMC-CCC-D. This is the reason for using a GLMC-CCC-D battery (Section 2.4) in the landfill-gas-to-biomethane intensified process. The results of the GLMC-PC-D are depicted via the following axial profiles in Figure 9: f ^ C O 2 V , f ^ C O 2 L , f ^ C H 4 V , f ^ C H 4 L (Figure 9a); V %mol CO2/CH4 (Figure 9b); f ^ H 2 S V , f ^ H 2 S L (Figure 9c); V ppm-mol H2S (Figure 9d); f ^ H 2 O V , f ^ H 2 O L (Figure 9e); V %mol H2O (Figure 9f); H2S/CO2 %Recovery and CH4 %Loss (Figure 9g); and CO2/CH4 Selectivity (Figure 9h). It is interesting to see (Table 3) that, for CO2/H2S/CH4 species, f ^ k V > f ^ k L throughout the GLMC-PC-D process due to parallel V → L transfer, while f ^ k V O U T > f ^ k L I N , f ^ k V I N > f ^ k L O U T in the GLMC-CCC-D process due to countercurrent V → L transfer.
GLMC-PC-D fugacity profiles evince the inefficiency of parallel contact, as transfer driving forces start high and then vanish rapidly, limiting CO2/H2S transfer, as seen in Table 3 and Figure 9a–c. In the study of Zhang et al. [39], which tested the increase in the solvent flowrate to decrease CO2 fugacity in the aqueous phase, it was also observed that it contributes to achieving extra CO2 mass transfer driving force. On the other hand, in the GLMC-CCC-D process, a higher driving force is maintained because, as V approaches the outlet, it contacts decreasing f ^ k L values. Moreover, in the GLMC-PC-D process, Figure 9a shows that CO2 transfer slows down at z = 1.4 m, suggesting that the battery is oversized for CO2 transfer via parallel contact, benefiting parasitic CH4 V→L transfers. Meanwhile, at z = 1.95 m, L becomes a two-phase component due to V → L CH4 transfer, and f ^ C H 4 L starts decreasing with a tendency to maintain the CH4 driving force. This effect creates CH4 bubbles in the L stream generating CO2/H2S stripping from the solvent that lowers f ^ C H 4 L . This behavior of CO2 and CH4 driving forces decreases CO2/CH4 selectivity (Figure 9h) in the 2nd half of Z M . All these aspects of the GLMC-PC-D process were similarly observed by de Medeiros et al. [54]. Figure 9e,f depict strong V → L water transfer driven by the lower L temperature ( f ^ H 2 O L < f ^ H 2 O V ). This makes landfill-gas decarbonation/desulfurization self-sufficient in water (i.e., water make-up is unnecessary and the process exports water). This behavior is in accordance with Ghasem et al.’s study [53], which previously demonstrated LV water transfer, and also with Villeneuve et al.’s study [68], which showed bidirectional water transfer, i.e., VL water transfer is also possible. The higher H2S %Recovery rate comparative to its CO2 counterpart (Figure 9g) results from a much higher H2S capture ratio (kgH2O/kgH2S). Table 4 also shows inferior CO2/H2S %Recoveries and a greater CH4 %Loss of GLMC-PC-D compared to GLMC-CCC-D, the former due to poor driving-force utilization and the latter due to the oversized battery for parallel contact benefiting CH4 V→L transfer.
Figure 10 presents the GLMC-PC-D temperature/pressure profiles. Regarding the thermal effects, a paramount factor is the high solvent/gas mass ratio (besides C ¯ P L 2 C ¯ P V ) to provide a high CO2 capture ratio (443.41 kgH2O/kgCO2). As a consequence, TL slightly increases while TV quenches rapidly toward TL (Figure 10a), mainly due to V→L heat transfer and the V . C ¯ P V reduction due to CO2 V → L transfer. Evidently, part of the small TL value increase comes from exothermic physical absorption. The high solvent/gas mass ratio entails a high shell-side L pressure drop compared to the low HFM-side V pressure drop (Figure 10b). These aspects are clear advantages in comparison with the incomplete isothermal/isobaric GLMC model of Belaissaoui and Favre [47].
GLMC-CCC-D’s and GLMC-PC-D’s performance comparison leads to the selection of the GLMC-CCC-D battery in the landfill-gas-to-biomethane process. Thus, the rich water from GLMC-CCC-D is regenerated via high-pressure (P = 30 bar) stripping (Figure 6). Table 5 shows the landfill-gas decarbonation/desulfurization balance and the high-pressure stripper outlets. The stripper regenerates water while releasing CO2 at P = 30 bar, thus lowering CO2-to-EOR compression investment and power. Some authors naively suggest low-pressure CO2 stripping for GLMCs with water [89], evidently entailing much higher compression power for CO2 utilization.

3.2. Siloxane Separation, Process Waste, and Power/Utilities Consumption

GLMC-CCC-D outlet landfill-gas experiences siloxane separation via DEPG absorption (Figure 8), as suggested by Ajhar et al. [75]. Table 6 shows the outlets of the DEPG absorber and DEPG stripper, confirming that biomethane CH4/CO2/H2S/Siloxane specifications were attained. But, biomethane still has 3100 ppm-mol H2O requiring further dehydration. Table 7 depicts siloxane separation balance, waste streams with high siloxane/H2S contents (demanding disposal), and process power/utilities consumption values.

4. Conclusions

A novel GLMC-intensified landfill-gas-to-biomethane process was disclosed, where the decarbonation/desulfurization of 0.5 MMNm3/d of landfill-gas were conducted in a battery of countercurrent GLMC modules using a pressurized-water solvent (P = 7 bar, T = 15 °C). Since water absorption is inefficient for siloxane removal, siloxanes were removed after decarbonation/desulfurization via DEPG absorption (P = 6.9 bar, T = 15 °C) with a small DEPG circulation rate, where DEPG regeneration was conducted via atmospheric stripping requiring a negligible DEPG make-up. A GLMC water solvent is regenerated via intensified high-pressure CO2 stripping, lowering CO2-to-EOR compression costs. This process dismisses water make-up and exports water. There are only two small waste streams with high siloxane/H2S contents. The main revenues comprise clean 0.26 MMNm3/d biomethane and 0.218 MMNm3/d of dense CO2 traded as an EOR agent. Power consumption reaches 9.41 MW, including CO2-to-EOR compression/pumping. To the authors’ knowledge, quantitative multicomponent siloxane removal from decarbonated/desulfurized landfill-gas has never been attempted in the literature. The landfill-gas-to-biomethane process proved to be technically viable, attaining CH4 (>85%mol), CO2 (<3%mol), H2S (<10 mg/Nm3), and siloxane (<0.03 mg/Nm3) specifications. Even if biomethane is burned without CCS, due to CO2 capture from landfill-gas and EOR utilization, the present landfill-gas-to-biomethane results represent BECCS implementation.
To simulate GLMC landfill-gas decarbonation/desulfurization, a thermodynamically rigorous 1D GLMC steady-state model—GLMC-UOE—was developed. GLMC-UOE runs, integrated into Aspen-HYSYS 10.0, as a new HYSYS unit operation using the HYSYS Acid-Gas Physical-Solvents Thermodynamic Package (as well as the HYSYS Chemical Solvents Package). GLMC-UOE can simulate countercurrent-contact 1D-distributed GLMCs with the GLMC-CCC-D model and parallel-contact 1D-distributed GLMCs with the GLMC-PC-D model. Both models were validated via literature comparisons and can handle multicomponent bidirectional transmembrane heat/mass transfers, two-phase V/L flows, and rigorous V/L mass/energy/momentum balances predicting composition/temperature/pressure changes.
After the calibration of component permeances with the literature data, GLMC-UOE can perform rigorous steady-state simulations of landfill-gas decarbonation/desulfurization using a GLMC with pressurized water. This way, GLMC-UOE is useful to design new industrial-scale landfill-gas-to-biomethane projects to perform plant retrofitting, predict process changes during scale-up/scale-down procedures, and to be used for process troubleshooting. The GLMC scale-up was demonstrated in Section 3.1 by using one of the main advantages of the GLMC concept: modularity. This way, the V flowrate can be increased/decreased, as long as the numbers of GLMC modules with the same design are added/removed proportionally in parallel, and the same hydrodynamic conditions for model validation are maintained, i.e., a constant V/L ratio and the same inlet temperatures/pressures.
It is worth noting that GLMC studies on landfill-gas commonly focus only on CO2 removal, ignoring H2S removal, water/CH4 bidirectional transmembrane transfers, temperature effects, and pressure drop, while, in this work, a GLMC was evaluated under multicomponent CO2/CH4/H2S/H2O bidirectional transmembrane transfers with respective thermal effects and T/P changes. The GLMC-PC-D profiles proved that isothermal/isobaric GLMC modeling is inadequate, despite the GLMC literature’s insistence of this assumption.
As a suggestion for future work, an economic analysis can be performed for novel landfill-gas-to-biomethane processes using GLMC decarbonation/desulfurization and siloxane removal via Selexol absorption.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pr12081667/s1; Supplement S1 is in the Supplementary Materials, which can be found in the online version.

Author Contributions

Conceptualization, J.L.d.M. and O.d.Q.F.A.; data curation, G.P.d.C.; formal analysis, G.P.d.C. and J.L.d.M.; funding acquisition, J.L.d.M. and O.d.Q.F.A.; investigation, G.P.d.C. and J.L.d.M.; methodology, J.L.d.M. and O.d.Q.F.A.; project administration, J.L.d.M. and O.d.Q.F.A.; resources, J.L.d.M. and O.d.Q.F.A.; software, G.P.d.C.; supervision, J.L.d.M. and O.d.Q.F.A.; validation, G.P.d.C. and J.L.d.M.; visualization, G.P.d.C. and J.L.d.M.; writing—original draft, G.P.d.C. and J.L.d.M.; writing—review and editing, J.L.d.M. All authors have read and agreed to the published version of the manuscript.

Funding

JL de Medeiros and OQF Araújo acknowledge the financial support received from CNPq-Brazil (313861/2020-0, 312328/2021-4) and from FAPERJ Brazil (E-26/200.522/2023, E-26/201.178/2021).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

1D: one-dimensional; BECCS: bioenergy with CCS; CCS: carbon capture and storage; ChW: chilled water; CW: cooling water; DEPG: Dimethyl-Ether Polyethylene-Glycol; EOR: enhanced oil recovery; GLMC: gas–liquid membrane contactor; GLMC-UOE: GLMC unit operation extension; GLMC-CCC-D countercurrent-contact GLMC distributed model; GLMC-PC-D: parallel-contact GLMC distributed model; HFM: hollow-fiber membrane; LPS: saturated low-pressure steam; MDEA: Methyl-diethanolamine; MEA: Monoethanolamine; MMNm3/d: million normal m3/d; MPS: saturated medium-pressure steam; NG: natural gas; PR-EOS: Peng–Robinson equation of state; VLE: vapor–liquid equilibrium.

Nomenclature

A C S H F , A C S S HFM-side and shell-side flow-section areas (m2)
A G L M C GLMC module transfer area (m2)
D , d H H F , d H S Shell internal diameter, HFM-side hydraulic diamter, and shell-side hydraulic diameter (m)
d i   ,   d o Internal/external HFM diameters (m)
f ^ k , n L   ,   f ^ k , n V L/V species k fugacities at element n (bar)
Δ f ^ k , n LM Logarithmic mean of k transmembrane fugacity difference at element n (bar)
H ¯ k , n L   , H ¯ k , n V L/V kth partial molar enthalpies at element n (kJ/kmol)
H ¯ L n   , H ¯ V n L/V molar enthalpies at element n (kJ/kmol)
h L n S , h V n H F Shell-side and HFM-side head losses for element n (Pa)
L ¯ n Vector of species L molar flowrates leaving element n (mol/s)
M , N H F Number of discretized GLMC elements; number of HFMs per module
N k , n , N M Transmembrane k transfer rate (mol/s) in element n and number of modules
p H F Cente–-center distance of adjacent HFMs (m)
P L n , P V n L/V pressures of element n (bar)
Δ T n L M Logarithmic mean transmembrane temperature difference at element n (K)
T L n , T V n L/V temperatures of element n (K)
U E   ,   U I External/internal GLMC heat transfer coefficients (kJ/(h.m2.K))
V ¯ n   Vector of species V molar flowrates leaving element n (mol/s)
v L n , v V n L/V axial velocities at element n (m/s)
X k , Y k L and V species k mol fraction
Z M , zGLMC length; GLMC axial position (m)
Greek Symbols
φ , κ Packing ratio; Kozeny factor
μ L n , μ V n L/V dynamic viscosities at element n (Pa.s)
Π k Transmembrane k mass transfer coefficient (mol/(s.bar.m2))
ρ ¯ L n , ρ ¯ V n L/V molar densities at element n (kmol/m3)

Appendix A. GLMC-PC-D Model Validation

The GLMC-PC-D model was simulated to reproduce the results of de Medeiros et al. [54], where a desulfurized high-pressure NG was decarbonated using a chemical-absorption parallel-contact GLMC with aqueous-MEA-MDEA. The HYSYS Acid-Gas Chemical-Solvents Thermodynamic Package [90] was used, which is based on e-NRTL for chemical-solvent modeling and PR-EOS for the vapor phase [91]. Besides CO2/CH4/H2O, new species of C2H6/C3H8/C4H10/C5H12/MEA/MDEA were included as transferable species of GLMC-PC-D. The same parameters of de Medeiros et al. [54] were adopted (Table A1). Π H 2 O = Π C O 2 was assumed because HFM Teflon AF1600 has H2O/CO2 selectivity close to 1 [92]. Table A2 shows inlet/outlet streams, while Figure A1 depicts the GLMC-PC-D model’s validation by comparing GLMC profiles. Both show GLMC-PC-D model predictions agreeing with de Medeiros et al. [54].
Table A1. Parameters: GLMC model and transmembrane transfer coefficients [54].
Table A1. Parameters: GLMC model and transmembrane transfer coefficients [54].
ItemValueItemValueItemValueItemValue
D 0.8 m d o 0.502 mm Π C O 2 1.7 × 1 0 4 mol/(s.bar.m2) Π C 5 H 12 10 10 mol/(s.bar.m2)
Z M 2 m N H F 2.188 × 10 6 Π C H 4 5.8 × 1 0 6 mol/(s.bar.m2) Π M E A 2 × 1 0 15 mol/(s.bar.m2)
Volume1.005 m3 A G L M C 6901.4 m2 Π C 2 H 6 1 0 8 mol/(s.bar.m2) Π M D E A 2 × 1 0 15 mol/(s.bar.m2)
N M 40 T a m b 27 °C Π C 3 H 8 1 0 9 mol/(s.bar.m2) Π H 2 O 1.7 × 1 0 4 mol/(s.bar.m2)
d i 0.5 mm U E 5 W/(m2.K) Π C 4 H 10 5 × 1 0 10 mol/(s.bar.m2) U I 2 W/(m2.K)
Table A2. GLMC-PC-D model validation by de Medeiros et al. [54]: inlet/outlet streams.
Table A2. GLMC-PC-D model validation by de Medeiros et al. [54]: inlet/outlet streams.
Solvent InletCO2-Rich NG InletTreated Gas
[54]
Treated-Gas GLMC-PC-D
P (bar)5.050.049.8549.85
T (°C)26.8526.8538.8039.77
MMNm3/d-1.00.8300.829
kg/h17,651---
CO2 (%mol)010.193.503.36
CH4 (%mol)073.2276.576.41
C2H6 (%mol)09.0911.0010.97
C3H8 (%mol)04.255.105.14
C4H10 (%mol)01.782.202.15
C5H12 (%mol)01.471.801.77
H2O (%mol)60000.20
MEA (%mol)2000 3.26 × 10 17
MDEA (%mol)2000 1.23 × 10 17
Figure A1. GLMC-PC-D model validation by de Medeiros et al. [54]: profiles versus z (m): (a) V fugacities (bar); (b) L fugacities (bar); (c) V flowrates (mol/s); (d) L flowrates (mol/s); (e) V composition (%mol); (f) %recovery; (g) CO2/CH4 selectivity.
Figure A1. GLMC-PC-D model validation by de Medeiros et al. [54]: profiles versus z (m): (a) V fugacities (bar); (b) L fugacities (bar); (c) V flowrates (mol/s); (d) L flowrates (mol/s); (e) V composition (%mol); (f) %recovery; (g) CO2/CH4 selectivity.
Processes 12 01667 g0a1

Appendix B. GLMC-PC-D Model Asymptotic Validation with P-H Flash

Large parallel-contact (co-current) adiabatic GLMC batteries should approach the response of an adiabatic gas–liquid direct-contact P-H flash because, with a large parallel-contact area, GLMC outlet streams nearly achieve adiabatic energy-constrained VLE at a given pressure. Thus, an adiabatic GLMC-PC-D model was simulated with a large battery area (54 modules) for an asymptotic comparison with HYSYS 10.0 P-H flash. The same desulfurized high-pressure NG in Appendix A is decarbonated by a chemical-absorption parallel-contact GLMC with aqueous-MEA-MDEA. Table A1 parameters (except UE = 0 for the adiabatic GLMC) and Table A2 inlet streams were adopted with P = 50 bar for both feeds. The HYSYS 10.0 Acid-Gas Chemical-Solvents Package was used in the GLMC-PC-D model and P-H flash. Figure A2 shows GLMC-PC-D fugacity profiles, where the vanishing CO2/CH4/H2O V/L fugacity differences evince that the GLMC battery is sufficiently large enough to achieve VLE for the main transferable species.
Figure A2. GLMC-PC-D V/L fugacity profiles: (a) CO2; (b) CH4; (c) H2O.
Figure A2. GLMC-PC-D V/L fugacity profiles: (a) CO2; (b) CH4; (c) H2O.
Processes 12 01667 g0a2
Table A3 depicts a comparison of the outlet streams, where V/L outlet temperatures and pressures perfectly match, as well as the treated-gas CO2, water, and hydrocarbon outlet compositions. V and L flowrates in kmol/h are in excellent agreement, since the numerical differences are about 0.2%. CO2-rich solvent outlet compositions are also in excellent agreement. The slight differences are because the GLMC-PC-D model approached the asymptotic limit of adiabatic P-H flash, but was not exactly equal to it. For the asymptotic limit, we expected a slightly higher transference of water L → V and hydrocarbon V → L, enhancing the solvent hydrocarbons and lowering solvent CO2 and H2O outlet compositions in %mol.
In Figure A3, HYSYS 10.0 P-H flash outlet streams are outlined with dashed lines along the GLMC axial direction, while L/V GLMC-PC-D profiles appear as solid lines. The results of the large GLMC-PC-D model asymptotically agree with P-H flash, as seen in Table A3 and in Figure A3a–g. Particularly, in Figure A3g, the order of magnitude of the CH4 L flowrate is 10−3 mol/s. In this case, in absolute terms, the error magnitude reaches 0.005 mol/s, a small value. But, in relative terms, the error magnitude reaches 150%. Considering the absolute error magnitude, these numbers are considered to be in good agreement. The temperature profiles of the GLMC-PC-D model also asymptotically converge to the flash temperature (65.9 °C) in Figure A3h.
Table A3. GLMC-PC-D model’s asymptotic validation with HYSYS 10.0 P-H flash: outlet streams.
Table A3. GLMC-PC-D model’s asymptotic validation with HYSYS 10.0 P-H flash: outlet streams.
CO2-Rich Solvent
P-H Flash
CO2-Rich Solvent
GLMC-PC-D
Treated-Gas
P-H Flash
Treated-Gas
GLMC-PC-D
P (bar)50.050.050.050.0
T (°C)65.9065.9065.9065.90
kmol/h440.4439.31796.41797.5
kg/h20,62920,60539,30239,326
CO2 (%mol)15.9415.996.646.64
CH4 (%mol)0.310.1275.7475.74
C2H6 (%mol)0.060.019.409.40
C3H8 (%mol)0.02 4.40 × 10 4 4.404.40
C4H10 (%mol)0.01 6.55 × 10 5 1.521.52
C5H12 (%mol)0.01 1.65 × 10 5 1.841.84
H2O (%mol)49.4349.580.460.46
MEA (%mol)17.1117.15 1.09 × 10 4 5.87 × 10 15
MDEA (%mol)17.1117.15 2.39 × 10 4 4.53 × 10 15
Figure A3. GLMC-PC-D asymptotic validation with HYSYS P-H flash—profiles versus z (m). V (%mol); (a) CO2/CH4 (b) H2O; (c) CO2/CH4 V (mol/s) (d) H2O; V (mol/s); (e) CO2 L (mol/s); (f) H2O L (mol/s); (g) CH4 L (mol/s) (h) Temperatures (°C): V and L.
Figure A3. GLMC-PC-D asymptotic validation with HYSYS P-H flash—profiles versus z (m). V (%mol); (a) CO2/CH4 (b) H2O; (c) CO2/CH4 V (mol/s) (d) H2O; V (mol/s); (e) CO2 L (mol/s); (f) H2O L (mol/s); (g) CH4 L (mol/s) (h) Temperatures (°C): V and L.
Processes 12 01667 g0a3

Appendix C. GLMC-CCC-D Asymptotic Validation with an Adiabatic Absorption Column

Another asymptotic GLMC-UOE validation is conducted to assess countercurrent contact. This time, a large-area adiabatic countercurrent GLMC battery must achieve the results of a large adiabatic absorption column. Thus, the results of a large GLMC-CCC-D battery (9 modules, M = 5 elements) are compared with the counterparts of a large HYSYS 10.0 countercurrent absorber (Stages = 18) with bottom NG feed and top solvent feed. The desulfurized high-pressure NG in Appendix A is decarbonated by a countercurrent chemical-absorption GLMC with aqueous-MEA-MDEA. The HYSYS Acid-Gas Chemical-Solvents Package was adopted used the GLMC-CCC-D model and absorption column. Since the solvent-CO2 weight ratio (capture-ratio) is small in this case, nine GLMC-CCC-D modules would require quite a large battery and an 18-staged column is also large (i.e., absorption is limited to the five upper column stages). The data from Table A1 (except U I and UE) and inlet streams from Table A2 (both feeds at 50 bar) were used. For the adiabatic GLMC-CCC-D model, UE = 0 was chosen and the internal GLMC-CCC-D heat transfer adopted the product U I A I of the GLMC-PC-D model (Appendix B), i.e., U I GLMC - CCC - L = 12 W / ( m 2 . K ) . The GLMC-CCC-D model predictions adhere to the absorber column results (Table A4).
Table A4. GLMC-CCC-D model asymptotic validation with HYSYS absorber: outlet streams.
Table A4. GLMC-CCC-D model asymptotic validation with HYSYS absorber: outlet streams.
ItemCO2-Rich Solvent
Absorber
CO2-Rich Solvent
GLMC-CCC-D
Treated-Gas
Absorber
Treated-Gas
GLMC-CCC-D
P (bar)50.050.050.050.0
T (°C)26.5340.1291.7589.87
kmol/h444.0456.91792.81779.9
kg/h21,18221,18538,74938,746
CO2 (%mol)20.0917.595.606.13
CH4 (%mol)0.110.1175.9376.48
C2H6 (%mol)0.04 1.67 × 10 3 9.429.50
C3H8 (%mol)0.02 6.71 × 10 5 4.414.44
C4H10 (%mol)0.01 9.77 × 10 6 1.521.54
C5H12 (%mol)0.01 2.46 × 10 6 1.861.86
H2O (%mol)45.8149.321.260.05
MEA (%mol)16.9516.49 4.17 × 10 3 4.85 × 10 16
MDEA (%mol)16.9616.49 2.35 × 10 3 4.85 × 10 16

Appendix D. GLMC-CCC-D Model Validation: Water Solvent GLMC

GLMC-CCC-D mass transfer and pressure-drop validations were performed by reproducing the results of Belaissaoui and Favre [47], where CO2 is removed from a desulfurized biogas by a GLMC-Absorber with pressurized water, while a GLMC-Stripper regenerates the solvent with nitrogen (P = 1 atm). In this process [47], CO2/CH4 is transferred with Selectivity CO 2 / CH 4 = 17 , but H2O/N2 is supposedly non-transferable and the GLMC is adiabatic producing isothermal results. Figure A4 depicts the system of Belaissaoui and Favre [47].
Figure A4. GLMC biogas purification [47].
Figure A4. GLMC biogas purification [47].
Processes 12 01667 g0a4
For the GLMC-CCC-D simulation (M = 5 elements), the HYSYS Acid-Gas Physical-Solvents Thermodynamic Package [81] was selected. GLMC modules and the capture ratio of Belaissaoui and Favre [47] were used (Table 2). Transmembrane transfer coefficients of components ( Π k ) were not informed. Thus, Π C O 2 and Π C H 4 were estimated to adhere to the GLMC-CCC-D predictions of Belaissaoui and Favre [47]. Π H 2 O = Π N 2 = 0 was set to turn off H2O/N2 transfers. With 2 W/m2.K [54], the GLMC-CCC-D calibration was performed to reproduce the CO2/CH4 transfers by Belaissaoui and Favre [47] leading to Π CO 2 = 6.5756 × 10 4 mol/(s.bar.m2) and Π CH 4 = 3.8680 × 10 5 mol/(s.bar.m2), which were applied to simulate both the GLMC-Absorber and GLMC-Stripper. Table A5, presenting the outlet streams and process results, summarizes the GLMC-CCC-D model’s validation against Belaissaoui and Favre [47]. It is seen that the GLMC-CCC-D model agrees with Belaissaoui’s and Favre’s [47] results. Thus, Π C O 2 and Π C H 4 were adopted in this work (Table 2) for landfill-gas decarbonation with water.
Table A5. GLMC-CCC-D model validation by Belaissaoui and Favre [47]: outlet streams.
Table A5. GLMC-CCC-D model validation by Belaissaoui and Favre [47]: outlet streams.
Biomethane
[47]
Biomethane
GLMC-CCC-D
Off Gas
[47]
Off-Gas
GLMC-CCC-D
P (bar)--7.996--0.6831
T (°C)1515.251518.37
Nm3/h33.6133.73140.88141.55
CO2 (%mol)2213.413.7
CH4 (%mol)98982.22.2
N2 (%mol)0084.384.1
Process: General Results
Belaissaoui and Favre [47]GLMC-CCC-D
Liquid Pressure Drop (bar)2.432.47
CO2 %Removal 96.6596.53
CH4 %Loss8.78.5
CO2-Rich Solvent Temperature (°C)1515.26
Regenerated Solvent Temperature (°C)1515.03

Appendix E. GLMC Internal Heat Transfer Coefficient for Landfill-Gas Purification

For the simulation of GLMC landfill-gas purification using physical-solvent water, the internal heat transfer coefficient, UI, has to be chosen, because the only available UI = 2 W/m2.K comes from a chemical-absorption GLMC [54]. Thus, UI was estimated by asymptotically comparing the thermal performances of a large adiabatic GLMC-CCC-D battery for landfill-gas purification (Table 2) using water (T = 15 °C, P = 7 bar) against a large adiabatic 30-staged absorption column with water fed through the top (T = 15 °C, P = 7 bar) and landfill-gas through the bottom (P = 7.1 bar). Theoretically, these two large adiabatic countercurrent operations should provide similar composition and thermal results. We emphasize that this stratagem is only being used to estimate a feasible UI, i.e., this is not a calibration. Indeed, there are innumerable UI values that match the large column thermal response. Table 2 shows the inlet streams. The GLMC-CCC-D battery has N M = 780 modules. Π C O 2 and Π C H 4 come from Appendix D, with Π H 2 O = Π C O 2 [92], Π H 2 S = Π C O 2 , and UE = 0. U I was manipulated until the GLMC-CCC-D model’s outlet temperatures approached the column counterparts in Table A6. The estimated UI = 0.09 W/m2.K was adopted for the GLMC-CCC-D and GLMC-PC-D simulations using water for landfill-gas purification (Table 2).
Table A6. GLMC-CCC-D UI estimation with the HYSYS 10.0 absorber.
Table A6. GLMC-CCC-D UI estimation with the HYSYS 10.0 absorber.
ItemCO2-Rich Water
Absorber
CO2-Rich Water
GLMC-CCC-D
Treated-Gas
Absorber
Treated-Gas
GLMC-CCC-D
P (bar)7.16.6647.06.998
T (°C)15.3715.3715.0215.08
MMNm3/d--0.2529570.253672
kg/h7,276,5417,276,586--
CH4 (%mol)0.010.0199.1099.10
CO2 (%mol)0.090.090.640.13
H2S (ppm-mol) 0.346 0.346 5 × 10 5 5 × 10 5
H2O (%mol)99.9099.900.260.26

References

  1. Popov, V. A new landfill system for cheaper landfill gas purification. Renew. Energy 2005, 30, 1021–1029. [Google Scholar] [CrossRef]
  2. Yang, L.; Ge, X.; Wan, C.; Yu, F.; Li, Y. Progress and perspectives in converting biogas to transportation fuels. Renew. Sustain. Energy Rev. 2014, 40, 1133–1152. [Google Scholar] [CrossRef]
  3. Zhou, K.; Chaemchuen, S.; Verpoort, F. Alternative materials in technologies for biogas upgrading via CO2 capture. Renew. Sustain. Energy Rev. 2017, 79, 1414–1441. [Google Scholar] [CrossRef]
  4. Wise, D.L.; Kispert, R.G.; Langton, E.W. A review of bioconversion systems for energy recovery from municipal solid waste part II: Fuel gas production. Resour. Conserv. 1981, 6, 117–136. [Google Scholar] [CrossRef]
  5. Parker, N.; Williams, R.; Dominguez-Faus, R.; Scheitrum, D. Renewable natural gas in California: An assessment of the technical and economic potential. Energy Policy 2017, 111, 235–245. [Google Scholar] [CrossRef]
  6. Nadaletti, W.C.; Cremonez, P.A.; de Souza, S.N.M.; Bariccatti, R.A.; Belli Filho, P.; Secco, D. Potential use of landfill biogas in urban bus fleet in the Brazilian states: A review. Renew. Sustain. Energy Rev. 2015, 41, 277–283. [Google Scholar] [CrossRef]
  7. Keogh, N.; Corr, D.; Monaghan, R.F.D. Biogenic renewable gas injection into natural gas grids: A review of technical and economic modelling studies. Renew. Sustain. Energy Rev. 2022, 168, 112818. [Google Scholar] [CrossRef]
  8. Schipfer, F.; Mäki, E.; Schmieder, U.; Lange, N.; Schildhauer, T.; Hennig, C.; Thrän, D. Status of and expectations for flexible bioenergy to support resource efficiency and to accelerate the energy transition. Renew. Sustain. Energy Rev. 2022, 158, 112094. [Google Scholar] [CrossRef]
  9. Araújo, O.Q.F.; de Medeiros, J.L. How is the transition away from fossil fuels doing, and how will the low-carbon future unfold? Clean Technol. Environ. Policy 2021, 23, 1385–1388. [Google Scholar] [CrossRef]
  10. Araújo, O.Q.F.; Boa Morte, I.B.; Borges, C.L.T.; Morgado, C.R.V.; de Medeiros, J.L. Beyond clean and affordable transition pathways: A review of issues and strategies to sustainable energy supply. Int. J. Electr. Power Energy Syst. 2024, 155, 109544. [Google Scholar] [CrossRef]
  11. Yu, H.; Xu, H.; Fan, J.; Zhu, Y.B.; Wang, F.; Wu, H. Transport of shale gas in microporous/nanoporous media: Molecular to pore-scale simulations. Energy Fuels 2021, 35, 911–943. [Google Scholar] [CrossRef]
  12. Brigagão, G.V.; de Medeiros, J.L.; Araújo, O.Q.F.; Mikulčić, H.; Duić, N. A zero-emission sustainable landfill-gas-to-wire oxyfuel process: Bioenergy with carbon capture and sequestration. Renew. Sustain. Energy Rev. 2021, 138, 110686. [Google Scholar] [CrossRef]
  13. Ruoso, A.C.; Nora, M.D.; Siluk, J.C.M.; Ribeiro, J.L.D. The impact of landfill operation factors on improving biogas generation in Brazil. Renew. Sustain. Energy Rev. 2022, 154, 111868. [Google Scholar] [CrossRef]
  14. Khan, M.U.; Lee, J.T.E.; Bashir, M.A.; Dissanayake, P.D.; Ok, Y.S.; Tong, Y.W.; Shariati, M.A.; Wu, S.; Ahring, B.K. Current status of biogas upgrading for direct biomethane use: A review. Renew. Sustain. Energy Rev. 2021, 149, 111343. [Google Scholar] [CrossRef]
  15. Araújo, O.Q.F.; Reis, A.C.; de Medeiros, J.L.; do Nascimento, J.F.; Grava, W.M.; Musse, A.P.S. Comparative analysis of separation technologies for processing carbon dioxide rich natural gas in ultra-deepwater oil fields. J. Clean. Prod. 2017, 155, 12–22. [Google Scholar] [CrossRef]
  16. Keshavarz, P.; Fathikalajahi, J.; Ayatollahi, S. Mathematical modeling of the simultaneous absorption of carbon dioxide and hydrogen sulfide in a hollow fiber membrane contactor. Sep. Purif. Technol. 2008, 63, 145–155. [Google Scholar] [CrossRef]
  17. Mokhatab, S.; Poe, W.A. Handbook of Natural Gas Transmission and Processing—Principles and Practices, 2nd ed.; Gulf Professional Publishing: Houston, TX, USA, 2012. [Google Scholar]
  18. Oshita, K.; Omori, K.; Takaoka, M.; Mizuno, T. Removal of siloxanes in sewage sludge by thermal treatment with gas stripping. Energy Convers. Manag. 2014, 81, 290–297. [Google Scholar] [CrossRef]
  19. de Arespacochaga, N.; Valderrama, C.; Raich-Montiu, J.; Crest, M.; Mehta, S.; Cortina, J.L. Understanding the effects of the origin, occurrence, monitoring, control, fate and removal of siloxanes on the energetic valorization of sewage biogas—A review. Renew. Sustain. Energy Rev. 2015, 52, 366–381. [Google Scholar] [CrossRef]
  20. Surita, S.C.; Tansel, B. Preliminary investigation to characterize deposits forming during combustion of biogas from anaerobic digesters and landfills. Renew. Energy 2015, 80, 674–681. [Google Scholar] [CrossRef]
  21. Scholz, M.; Melin, T.; Wessling, M. Transforming biogas into biomethane using membrane technology. Renew. Sustain. Energy Rev. 2013, 17, 199–212. [Google Scholar] [CrossRef]
  22. Leung, D.Y.C.; Caramanna, G.; Maroto-Valer, M.M. An overview of current status of carbon dioxide capture and storage technologies. Renew. Sustain. Energy Rev. 2014, 39, 426–443. [Google Scholar] [CrossRef]
  23. Pour, N.; Webley, P.A.; Cook, P.J. Potential for using municipal solid waste as a resource for bioenergy with carbon capture and storage (BECCS). Int. J. Greenh. Gas. Control 2018, 68, 1–15. [Google Scholar] [CrossRef]
  24. Milão, R.F.D.; Carminati, H.B.; Araújo, O.Q.F.; de Medeiros, J.L. Thermodynamic, financial and resource assessments of a large-scale sugarcane-biorefinery: Prelude of full bioenergy carbon capture and storage scenario. Renew. Sustain. Energy Rev. 2019, 113, 109251. [Google Scholar] [CrossRef]
  25. Kemper, J. Biomass and carbon dioxide capture and storage: A review. Int. J. Greenh. Gas. Control 2015, 40, 401–430. [Google Scholar] [CrossRef]
  26. Gong, H.; Zhou, S.; Chen, Z.; Chen, L. Effect of volatile organic compounds on carbon dioxide adsorption performance via pressure swing adsorption for landfill gas upgrading. Renew. Energy 2019, 135, 811–818. [Google Scholar] [CrossRef]
  27. Gaur, A.; Park, J.W.; Maken, S.; Song, H.J.; Park, J.J. Landfill gas (LFG) processing via adsorption and alkanolamine absorption. Fuel Process. Technol. 2010, 91, 635–640. [Google Scholar] [CrossRef]
  28. Tay, W.H.; Lau, K.K.; Lai, L.S.; Shariff, A.M.; Wang, T. Current development and challenges in the intensified absorption technology for natural gas purification at offshore condition. J. Nat. Gas. Sci. Eng. 2019, 71, 102977. [Google Scholar] [CrossRef]
  29. Tao, J.; Wang, J.; Zhu, L.; Chen, X. Integrated design of multi-stage membrane separation for landfill gas with uncertain feed. J. Membr. Sci. 2019, 590, 117260. [Google Scholar] [CrossRef]
  30. Baker, R.W.; Lokhandwala, K. Natural Gas Processing with Membranes: An Overview. Ind. Eng. Chem. Res. 2008, 47, 2109–2121. [Google Scholar] [CrossRef]
  31. Moreira, L.C.; Borges, P.O.; Cavalcante, R.M.; Young, A.F. Simulation and economic evaluation of process alternatives for biogas production and purification from sugarcane vinasse. Renew. Sustain. Energy Rev. 2022, 163, 112532. [Google Scholar] [CrossRef]
  32. Siagian, U.W.R.; Raksajati, A.; Himma, N.F.; Khoiruddin, K.; Wenten, I.G. Membrane-based carbon capture technologies: Membrane gas separation vs. membrane contactor. J. Nat. Gas. Sci. Eng. 2019, 67, 172–195. [Google Scholar] [CrossRef]
  33. Buonomenna, M.G.; Bae, J. Membrane processes and renewable energies. Renew. Sustain. Energy Rev. 2015, 43, 1343–1398. [Google Scholar] [CrossRef]
  34. Ramezani, R.; Di Felice, L.; Gallucci, F. A Review on Hollow Fiber Membrane Contactors for Carbon Capture: Recent Advances and Future Challenges. Processes 2022, 10, 2103. [Google Scholar] [CrossRef]
  35. Sirkar, K.K.; Fane, A.G.; Wang, R.; Wickramasinghe, S.R. Process intensification with selected membrane processes. Chem. Eng. Process. Process Intensif. 2015, 87, 16–25. [Google Scholar] [CrossRef]
  36. Asfand, F.; Bourouis, M. A review of membrane contactors applied in absorption refrigeration systems. Renew. Sustain. Energy Rev. 2015, 45, 173–191. [Google Scholar] [CrossRef]
  37. Lee, Y.; Park, Y.J.; Lee, J.; Bae, T.H. Recent advances and emerging applications of membrane contactors. Chem. Eng. J. 2023, 461, 141948. [Google Scholar] [CrossRef]
  38. Amaral, R.A.; Habert, C.A.; Borges, C.P. Performance evaluation of composite and microporous gas-liquid membrane contactors for CO2 removal from a gas mixture. Chem. Eng. Process. Process Intensif. 2016, 102, 202–209. [Google Scholar] [CrossRef]
  39. Zhang, H.Y.; Wang, R.; Liang, D.T.; Tay, J.H. Modeling and experimental study of CO2 absorption in a hollow fiber membrane contactor. J. Membr. Sci. 2006, 279, 301–310. [Google Scholar] [CrossRef]
  40. Yin, Y.; Cao, Z.; Gao, H.; Sema, T.; Na, Y.; Xiao, M.; Liang, Z.; Tontiwachwuthikul, P. Experimental Measurement and Modeling Prediction of Mass Transfer in a Hollow Fiber Membrane Contactor Using Tertiary Amine Solutions for CO2 Absorption. Ind. Eng. Chem. Res. 2022, 61, 9632–9643. [Google Scholar] [CrossRef]
  41. Lee, H.J.; Kim, M.K.; Park, J.H.; Magnone, E. Temperature and pressure dependence of the CO2 absorption through a ceramic hollow fiber membrane contactor module. Chem. Eng. Process. Process Intensif. 2020, 150, 107871. [Google Scholar] [CrossRef]
  42. Saidi, M.; Inaloo, E.B. CO2 removal using 1DMA2P solvent via membrane technology: Rate based modeling and sensitivity analysis. Chem. Eng. Process. Process Intensif. 2021, 166, 108464. [Google Scholar] [CrossRef]
  43. Koonaphapdeelert, S.; Wu, Z.; Li, K. Carbon dioxide stripping in ceramic hollow fibre membrane contactors. Chem. Eng. Sci. 2009, 64, 1–8. [Google Scholar] [CrossRef]
  44. da Cunha, G.P.; de Medeiros, J.L.; Araújo, O.Q.F. Carbon Capture from CO2-Rich Natural Gas via Gas-Liquid Membrane Contactors with Aqueous-Amine Solvents: A Review. Gases 2022, 2, 98–133. [Google Scholar] [CrossRef]
  45. Teplyakov, V.V.; Okunev, A.Y.; Laguntsov, N.I. Computer design of recycle membrane contactor systems for gas separation. Sep. Purif. Technol. 2007, 57, 450–454. [Google Scholar] [CrossRef]
  46. Belaissaoui, B.; Claveria-Baro, J.; Lorenzo-Hernando, A.; Zaidiza, D.A.; Chabanon, E.; Castel, C.; Rode, S.; Roizard, D.; Favre, E. Potentialities of a dense skin hollow fiber membrane contactor for biogas purification by pressurized water absorption. J. Membr. Sci. 2016, 513, 236–249. [Google Scholar] [CrossRef]
  47. Belaissaoui, B.; Favre, E. Novel dense skin hollow fiber membrane contactor based process for CO2 removal from raw biogas using water as absorbent. Sep. Purif. Technol. 2018, 193, 112–126. [Google Scholar] [CrossRef]
  48. Fougerit, V.; Pozzobon, V.; Pareau, D.; Théoleyre, M.A.; Stambouli, M. Experimental and numerical investigation binary mixture mass transfer in a gas-liquid membrane contactor. J. Membr. Sci. 2019, 572, 1–11. [Google Scholar] [CrossRef]
  49. Li, Y.; Wang, L.; Jin, P.; Song, X.; Zhan, X. Removal of carbon dioxide from pressurized landfill gas by physical absorbents using a hollow fiber membrane contactor. Chem. Eng. Process. Process Intensif. 2017, 121, 149–161. [Google Scholar] [CrossRef]
  50. Li, Y.; Wang, L.; Hu, X.; Jin, P.; Song, X. Surface modification to produce superhydrophobic hollow fiber membrane contactor to avoid membrane wetting for biogas purification under pressurized conditions. Sep. Purif. Technol. 2018, 194, 222–230. [Google Scholar] [CrossRef]
  51. Tantikhajorngosol, P.; Laosiripojana, N.; Jiraratananon, R.; Assabumrungrat, S. A modeling study of module arrangement and experimental investigation of single stage module for physical absorption of biogas using hollow fiber membrane contactors. J. Membr. Sci. 2018, 549, 283–294. [Google Scholar] [CrossRef]
  52. Nakhjiri, A.T.; Heydarinasab, A. Computational simulation and theoretical modeling of CO2 separation using EDA, PZEA and PS absorbents inside the hollow fiber membrane contactor. J. Ind. Eng. Chem. 2019, 78, 106–115. [Google Scholar] [CrossRef]
  53. Ghasem, N.; Al-Marzouqi, M.; Rahim, N.A. Modeling of CO2 absorption in a membrane contactor considering solvent evaporation. Sep. Purif. Technol. 2013, 110, 1–10. [Google Scholar] [CrossRef]
  54. de Medeiros, J.L.; Nakao, A.; Grava, W.M.; Nascimento, J.F.; Araújo, O.Q.F. Simulation of an offshore natural gas purification process for CO2 removal with gas-liquid contactors employing aqueous solutions of ethanolamines. Ind. Eng. Chem. Res. 2013, 52, 7074–7089. [Google Scholar] [CrossRef]
  55. de Medeiros, J.L.; Barbosa, L.C.; Araújo, O.Q.F. Equilibrium approach for CO2 and H2S absorption with aqueous solutions of alkanolamines: Theory and parameter estimation. Ind. Eng. Chem. Res. 2013, 52, 9203–9226. [Google Scholar] [CrossRef]
  56. Huang, M.; Xu, H.; Yu, H.; Zhang, H.; Micheal, M.; Yuan, X.; Wu, H. Fast prediction of methane adsorption in shale nanopores using kinetic theory and machine learning algorithm. Chem. Eng. J. 2022, 446, 137221. [Google Scholar] [CrossRef]
  57. Aguado, D.; Noriega-Hevia, G.; Serralta, J.; Seco, A. Using machine learning techniques to predict ammonium concentration in membrane contactors for nitrogen recovery as a valuable resource. Eng. Appl. Artif. Intell. 2023, 126, 107330. [Google Scholar] [CrossRef]
  58. Yarveicy, H.; Ghiasi, M.M.; Mohammadi, A.H. Performance evaluation of the machine learning approaches in modeling of CO2 equilibrium absorption in piperazine aqueous solution. J. Mol. Liq. 2018, 255, 375–383. [Google Scholar] [CrossRef]
  59. Yu, H.; Zhu, Y.; Jin, X.; Liu, H.; Wu, H. Multiscale simulations of shale gas transport in micro/nano-porous shale matrix considering pore structure influence. J. Nat. Gas. Sci. Eng. 2019, 64, 28–40. [Google Scholar] [CrossRef]
  60. Ng, E.L.H.; Lau, K.K.; Lau, W.J.; Ahmad, F. Holistic review on the recent development in mathematical modelling and process simulation of hollow fiber membrane contactor for gas separation process. J. Ind. Eng. Chem. 2021, 104, 231–257. [Google Scholar] [CrossRef]
  61. Hoff, K.A.; Juliussen, O.; Falk-Pedersen, O.; Svendsen, H.F. Modeling and Experimental Study of Carbon Dioxide Absorption in Aqueous Alkanolamine Solutions Using a Membrane Contactor. Ind. Eng. Chem. Res. 2004, 43, 4908–4921. [Google Scholar] [CrossRef]
  62. Hoff, K.A.; Svendsen, H.F. CO2 absorption with membrane contactors vs. packed absorbers-Challenges and opportunities in post combustion capture and natural gas sweetening. Energy Procedia 2013, 37, 952–960. [Google Scholar] [CrossRef]
  63. Quek, V.C.; Shah, N.; Chachuat, B. Modeling for design and operation of high-pressure membrane contactors in natural gas sweetening. Chem. Eng. Res. Des. 2018, 132, 1005–1019. [Google Scholar] [CrossRef]
  64. Quek, V.C.; Shah, N.; Chachuat, B. Plant-wide assessment of high-pressure membrane contactors in natural gas sweetening—Part I: Model development. Sep. Purif. Technol. 2021, 258, 117898. [Google Scholar] [CrossRef]
  65. Quek, V.C.; Shah, N.; Chachuat, B. Plant-wide assessment of high-pressure membrane contactors in natural gas sweetening—Part II: Process analysis. Sep. Purif. Technol. 2021, 258, 117938. [Google Scholar] [CrossRef]
  66. Kerber, J.; Repke, J.U. Mass transfer and selectivity analysis of a dense membrane contactor for upgrading biogas. J. Membr. Sci. 2016, 520, 450–464. [Google Scholar] [CrossRef]
  67. Villeneuve, K.; Zaidiza, D.A.; Roizard, D.; Rode, S. Modeling and simulation of CO2 capture in aqueous ammonia with hollow fiber composite membrane contactors using a selective dense layer. Chem. Eng. Sci. 2018, 190, 345–360. [Google Scholar] [CrossRef]
  68. Villeneuve, K.; Hernandez, A.A.T.; Zaidiza, D.A.; Roizard, D.; Rode, S. Effects of water condensation on hollow fiber membrane contactor performance for CO2 capture by absorption into a chemical solvent. J. Membr. Sci. 2018, 556, 365–373. [Google Scholar] [CrossRef]
  69. Usman, M.; Dai, Z.; Hillestad, M.; Deng, L. Mathematical modeling and validation of CO2 mass transfer in a membrane contactor using ionic liquids for pre-combustion CO2 capture. Chem. Eng. Res. Des. 2017, 123, 377–387. [Google Scholar] [CrossRef]
  70. Usman, M.; Hillestad, M.; Deng, L. Assessment of a membrane contactor process for pre-combustion CO2 capture by modelling and integrated process simulation. Int. J. Greenh. Gas. Control 2018, 71, 95–103. [Google Scholar] [CrossRef]
  71. McQuillan, R.V.; Momeni, A.; Alivand, M.S.; Stevens, G.W.; Mumford, K.A. Evaluation of potassium glycinate as a green solvent for direct air capture and modelling its performance in hollow fiber membrane contactors. Chem. Eng. J. 2024, 481, 148764. [Google Scholar] [CrossRef]
  72. Arinelli, L.O.; Trotta, T.A.F.; Teixeira, A.M.; de Medeiros, J.L.; Araújo, O.Q.F. Offshore processing of CO2 rich natural gas with supersonic separator versus conventional routes. J. Nat. Gas. Sci. Eng. 2017, 46, 199–221. [Google Scholar] [CrossRef]
  73. da Cunha, G.P.; de Medeiros, J.L.; Araújo, O.Q.F. Technical evaluation of the applicability of gas-liquid membrane contactors for CO2 removal from CO2 rich natural gas streams in offshore rigs. Mater. Sci. Forum 2019, 965, 29–38. [Google Scholar] [CrossRef]
  74. da Cunha, G.P.; de Medeiros, J.L.; Araújo, O.Q.F. Offshore decarbonation of CO2-rich natural gas intensified via gas-liquid membrane contactors with blended aqueous-amines. Chem. Eng. Process. Process Intensif. 2023, 191, 109462. [Google Scholar] [CrossRef]
  75. Ajhar, M.; Travesset, M.; Yüce, S.; Melin, T. Siloxane removal from landfill and digester gas—A technology overview. Bioresour. Technol. 2010, 101, 2913–2923. [Google Scholar] [CrossRef] [PubMed]
  76. Läntelä, J.; Rasi, S.; Lehtinen, J.; Rintala, J. Landfill gas upgrading with pilot-scale water scrubber: Performance assessment with absorption water recycling. Appl. Energy 2012, 92, 307–314. [Google Scholar] [CrossRef]
  77. Ryckebosch, E.; Drouillon, M.; Vervaeren, H. Techniques for transformation of biogas to biomethane. Biomass Bioenergy 2011, 35, 1633–1645. [Google Scholar] [CrossRef]
  78. Ashrafi, O.; Bashiri, H.; Esmaeili, A.; Sapoundjiev, H.; Navarri, P. Ejector integration for the cost effective design of the SelexolTM process. Energy 2018, 162, 380–392. [Google Scholar] [CrossRef]
  79. Faiz, R.; Li, K.; Al-Marzouqi, M. H2S absorption at high pressure using hollow fibre membrane contactors. Chem. Eng. Process. Process Intensif. 2014, 83, 33–42. [Google Scholar] [CrossRef]
  80. Li, M.; Zhu, Z.; Zhou, M.; Jie, X.; Wang, L.; Kang, G.; Cao, Y. Removal of CO2 from biogas by membrane contactor using PTFE hollow fibers with smaller diameter. J. Membr. Sci. 2021, 627, 119232. [Google Scholar] [CrossRef]
  81. Dyment, J.; Watanasiri, S. Acid Gas Cleaning Using DEPG Physical Solvents: Validation with Experimental and Plant Data. White Paper; Aspen Technology Inc.: Houston, TX, USA, 2015; Available online: https://www.aspentech.com/en/resources/white-papers/acid-gas-cleaning-using-depg-physical-solvents---validation-with-experimental-and-plant-data (accessed on 28 October 2022).
  82. Seader, J.D.; Henley, E.J. Separation Process Principles, 2nd ed.; John Wiley & Sons, Inc.: New York, NY, USA, 2006. [Google Scholar]
  83. Chabanon, E.; Bounaceur, R.; Castel, C.; Rode, S.; Roizard, D.; Favre, E. Pushing the limits of intensified CO2 post-combustion capture by gas-liquid absorption through a membrane contactor. Chem. Eng. Process. Process Intensif. 2015, 91, 7–22. [Google Scholar] [CrossRef]
  84. Tansel, B.; Surita, S.C. Managing siloxanes in biogas-to-energy facilities: Economic comparison of pre- vs post-combustion practices. Waste Manag. 2019, 96, 121–127. [Google Scholar] [CrossRef] [PubMed]
  85. ANP—Agência Nacional do Petróleo, Gás Natural e Biocombustíveis. ANP N⁰16 (17.6.2008)—DOU 18.6.2008. 2008. Available online: https://atosoficiais.com.br/anp/resolucao-n-16-2008-dispoe-sobre-as-informacoes-constantes-dos-documentos-da-qualidade-e-o-envio-dos-dados-da-qualidade-dos-combustiveis-produzidos-no-territorio-nacional-ou-importados-e-da-outras-providencias (accessed on 5 February 2024).
  86. Ecker, A.M.; Klein, H.; Peschel, A. Systematic and efficient optimization-based design of a process for CO2 removal from natural gas. Chem. Eng. J. 2022, 445, 136178. [Google Scholar] [CrossRef]
  87. Smith, J.M.; Van Ness, H.C.; Abbott, M.M. Introduction to Chemical Engineering Thermodynamics, 7th ed.; McGraw-Hill Book: New York, NY, USA, 2005. [Google Scholar]
  88. Kern, D.Q. Process Heat Transfer; McGraw-Hill Book: New York, NY, USA, 1950. [Google Scholar]
  89. Mehdipourghazi, M.; Barati, S.; Varaminian, F. Mathematical modeling and simulation of carbon dioxide stripping from water using hollow fiber membrane contactors. Chem. Eng. Process. Process Intensif. 2015, 95, 159–164. [Google Scholar] [CrossRef]
  90. Chang, Y.T.; Dyment, J. Jump Start Guide: Acid Gas Cleaning in Aspen HYSYS®. A Brief Tutorial (and Supplement to and Online Documentation); Aspen Technology Inc.: Houston, TX, USA, 2018; Available online: https://www.aspentech.com/en/resources/jump-start-guide/acid-gas-cleaning-in-aspen-hysys (accessed on 5 October 2022).
  91. Dyment, J.; Watanasiri, S. Acid Gas Cleaning Using Amine Solvents: Validation with Experimental and Plant Data. White Paper; Aspen Technology Inc.: Houston, TX, USA, 2018; Available online: https://www.aspentech.com/en/resources/white-papers/acid-gas-cleaning-using-amine-solvents---validation-with-experimental-and-plant-data (accessed on 5 February 2024).
  92. Scholes, C.A.; Kanehashi, S.; Stevens, G.W.; Kentish, S.E. Water permeability and competitive permeation with CO2 and CH4 in perfluorinated polymeric membranes. Sep. Purif. Technol. 2015, 147, 203–209. [Google Scholar] [CrossRef]
Figure 1. Representations of countercurrent and parallel GLMC modules as cascades of M elements (streams are numbered by the origin element). GLMC battery feed data: (a) L ¯ 0 , V ¯ M + 1 , T L 0 , T V M + 1 , P L 0 , P V M + 1 (GLMC-CCC-D) and (b) L ¯ 0 , V ¯ 0 , T L 0 , T V 0 , P L 0 , P V 0 (GLMC-PC-D).
Figure 1. Representations of countercurrent and parallel GLMC modules as cascades of M elements (streams are numbered by the origin element). GLMC battery feed data: (a) L ¯ 0 , V ¯ M + 1 , T L 0 , T V M + 1 , P L 0 , P V M + 1 (GLMC-CCC-D) and (b) L ¯ 0 , V ¯ 0 , T L 0 , T V 0 , P L 0 , P V 0 (GLMC-PC-D).
Processes 12 01667 g001
Figure 2. HFM bundle as equilateral triangular lattice (edge pHF): triangle-free area, SFREE, for shell-side liquid flow.
Figure 2. HFM bundle as equilateral triangular lattice (edge pHF): triangle-free area, SFREE, for shell-side liquid flow.
Processes 12 01667 g002
Figure 3. Algorithm flowcharts for solving: (a) a countercurrent-contact GLMC (GLMC-CCC-D) and (b) parallel-contact GLMC (GLMC-PC-D).
Figure 3. Algorithm flowcharts for solving: (a) a countercurrent-contact GLMC (GLMC-CCC-D) and (b) parallel-contact GLMC (GLMC-PC-D).
Processes 12 01667 g003
Figure 4. Block diagram of GLMC-based industrial-scale landfill-gas-to-biomethane process.
Figure 4. Block diagram of GLMC-based industrial-scale landfill-gas-to-biomethane process.
Processes 12 01667 g004
Figure 5. Landfill-gas compression.
Figure 5. Landfill-gas compression.
Processes 12 01667 g005
Figure 6. Landfill-gas CO2/H2S removal via countercurrent pressurized-water GLMC.
Figure 6. Landfill-gas CO2/H2S removal via countercurrent pressurized-water GLMC.
Processes 12 01667 g006
Figure 7. CO2-to-EOR compression.
Figure 7. CO2-to-EOR compression.
Processes 12 01667 g007
Figure 8. Landfill-gas siloxane removal via DEPG absorption.
Figure 8. Landfill-gas siloxane removal via DEPG absorption.
Processes 12 01667 g008
Figure 9. GLMC-PC-D axial profiles: (a) f ^ CO 2 V , f ^ CO 2 L , f ^ CH 4 V , f ^ CH 4 L ; (b) V (%mol) CO2/H2S; (c) f ^ H 2 S V , f ^ H 2 S L ; (d) V ppm-mol H2S; (e) f ^ H 2 O V , f ^ H 2 O L ; (f) V (%mol) H2O; (g) %Recovery, %CH4 Loss; (h) CO2/CH4 Selectivity.
Figure 9. GLMC-PC-D axial profiles: (a) f ^ CO 2 V , f ^ CO 2 L , f ^ CH 4 V , f ^ CH 4 L ; (b) V (%mol) CO2/H2S; (c) f ^ H 2 S V , f ^ H 2 S L ; (d) V ppm-mol H2S; (e) f ^ H 2 O V , f ^ H 2 O L ; (f) V (%mol) H2O; (g) %Recovery, %CH4 Loss; (h) CO2/CH4 Selectivity.
Processes 12 01667 g009
Figure 10. GLMC-PC-D axial profiles: (a) temperatures; (b) pressures.
Figure 10. GLMC-PC-D axial profiles: (a) temperatures; (b) pressures.
Processes 12 01667 g010
Table 1. Landfill-gas silicon compounds with typical contents [18,20].
Table 1. Landfill-gas silicon compounds with typical contents [18,20].
NameIDFormulaMolar Mass (g/mol)Content (mg/Nm3)
Hexamethyl-disiloxaneL2C6H18OSi2162.386.07
Octamethyl-trisiloxaneL3C8H24O2Si3236.53-
Decamethyl-tetrasiloxaneL4C10H30O3Si4310.690.04
Dodecamethyl-pentasiloxaneL5C12H36O4Si5384.84-
Tetradecamethyl-hexasiloxaneL6C14H42O5Si6458.990.01
Hexamethyl-cyclotrisiloxaneD3C6H18O3Si3222.460.49
Octamethyl-cyclotetrasiloxaneD4C8H24O4Si4296.6212.53
Decamethyl-cyclopentasiloxaneD5C10H30O5Si5370.774.73
Dodecamethyl-cyclohexasiloxaneD6C12H36O6Si6444.930.33
Trimethyl-silanolTMSC3H10OSi90.20-
Total (ppm-mol)2.14
Table 2. Landfill-gas decarbonation/desulfurization siloxane-removal simulation/design assumptions.
Table 2. Landfill-gas decarbonation/desulfurization siloxane-removal simulation/design assumptions.
TopicDescription
Thermodynamic ModelingLandfill-Gas Compression, CO2/H2S Separation, Siloxanes Separation: HYSYS Acid-Gas Physical-Solvents Package; CO2-to-EOR: PR-EOS;
Cooling-Water(CW)/Chilled-Water(ChW)/LPS,MPS: HYSYS ASME-Steam-Table;
Landfill-Gas0.5 MMNm3/d; T = 30 °C; P = 1 bar; Mol = 27.73 g/mol; CH4 = 55.7 %mol; CO2 = 40 %mol;
H2OSaturation = 4.28 %mol; H2S = 150 ppm-mol; Siloxanes: Table 1.
BiomethaneCH4 ≥ 85 %mol; CO2 ≤ 3 %mol; H2S ≤ 10 mg/Nm3; Siloxanes ≤ 0.03 mg/Nm3
[19,85,86]
GLMC Module
[47]
HFM: Polyphenylene-Oxide (Parker P-240);  d i = 370   μ m ; d o = 520   μ m ;
HFM-Side:Landfill-Gas; Shell-Side:Water;  D = 0.36 m; Packing-Ratio:  φ = 0.5;
N H F = 2.39 × 10 5   f i b e r s ; GLMC-Absorber:  A G L M C = 663.65   m 2 / m o d u l e ; Z M = 2   m ;
GLMC-Stripper:  A G L M C = 1991.85   m 2 / m o d u l e ; Z M = 6   m ;
GLMC Modeling U I G L M C C C C D = U I G L M C P C D = 0.09   W m 2 K 1 ;  U E = 0 ;
Π C O 2 = Π H 2 S = Π H 2 O = 6.5756 × 10 4   mol/(s.bar.m2); Π CH 4 = 3.868 × 10 5   mol/(s.bar.m2);
Π S i l o x a n e s = 0 ;  Capture-Ratio = 443.41 kgH2O/kgCO2; {TS} = {CO2, H2S, CH4, H2O};
GLMC-CCC-D: Countercurrent-Contact Distributed-Model (Section 2.1);
GLMC-PC-D: Parallel-Contact Distributed-Model (Section 2.1).
High-Pressure CO2/H2S Reboilered
Stripper
Feed[H2O/CO2/H2S] = 7,275,950 kg/h;  P F e e d = 30.14   b a r ;   T F e e d = 223.2   ° C ; S t a g e s T h e o r e t i c a l = 10 ;
Feed-Stage = 5;  P C o n d e n s e r = 30   b a r ; T C o n d e n s e r = 40   ° C ;
P R e b o i l e r = 30.2   b a r ; T R e b o i l e r = 233.8   ° C ;  Condenser: Total-Reflux;
Reboiler: Kettle (MPS); Reflux-RatioTop = 721.6.
DEPG
Absorber
Solvent: 35.06 kmol/h; DEPG = 98.4 %w/w; H2O = 1.6 %w/w; P = 6.9   b a r T = 15   ° C ;
P V i n  = 6.995 bar;  T V i n  = 17.29 °C; StagesTheoretical = 20.
DEPG
Reboilerd Stripper
Feed: 36.85 kmol/h; P F e e d  = 1.17 bar;  T F e e d  = 162.7 °C; StagesTheoretical = 10; Feed-Stage = 5;  P C o n d e n s e r  = 1.1 bar;  T C o n d e n s e r  = 88.72 °C;  P R e b o i l e r  = 1.2 bar;  T R e b o i l e r  = 175 °C;
Condenser: Total-Reflux; Reboiler: Kettle (LPS); Reflux-RatioTop = 100.
Saturated-Steam
[87]
Low-Pressure-Steam (LPS): P = 14.3 bar, T = 196 °C;
Medium-Pressure-Steam (MPS): P = 42.5 bar, T = 254 °C.
CompressorsAdiabatic-Efficiency = 75%; Compression-RatioStage = 3 (Landfill-Gas);
Compression-RatioStage = 2.25 (CO2-to-EOR).
PumpsAdiabatic-Efficiency = 75%.
IntercoolersTGas-Out = 40 °C; ΔPGas = 0.5 bar.
Exchangers Δ T A p p r o a c h = 10   ° C ; Δ P = 0.5   b a r .
Cooling-Water T C W i n = 30   ° C ;   T C W o u t = 45   ° C ;   P C W i n = 4   b a r ;   P C W o u t = 3.5   b a r .
Chilled-Water T C h W i n = 10   ° C ;   T C h W o u t = 15   ° C ;   P C h W i n = 4   b a r ;   P C h W o u t = 3.5   b a r .
Table 3. Inlet/outlet fugacities: GLMC-CCC-D (M = 5) and GLMC-PC-D (M = 100).
Table 3. Inlet/outlet fugacities: GLMC-CCC-D (M = 5) and GLMC-PC-D (M = 100).
StreamsFugacityCO2CH4H2SH2O
Inlets f ^ k V (bar)2.8123.989 1.048 × 10 3 0.074
f ^ k L (bar) 1.602 × 10 9 000.017
GLMC-CCC-D
Outlets
f ^ k V (bar)0.2036.669 3.318 × 10 5 0.019
f ^ k L (bar)1.1903.899 1.505 × 10 4 0.018
GLMC-PC-D
Outlets
f ^ k V (bar)0.9895.876 1.448 × 10 4 0.018
f ^ k L (bar)0.9775.120 1.403 × 10 4 0.017
Table 4. Landfill-gas decarbonation/desulfurization results: GLMC-CCC-D and GLMC-PC-D batteries.
Table 4. Landfill-gas decarbonation/desulfurization results: GLMC-CCC-D and GLMC-PC-D batteries.
Landfill-Gas InletWater
Inlet
GLMC-CCC-D
Landfill-Gas Outlet
GLMC-CCC-D Water OutletGLMC-PC-D
Landfill-Gas
Outlet
GLMC-PC-D
Water Outlet
P (bar)7.07.06.9956.2136.9966.211
T (°C)40.0015.0017.2915.3721.1115.32
MMNm3/d0.483961-0.261854-0.286783-
kg/h-7,259,307-7,275,950-7,273,439
H2O (%mol)1.11 *1000.2999.900.2899.91
CH4 (%mol)57.55 2.06 × 10 23 96.710.0185.140.02
CO2 (%mol)41.32 1.20 × 10 10 3.00.0914.590.07
H2S (ppm-mol)154.94 2.77 × 10 13 4.940.3421.530.32
D3 (ppb-mol) #50.97094.21086.020
D4 (ppb-mol) #977.5901806.7901649.730
D5 (ppb-mol) #1.1602.1501.960
D6 (ppb-mol) #12.46023.03021.030
L2 (ppb-mol) #865.0901598.8701459.890
L4 (ppb-mol) #2.8405.2404.790
L6 (ppb-mol) #0.5000.9300.850
Siloxanes (ppb-mol) #1910.6103531.2203224.270
Final Results: Gas-to-Solvent CO2/H2S %Recoveries and Gas-to-Solvent CH4 %Loss
GLMC Battery%Recovery CO2%Recovery H2S%Loss CH4
GLMC-PC-D79.0891.7712.33
GLMC-CCC-D96.0898.289.07
* Water saturated; # Siloxanes: non-transferable.
Table 5. Landfill-gas decarbonation/desulfurization balance and high-pressure stripper products.
Table 5. Landfill-gas decarbonation/desulfurization balance and high-pressure stripper products.
Inlet Landfill-GasOutlet Landfill-GasStripper Top GasExcess Water
H2O (kmol/h)10.031.431.287.32
CH4 (kmol/h)518.05471.0547.00 1.94 × 10 24
CO2 (kmol/h)372.0014.59357.41 1.11 × 10 11
H2S (kmol/h)0.13950.00240.1371 5.41 × 10 18
High-Pressure CO2/H2S Stripper—Water Regeneration
Stripper Top GasLean Water
P (bar)30.030.2
T (°C)40.0233.8
kmol/h405.82402,960.11
MMNm3/d0.218-
H2O (%mol)0.31100
CH4 (%mol)11.58 2.65 × 10 23
CO2 (%mol)88.07 1.52 × 10 10
H2S (ppm-mol)337.78 7.39 × 10 13
Table 6. Siloxane DEPG absorber and DEPG stripper.
Table 6. Siloxane DEPG absorber and DEPG stripper.
Biomethane OutletAbsorber Rich DEPGStripper Top GasLean DEPG
P (bar)6.96.9951.11.2
T (°C)15.8419.3588.72175
kmol/h485.28 (0.26 MMNm3/d)36.861.8035.06
DEPG (%mol) 3.72 × 10 6 75.98 1.74 × 10 18 79.87
H2O (%mol)0.3122.1261.0920.13
CH4 (%mol)96.971.2425.54 2.65 × 10 25
CO2 (%mol)2.960.6513.26 7.95 × 10 15
H2S (ppm-mol)4.36 (6.7 mg/Nm3)7.77159.44 5.33 × 10 14
D3 (ppm-mol) 1 × 10 19 1.2525.6 3 × 10 17
D4 (ppm-mol) 6 × 10 18 23.9490 8 × 10 15
D5 (ppm-mol)0.0014512.2 8 × 10 7 12.8
D6 (ppm-mol)0.0002557.25.0959.9
L2 (ppm-mol) 2 × 10 9 21.1434 9 × 10 19
L4 (ppm-mol)0.000010.19351.590.122
L6 (ppm-mol) 4 × 10 23 0.01230.253 1.5 × 10 17
Siloxanes (ppm-mol)0.0017 (0.029 mg/Nm3)11695772.8
Table 7. Siloxane separation balance, waste streams, and power/utilities consumption.
Table 7. Siloxane separation balance, waste streams, and power/utilities consumption.
Inlet Landfill-GasOutlet BiomethaneTop-Gas StripperDEPG Make-Up
DEPG (kmol/h)0 1.81 × 10 5 3.13 × 10 20 1.81 × 10 5
H2O (kmol/h)1.430.331.100
CH4 (kmol/h)471.05470.590.46-
CO2 (kmol/h)14.5914.350.24-
H2S (kmol/h)0.00240.00210.0003-
Siloxanes (kmol/h) 1.72 × 10 3 8.28 × 10 7 1.72 × 10 3 -
Process WastesPower and Utilities Consumption
Residual GasResidual Water Consumption
kmol/h0.761.04Power9.41 MW
DEPG (%mol)00Chilled Water16,461 t/h
H2O (%mol)7.4499.99Cooling Water225,507 t/h
CH4 (%mol)60.770.00124LPS4 t/h
CO2 (%mol)31.530.01MPS8604 t/h
H2S (ppm-mol)378.760.45
Siloxanes (ppm-mol)22007.71
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

da Cunha, G.P.; de Medeiros, J.L.; Araújo, O.d.Q.F. Novel Landfill-Gas-to-Biomethane Route Using a Gas–Liquid Membrane Contactor for Decarbonation/Desulfurization and Selexol Absorption for Siloxane Removal. Processes 2024, 12, 1667. https://doi.org/10.3390/pr12081667

AMA Style

da Cunha GP, de Medeiros JL, Araújo OdQF. Novel Landfill-Gas-to-Biomethane Route Using a Gas–Liquid Membrane Contactor for Decarbonation/Desulfurization and Selexol Absorption for Siloxane Removal. Processes. 2024; 12(8):1667. https://doi.org/10.3390/pr12081667

Chicago/Turabian Style

da Cunha, Guilherme Pereira, José Luiz de Medeiros, and Ofélia de Queiroz F. Araújo. 2024. "Novel Landfill-Gas-to-Biomethane Route Using a Gas–Liquid Membrane Contactor for Decarbonation/Desulfurization and Selexol Absorption for Siloxane Removal" Processes 12, no. 8: 1667. https://doi.org/10.3390/pr12081667

APA Style

da Cunha, G. P., de Medeiros, J. L., & Araújo, O. d. Q. F. (2024). Novel Landfill-Gas-to-Biomethane Route Using a Gas–Liquid Membrane Contactor for Decarbonation/Desulfurization and Selexol Absorption for Siloxane Removal. Processes, 12(8), 1667. https://doi.org/10.3390/pr12081667

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop