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Multi-scale Material Screening and Process Optimization for Natural Gas Purification
Mario R. Eden, John D. Siirola and Gavin P. Towler (Editors) Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design – FOCAPD 2014 July 13-17, 2014, Cle Elum, Washington, USA © 2014 Elsevier B.V. All rights reserved.
Multi-scale Material Screening and Process Optimization for Natural Gas Purification Eric L. First, M. M. Faruque Hasan, Christodoulos A. Floudas* Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA [email protected]
Abstract Vast reserves of natural gas are uneconomical to develop with current technologies due to high CO2 content that must be removed to enhance energy content and satisfy pipeline specifications. Other methane sources include coalbed methane, shale gas, enhanced oil recovery (EOR) gas, biogas, and landfill gas. Pressure swing adsorption (PSA) is a promising technology to separate gas mixtures, but identifying the best sorbent, process topology, and operating conditions are important challenges in designing a cost-effective process. We propose a multi-scale computational framework to simultaneously address these issues using a hierarchical screening approach to filter a database of materials and rigorous mathematical process optimization to minimize the total cost of separation. The methodology has been used to discover 10 previously unconsidered sorbents, such as zeolite WEI, which can be used in an optimized adsorption process to purify natural gas with 5 % CO 2 at a cost of 0.15 $/MMBTU and natural gas with 50 % CO2 at a cost of 1.44 $/MMBTU. Other top zeolites with similar separation costs include zeolites AEN and AHT. We anticipate that this approach is suitable for the discovery of novel materials for other molecular separations of industrial importance. Keywords: material screening, process optimization, pressure swing adsorption
1. Introduction Natural gas is an attractive energy source for the U.S. due to its lower emissions and domestic availability. However, 10 % of natural gas reserves in the U.S. have CO2 concentrations in excess of pipeline specifications, typically less than 3 % by volume (Baker and Lokhandwala, 2008). Other methane sources contain significant contamination, such as landfill gas, which is an approximately 50–50 mixture of CH4 and CO2, as well as coalbed methane, shale gas, enhanced oil recovery (EOR) gas, and biogas. In purifying these sources, it is important to minimize methane losses for both economic and environmental considerations. Additionally, it is desirable to capture and compress the CO2 that is removed to limit releases to the atmosphere. Pressure swing adsorption (PSA), which involves passing the CH 4–CO2 mixture through a column packed with a microporous sorbent that selectively adheres CO 2, has been proposed as a suitable technology for methane purification (see Tagliabue et al., 2009 for a review). There are thousands of choices for the sorbent, such as zeolites and metal–organic frameworks, and an efficient computational framework is therefore required to screen for the most promising candidates.
514
E.L. First et al.
In this contribution, we present an in silico framework that combines material selection with process optimization to identify the most cost-effective zeolites for CH4/CO2 separation. Several material metrics are used to filter a database of zeolites to a short list of candidates. The PSA process using each of these zeolites is modeled using a detailed system of nonlinear algebraic and partial differential equations. The process topology and conditions are optimized for each zeolite to minimize the total cost of separation, which serves as the final ranking criterion.
2. Zeolite database screening We have developed a hierarchical screening procedure to filter the ZEOMICS database of 199 zeolite structures and pore characterizations (First et al., 2011) to a short list of candidate sorbents for process optimization. The method begins with the calculation of three geometric-level metrics: shape selectivity, size selectivity, and pore selectivity. Shape selectivity describes the difference in energetic cost of transport through a zeolite’s pores for two molecules due to strain and distortion caused by shape and size. Gounaris et al. (2006a, 2006b, 2009) introduced a computational framework for quantifying shape selectivity in zeolites, which was extended by First et al. (2013) to three-dimensional pore networks. Shape selectivity is calculated as the absolute difference in Boltzmann factors of the minimum pathway energies of CH 4 and CO2. While shape selectivity focuses on the most dominant pathway through a zeolite, the metric of size selectivity considers the entire distribution of pore sizes. Hasan et al. (2013) first introduced this metric, which is calculated as the relative difference in accessible pore volume between CH4 and CO2. The metric uses a hard sphere approximation for each molecule to determine accessibility. Table 1. Candidate zeolites for process optimization. Zeolite
Shape selectivity
Size selectivity
Pore selectivity
Adsorption selectivity
ABW AEN AHT ANA APC BIK DFT GIS GOO JBW LOV LTJ MER MON NSI PAU PON RSN RWR SIV WEI YUG
0.41 0.51 0.46 0.49 0.49 0.48 0.57 0.56 0.51 0.38 0.58 0.37 0.73 0.49 0.48 1.11 0.45 0.58 0.51 0.60 0.39 0.51
0.41 0.37 0.34 0.30 0.38 0.36 0.43 0.39 0.36 0.38 0.34 0.37 0.48 0.35 0.34 0.47 0.39 0.34 0.38 0.44 0.39 0.37
0 0 0.40 0.44 0 0 0 0 0 0 0 0 0 0 0.06 0 0 0 0 0 0 0
0 1 0 0 1 1 0.27 1 1 1 1 1 0.51 1 1 0.33 1 1 1 0.54 1 1
Multi-scale Material Screening and Process Optimization for Natural Gas Purification
515
Pore selectivity is a new metric proposed for natural gas purification to relax the hard sphere approximation of size selectivity. It combines the energetic calculations of shape selectivity with the accessible pore volume calculations of size selectivity. Pore selectivity is calculated as the relative difference in energetically-accessible pore volume as described in First et al. (2014). Each of these metrics is calculated as a number between 0 and 1, with 0 indicating low selectivity and 1 indicating high selectivity. Minimum cut-off values of 0, 0.1, and 0.1 are introduced for shape, size, and pore selectivity, respectively, to filter the full database of zeolites. We find that 86 zeolites satisfy these criteria, and each of these is next evaluated with the atomistic-level metric of adsorption selectivity. Adsorption selectivity is determined by performing grand canonical Monte Carlo (GCMC) simulations to calculate the Henry constants for CH4 and CO2 in each zeolite. The details of the GCMC simulations are reported elsewhere (Hasan et al., 2013, First et al., 2014). The ratio of the Henry constant of CO2 to the Henry constant of CH4 defines the adsorption selectivity. This is an appropriate metric because of the low pressure operation of this PSA process. We find 22 zeolites, listed in Table 1, with an adsorption selectivity of at least 10 to serve as candidates for process optimization.
3. Process optimization of pressure swing adsorption, PSA The PSA process that we consider for natural gas purification is illustrated in Figure 1a. The feed may pass through an expander or compressor to bring it to the desired adsorption pressure. Then one or more columns packed with a zeolite sorbent are used for adsorption. Clean CH 4 is collected from the product-end of the column and compressed to 60 bar for pipeline transport. A desorption vacuum pump is used to evacuate CO2 from the feed-end of the column, which is compressed to 150 bar using a 6-stage compression train for utilization or sequestration. The process is operated using a 3-step PSA cycle (Figure 1b) consisting of (i) column pressurization, (ii) CO2 adsorption and CH4 recovery, and (iii) counter-current CO2 desorption. During pressurization, the column fills from the feed-end with the CH4–CO2 mixture until the adsorption pressure, Pads, is reached. Then, during the adsorption step, the product-end of the column opens and clean CH4 exits the column while CO2 is selective adsorbed by the zeolite sorbent. Finally, during desorption, the product-end of the column is again closed, and CO2 is evacuated from the feed-end of the column at the desorption pressure, Pdes. A typical pressure profile during a cycle is provided in Figure 1b. Multiple adsorption columns can be used with staggered PSA cycles to maximize utilization of the vacuum pumps and compressors. The PSA process is modeled using a detailed system of nonlinear algebraic and partial differential equations, which is presented in First et al. (2014). These equations describe mass and energy balances, flow through porous media, temperature and pressure effects, and heat transfer resistance across the column wall (Hasan et al., 2012, 2013). A candidate zeolite is represented in the model by parameters describing CH 4 and CO2 adsorption isotherms and heats of adsorption, calculated using GCMC simulations.
516
E.L. First et al.
Pressure
Desorption
Product compressor
CH4 Adsorption
b)
Pressurization
CH4 at 60 bar
a)
CH4/CO2
L
CO2
Pads
Pdes Press. tpr
Adsorption tads Full cycle
Desorption tdes
Time
CO2 at 150 bar
Feed expander Desorption vacuum pump
CH4–CO2
6-stage compression train
(60 bar) Feed compressor
Figure 1. Process flow diagram (a) of the PSA process for CH4/CO2 separation and typical pressure profile (b) of the 3-step cycle. Adapted from First et al., 2014.
The model is optimized to identify the column length (L), adsorption (Pads) and desorption (Pdes) pressures, and adsorption (tads) and desorption (tdes) step durations. Bounds for each of these variables, which were determined through refinement, are provided in Table 2. The pressurization time is fixed to 20 s (Hasan et al., 2012), and the column diameter and number of columns are determined using analytical expressions described in Hasan et al. (2013). Constraints are introduced to specify a minimum product methane purity of 97 %, to meet pipeline specifications, and minimum methane recovery of 95 %, to limit product losses. Such stringent constraints have proven difficult to achieve in previous studies for pressure swing adsorption processes for natural gas purification. To efficiently optimize the large, complex process model, an efficient Kriging-based grey-box constrained optimization approach is used (Hasan et al., 2012, 2013). The original model is sampled at several sample conditions to construct a Kriging-based surrogate model from the input–output data. The surrogate model is optimized with the purity and recovery constraints to determine additional sample conditions needed to reach convergence. Details of the surrogate model formulation and optimization methodology are provided in First et al. (2014). Table 2. Bounds on the decision variables in the adsorption process model. Variable
Lower bound
Upper bound
L (m) Pads (bar) Pdes (bar) tads (sec) tdes (sec)
1 1 0.01 40 20
1 5 0.1 100 150
Multi-scale Material Screening and Process Optimization for Natural Gas Purification
517
4. Cost-effective zeolites for natural gas purification We optimize the adsorption process using each of the 22 candidate zeolite sorbents for feed conditions representative of CH4–CO2 methane sources, including natural gas, coalbed methane, shale gas, EOR gas, biogas, and landfill gas sources. The CO2 composition of the feed ranges between 5 % and 50 %, the feed pressure is 60 bar, and the feed flow rate is 0.1 kmol/s. We find that 8 zeolites satisfy the feasibility constraints of 97 % purity and 95 % recovery across all feed compositions, while an additional 2 zeolites are feasible only for 5 % CO2 in the feed. The optimized cost for each of these zeolites is provided in Table 3 for each CO2 feed composition studied. The top-performing sorbent across all feed conditions is zeolite WEI, though many of the feasible zeolites have similar process costs, and other top contenders include zeolites AHT and AEN. For 5 % CO2 in the feed, the total cost for separation, which includes product compression to 60 bar and CO2 capture and compression to 150 bar, is 0.15 $/MMBTU using zeolite WEI. The cost rises to 1.44 $/MMBTU using WEI with 50 % CO2 in the feed. These results compare favorably with the Henry Hub price of natural gas, which the U.S. Energy Information Administration (2014) reports to be 4.24 $/MMBTU as of December 2013, the most recent data available. An analysis of the optimal process conditions reveals that as the CO2 concentration in the feed increases, the average adsorption time decreases and the average desorption time increases. The average adsorption and desorption pressures decrease as the CO 2 concentration in the feed increases to counteract the increase in CO2 partial pressure, which affects the equilibrium adsorption and desorption levels. A complete discussion of the process conditions and cost breakdown is provided in First et al. (2014).
5. Conclusions Using a combined material screening and process optimization methodology, we have discovered several new zeolites for purifying natural gas. The most economical sorbent, zeolite WEI, is attractive for use in a PSA process to separate CO 2 from methane sources ranging from 5 % to 50 % CO2. The process achieves 97 % purity of the CH 4 product, which satisfies U.S. natural gas pipeline specifications, and 95 % recovery of the CH4 in the feed, which limits losses to the atmosphere. The costs are inclusive of compression of the product methane to 60 bar for pipeline transport and capture and compression of the CO2 to 150 bar for subsequent utilization or sequestration. Table 3. Optimized cost ($/MMBTU) for feasible zeolites at each feed CO2 composition. Zeolite
5 % CO2
10 % CO2
20 % CO2
30 % CO2
40 % CO2
50 % CO2
ABW AEN AHT APC BIK JBW LTJ MON NSI WEI
0.15 0.16 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.15
0.27 0.27 0.27 0.27 0.27 0.28 – 0.27 – 0.26
0.52 0.49 0.48 0.5 0.5 0.5 – 0.5 – 0.48
0.81 0.76 0.77 0.76 0.8 0.77 – 0.77 – 0.72
1.2 1.1 1.08 1.14 1.14 1.16 – 1.12 – 1.04
1.74 1.57 1.64 1.65 1.64 1.62 – 1.59 – 1.44
518
E.L. First et al.
The multi-scale computational framework, which has also been applied by Hasan et al. (2013) to CO2/N2 separation for CO2 capture from postcombustion power plant flue gases, has vast possibilities for applications to other separations of industrial importance. The material screening metrics can easily accommodate other molecules of interest, and the process model can be customized to various PSA cycles and process configurations as well as substituted with entirely different adsorption processes.
Acknowledgements This work was partially supported by the National Science Foundation under awards EFRI-0937706 and CBET-1263165. E.L.F. is also thankful for his National Defense Science and Engineering Graduate (NDSEG) fellowship. A portion of the computation was performed at the TIGRESS high performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology.
References Baker, R. W., Lokhandwala, K. (2008). Natural gas processing with membranes: An overview. Industrial & Engineering Chemistry Research, 47(7):2109–2121. First, E. L., Gounaris, C. E., Floudas, C. A. (2013). Predictive framework for shape-selective separations in three-dimensional zeolites and metal–organic frameworks. Langmuir, 29(18):5599–5608. First, E. L., Gounaris, C. E., Wei, J., Floudas, C. A. (2011). Computational characterization of zeolite porous networks: an automated approach. Physical Chemistry Chemical Physics, 13(38):17339–17358. First, E. L., Hasan, M. M. F., Floudas, C. A. (2014). Discovery of novel zeolites for natural gas purification through combined material screening and process optimization. AIChE Journal, accepted for publication. Gounaris, C. E., Floudas, C. A., Wei, J. (2006a). Rational design of shape selective separation and catalysis–I: Concepts and analysis. Chemical Engineering Science, 61(24):7933–7948. Gounaris, C. E., Wei, J., Floudas, C. A. (2006b). Rational design of shape selective separation and catalysis–II: Mathematical model and computational studies. Chemical Engineering Science, 61(24):7949–7962. Gounaris, C. E., Wei, J., Floudas, C. A., Ranjan, R., Tsapatsis, M. (2009). Rational design of shape selective separations and catalysis: Lattice relaxation and effective aperture size. AIChE Journal, 56(3):611–632. Hasan, M. M. F., Baliban, R. C., Elia, J. A., Floudas, C. A. (2012). Modeling, simulation and optimization of CO2 capture for variable feed CO2 concentration and feed flow. 2. Pressure swing adsorption and vacuum swing adsorption processes. Industrial & Engineering Chemistry Research, 51(48):15665–15682. Hasan, M. M. F., First, E. L., Floudas, C. A. (2013). Cost-effective CO2 capture based on in silico screening of zeolites and process optimization. Physical Chemistry Chemical Physics, 15(40):17601–17618. Tagliabue, M., Farrusseng, D., Valencia, S., Aguado, S., Ravon, U., Rizzo, C., Corma, A., Mirodatos, C. (2009). Natural gas treating by selective adsorption: Material science and chemical engineering interplay. Chemical Engineering Journal, 155(3):553–566. U.S. Energy Information Administration (2014). Natural gas spot and futures prices. http://www.eia.gov/dnav/ng/ng_pri_fut_s1_m.htm.
Multi-scale Material Screening and Process Optimization for Natural Gas Purification Eric L. First, M. M. Faruque Hasan, Christodoulos A. Floudas* Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA [email protected]
Abstract Vast reserves of natural gas are uneconomical to develop with current technologies due to high CO2 content that must be removed to enhance energy content and satisfy pipeline specifications. Other methane sources include coalbed methane, shale gas, enhanced oil recovery (EOR) gas, biogas, and landfill gas. Pressure swing adsorption (PSA) is a promising technology to separate gas mixtures, but identifying the best sorbent, process topology, and operating conditions are important challenges in designing a cost-effective process. We propose a multi-scale computational framework to simultaneously address these issues using a hierarchical screening approach to filter a database of materials and rigorous mathematical process optimization to minimize the total cost of separation. The methodology has been used to discover 10 previously unconsidered sorbents, such as zeolite WEI, which can be used in an optimized adsorption process to purify natural gas with 5 % CO 2 at a cost of 0.15 $/MMBTU and natural gas with 50 % CO2 at a cost of 1.44 $/MMBTU. Other top zeolites with similar separation costs include zeolites AEN and AHT. We anticipate that this approach is suitable for the discovery of novel materials for other molecular separations of industrial importance. Keywords: material screening, process optimization, pressure swing adsorption
1. Introduction Natural gas is an attractive energy source for the U.S. due to its lower emissions and domestic availability. However, 10 % of natural gas reserves in the U.S. have CO2 concentrations in excess of pipeline specifications, typically less than 3 % by volume (Baker and Lokhandwala, 2008). Other methane sources contain significant contamination, such as landfill gas, which is an approximately 50–50 mixture of CH4 and CO2, as well as coalbed methane, shale gas, enhanced oil recovery (EOR) gas, and biogas. In purifying these sources, it is important to minimize methane losses for both economic and environmental considerations. Additionally, it is desirable to capture and compress the CO2 that is removed to limit releases to the atmosphere. Pressure swing adsorption (PSA), which involves passing the CH 4–CO2 mixture through a column packed with a microporous sorbent that selectively adheres CO 2, has been proposed as a suitable technology for methane purification (see Tagliabue et al., 2009 for a review). There are thousands of choices for the sorbent, such as zeolites and metal–organic frameworks, and an efficient computational framework is therefore required to screen for the most promising candidates.
514
E.L. First et al.
In this contribution, we present an in silico framework that combines material selection with process optimization to identify the most cost-effective zeolites for CH4/CO2 separation. Several material metrics are used to filter a database of zeolites to a short list of candidates. The PSA process using each of these zeolites is modeled using a detailed system of nonlinear algebraic and partial differential equations. The process topology and conditions are optimized for each zeolite to minimize the total cost of separation, which serves as the final ranking criterion.
2. Zeolite database screening We have developed a hierarchical screening procedure to filter the ZEOMICS database of 199 zeolite structures and pore characterizations (First et al., 2011) to a short list of candidate sorbents for process optimization. The method begins with the calculation of three geometric-level metrics: shape selectivity, size selectivity, and pore selectivity. Shape selectivity describes the difference in energetic cost of transport through a zeolite’s pores for two molecules due to strain and distortion caused by shape and size. Gounaris et al. (2006a, 2006b, 2009) introduced a computational framework for quantifying shape selectivity in zeolites, which was extended by First et al. (2013) to three-dimensional pore networks. Shape selectivity is calculated as the absolute difference in Boltzmann factors of the minimum pathway energies of CH 4 and CO2. While shape selectivity focuses on the most dominant pathway through a zeolite, the metric of size selectivity considers the entire distribution of pore sizes. Hasan et al. (2013) first introduced this metric, which is calculated as the relative difference in accessible pore volume between CH4 and CO2. The metric uses a hard sphere approximation for each molecule to determine accessibility. Table 1. Candidate zeolites for process optimization. Zeolite
Shape selectivity
Size selectivity
Pore selectivity
Adsorption selectivity
ABW AEN AHT ANA APC BIK DFT GIS GOO JBW LOV LTJ MER MON NSI PAU PON RSN RWR SIV WEI YUG
0.41 0.51 0.46 0.49 0.49 0.48 0.57 0.56 0.51 0.38 0.58 0.37 0.73 0.49 0.48 1.11 0.45 0.58 0.51 0.60 0.39 0.51
0.41 0.37 0.34 0.30 0.38 0.36 0.43 0.39 0.36 0.38 0.34 0.37 0.48 0.35 0.34 0.47 0.39 0.34 0.38 0.44 0.39 0.37
0 0 0.40 0.44 0 0 0 0 0 0 0 0 0 0 0.06 0 0 0 0 0 0 0
0 1 0 0 1 1 0.27 1 1 1 1 1 0.51 1 1 0.33 1 1 1 0.54 1 1
Multi-scale Material Screening and Process Optimization for Natural Gas Purification
515
Pore selectivity is a new metric proposed for natural gas purification to relax the hard sphere approximation of size selectivity. It combines the energetic calculations of shape selectivity with the accessible pore volume calculations of size selectivity. Pore selectivity is calculated as the relative difference in energetically-accessible pore volume as described in First et al. (2014). Each of these metrics is calculated as a number between 0 and 1, with 0 indicating low selectivity and 1 indicating high selectivity. Minimum cut-off values of 0, 0.1, and 0.1 are introduced for shape, size, and pore selectivity, respectively, to filter the full database of zeolites. We find that 86 zeolites satisfy these criteria, and each of these is next evaluated with the atomistic-level metric of adsorption selectivity. Adsorption selectivity is determined by performing grand canonical Monte Carlo (GCMC) simulations to calculate the Henry constants for CH4 and CO2 in each zeolite. The details of the GCMC simulations are reported elsewhere (Hasan et al., 2013, First et al., 2014). The ratio of the Henry constant of CO2 to the Henry constant of CH4 defines the adsorption selectivity. This is an appropriate metric because of the low pressure operation of this PSA process. We find 22 zeolites, listed in Table 1, with an adsorption selectivity of at least 10 to serve as candidates for process optimization.
3. Process optimization of pressure swing adsorption, PSA The PSA process that we consider for natural gas purification is illustrated in Figure 1a. The feed may pass through an expander or compressor to bring it to the desired adsorption pressure. Then one or more columns packed with a zeolite sorbent are used for adsorption. Clean CH 4 is collected from the product-end of the column and compressed to 60 bar for pipeline transport. A desorption vacuum pump is used to evacuate CO2 from the feed-end of the column, which is compressed to 150 bar using a 6-stage compression train for utilization or sequestration. The process is operated using a 3-step PSA cycle (Figure 1b) consisting of (i) column pressurization, (ii) CO2 adsorption and CH4 recovery, and (iii) counter-current CO2 desorption. During pressurization, the column fills from the feed-end with the CH4–CO2 mixture until the adsorption pressure, Pads, is reached. Then, during the adsorption step, the product-end of the column opens and clean CH4 exits the column while CO2 is selective adsorbed by the zeolite sorbent. Finally, during desorption, the product-end of the column is again closed, and CO2 is evacuated from the feed-end of the column at the desorption pressure, Pdes. A typical pressure profile during a cycle is provided in Figure 1b. Multiple adsorption columns can be used with staggered PSA cycles to maximize utilization of the vacuum pumps and compressors. The PSA process is modeled using a detailed system of nonlinear algebraic and partial differential equations, which is presented in First et al. (2014). These equations describe mass and energy balances, flow through porous media, temperature and pressure effects, and heat transfer resistance across the column wall (Hasan et al., 2012, 2013). A candidate zeolite is represented in the model by parameters describing CH 4 and CO2 adsorption isotherms and heats of adsorption, calculated using GCMC simulations.
516
E.L. First et al.
Pressure
Desorption
Product compressor
CH4 Adsorption
b)
Pressurization
CH4 at 60 bar
a)
CH4/CO2
L
CO2
Pads
Pdes Press. tpr
Adsorption tads Full cycle
Desorption tdes
Time
CO2 at 150 bar
Feed expander Desorption vacuum pump
CH4–CO2
6-stage compression train
(60 bar) Feed compressor
Figure 1. Process flow diagram (a) of the PSA process for CH4/CO2 separation and typical pressure profile (b) of the 3-step cycle. Adapted from First et al., 2014.
The model is optimized to identify the column length (L), adsorption (Pads) and desorption (Pdes) pressures, and adsorption (tads) and desorption (tdes) step durations. Bounds for each of these variables, which were determined through refinement, are provided in Table 2. The pressurization time is fixed to 20 s (Hasan et al., 2012), and the column diameter and number of columns are determined using analytical expressions described in Hasan et al. (2013). Constraints are introduced to specify a minimum product methane purity of 97 %, to meet pipeline specifications, and minimum methane recovery of 95 %, to limit product losses. Such stringent constraints have proven difficult to achieve in previous studies for pressure swing adsorption processes for natural gas purification. To efficiently optimize the large, complex process model, an efficient Kriging-based grey-box constrained optimization approach is used (Hasan et al., 2012, 2013). The original model is sampled at several sample conditions to construct a Kriging-based surrogate model from the input–output data. The surrogate model is optimized with the purity and recovery constraints to determine additional sample conditions needed to reach convergence. Details of the surrogate model formulation and optimization methodology are provided in First et al. (2014). Table 2. Bounds on the decision variables in the adsorption process model. Variable
Lower bound
Upper bound
L (m) Pads (bar) Pdes (bar) tads (sec) tdes (sec)
1 1 0.01 40 20
1 5 0.1 100 150
Multi-scale Material Screening and Process Optimization for Natural Gas Purification
517
4. Cost-effective zeolites for natural gas purification We optimize the adsorption process using each of the 22 candidate zeolite sorbents for feed conditions representative of CH4–CO2 methane sources, including natural gas, coalbed methane, shale gas, EOR gas, biogas, and landfill gas sources. The CO2 composition of the feed ranges between 5 % and 50 %, the feed pressure is 60 bar, and the feed flow rate is 0.1 kmol/s. We find that 8 zeolites satisfy the feasibility constraints of 97 % purity and 95 % recovery across all feed compositions, while an additional 2 zeolites are feasible only for 5 % CO2 in the feed. The optimized cost for each of these zeolites is provided in Table 3 for each CO2 feed composition studied. The top-performing sorbent across all feed conditions is zeolite WEI, though many of the feasible zeolites have similar process costs, and other top contenders include zeolites AHT and AEN. For 5 % CO2 in the feed, the total cost for separation, which includes product compression to 60 bar and CO2 capture and compression to 150 bar, is 0.15 $/MMBTU using zeolite WEI. The cost rises to 1.44 $/MMBTU using WEI with 50 % CO2 in the feed. These results compare favorably with the Henry Hub price of natural gas, which the U.S. Energy Information Administration (2014) reports to be 4.24 $/MMBTU as of December 2013, the most recent data available. An analysis of the optimal process conditions reveals that as the CO2 concentration in the feed increases, the average adsorption time decreases and the average desorption time increases. The average adsorption and desorption pressures decrease as the CO 2 concentration in the feed increases to counteract the increase in CO2 partial pressure, which affects the equilibrium adsorption and desorption levels. A complete discussion of the process conditions and cost breakdown is provided in First et al. (2014).
5. Conclusions Using a combined material screening and process optimization methodology, we have discovered several new zeolites for purifying natural gas. The most economical sorbent, zeolite WEI, is attractive for use in a PSA process to separate CO 2 from methane sources ranging from 5 % to 50 % CO2. The process achieves 97 % purity of the CH 4 product, which satisfies U.S. natural gas pipeline specifications, and 95 % recovery of the CH4 in the feed, which limits losses to the atmosphere. The costs are inclusive of compression of the product methane to 60 bar for pipeline transport and capture and compression of the CO2 to 150 bar for subsequent utilization or sequestration. Table 3. Optimized cost ($/MMBTU) for feasible zeolites at each feed CO2 composition. Zeolite
5 % CO2
10 % CO2
20 % CO2
30 % CO2
40 % CO2
50 % CO2
ABW AEN AHT APC BIK JBW LTJ MON NSI WEI
0.15 0.16 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.15
0.27 0.27 0.27 0.27 0.27 0.28 – 0.27 – 0.26
0.52 0.49 0.48 0.5 0.5 0.5 – 0.5 – 0.48
0.81 0.76 0.77 0.76 0.8 0.77 – 0.77 – 0.72
1.2 1.1 1.08 1.14 1.14 1.16 – 1.12 – 1.04
1.74 1.57 1.64 1.65 1.64 1.62 – 1.59 – 1.44
518
E.L. First et al.
The multi-scale computational framework, which has also been applied by Hasan et al. (2013) to CO2/N2 separation for CO2 capture from postcombustion power plant flue gases, has vast possibilities for applications to other separations of industrial importance. The material screening metrics can easily accommodate other molecules of interest, and the process model can be customized to various PSA cycles and process configurations as well as substituted with entirely different adsorption processes.
Acknowledgements This work was partially supported by the National Science Foundation under awards EFRI-0937706 and CBET-1263165. E.L.F. is also thankful for his National Defense Science and Engineering Graduate (NDSEG) fellowship. A portion of the computation was performed at the TIGRESS high performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology.
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