US20160121258A1

US20160121258A1 - Methods of separating molecules

Info

Publication number: US20160121258A1
Application number: US14/766,401
Authority: US
Inventors: Eric L. FIRST; M.M. Faruque HASAN; Christodoulos A. Floudas
Original assignee: Princeton University
Current assignee: Princeton University
Priority date: 2013-02-06
Filing date: 2014-02-05
Publication date: 2016-05-05
Also published as: WO2014124021A1; WO2014124035A1; US20150360166A1; US10239012B2

Abstract

Disclosed herein are new methods, machines, processes, and systems for separating molecules by determining better materials and process optimization conditions. As a result of these advances, this disclosure provides improved carbon dioxide capture, better flue gas treatments, and more efficient methods of purifying gases have been developed. Optimal sorbents can be obtained by using a computational screening method that selects microporous structures (e.g. zeolites and metal-organic frameworks) from a database of materials with the greatest potential for cost-effective separations. The disclosed methods are the first to consider both the size and shape of the adsorbent material. This is also the first disclosure to consider the process application and cost when selecting which material to use.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application Nos. 61/926,561 (filed Jan. 13, 2014), 61/873,940 (Filed Sep. 5, 2013), 61/765,284 (filed Feb. 15, 2013), 61/761,436 (filed Feb. 6, 2013), and 61/889,296 (filed Oct. 19, 2013), the contents of which are incorporated by reference in their entirety.

STATEMENT REGARDING UNITED STATES GOVERNMENT FUNDING

This invention was made with government support under Grant No. A0000994101 awarded by the University of Minnesota (University of Minnesota Prime from the National Science Foundation Prime Award No. EFRI-0937706); Grant No. CBET-1263165 awarded by the National Science Foundation; and Government support under FA9550-11-C-0028 awarded by the DoD, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 10 168a. The Government has certain rights in the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flow chart for an exemplary method of separating gases by optimizing a mathematical model of a separation process.
FIG. 2 shows a flow chart for an exemplary method of separating carbon dioxide gas from methane gas.
FIG. 3 shows a flow chart for an exemplary method of separating carbon dioxide gas from nitrogen gas.
FIG. 4 shows an exemplary gas separation process, applied to methane purification.
FIG. 5 shows an exemplary pressure swing adsorption process cycle configuration, applied to methane purification.
FIG. 6 shows an exemplary gas separation process, applied to capturing carbon dioxide from flue gas.
FIG. 7 shows an exemplary pressure swing adsorption process cycle configuration, applied to capturing carbon dioxide from flue gas.

BACKGROUND

Separating mixtures of two or more molecular entities into purified forms has challenged chemical engineers for centuries. The field is constantly seeking new approaches and better means for separating mixtures of molecules.
Every molecular separation involves the concentration of a target molecule or molecules from a mixture of molecules. Separation is the reverse of mixing, which requires a suitable medium or material for separation, a driving force or energy to achieve the specified separation, and a process to perform the specified separation. Among the decisions that one needs to make while designing an industrial separation, the following three are especially critical: (i) which material to select from the universe of potential materials, (ii) which process and which configuration of the process to use that would give the best separation, and (iii) which set or sets of operating conditions that the process should use to achieve the best separation.
As processes for separating molecules have evolved, some approaches have grown in complexity, requiring multiple steps and machines. Previous separation methods only considered each of the three above-listed strategies in isolation. By focusing only on one area at a time, the interplay between strategies has been ignored by the art. Such isolated studies have resulted in suboptimal selection of materials, process configurations, and operating conditions.
Selecting an appropriate material can be complicated because not all molecules show equal affinity to adhere, absorb, adsorb, desorb, or pass through a given material. Some molecules adsorb more strongly onto the material surface than others, while some molecules pass through a material more easily than others. Even the groups of materials which have high internal surface areas and pore volumes for adsorption-based separation, such as zeolites and metal organic frameworks (MOFs), have hundreds and thousands of members to select from. Furthermore, the selection of materials varies with separation. Accordingly, a material appropriate for separating CO2 from power plant flue gases may not be appropriate for separating CO2 from natural gas, or separating H2 from synthesis gas and so on.
While material selection is central to any molecular separation, it is equally important to select a process configuration and optimize its performance. Many different separation processes exist, which can be used to perform molecular separation. The most widely used technologies include distillation, absorption, adsorption and membrane-based processes. These technologies are commercially implemented for different gas separation applications. For instance, many amine-based chemical absorption, solid sorbent-based pressure swing adsorption (PSA), and polymeric material-based membrane processes are used in the chemical and power industries to remove acid gases from natural gas, flue gas, fuel gas and so on. Major sorbents for the adsorption-based separation include microporous/mesoporous silica or zeolites, activated carbonaceous materials and MOFs.
A separation process, either absorption, adsorption or membrane-based process, can be designed in different modes and can have different configurations. For instance, adsorption-based separation offers three operational modes, namely Pressure Swing Adsorption (PSA), Vacuum Swing Adsorption (VSA), and Temperature Swing Adsorption (TSA). One major difference between the PSA and VSA processes is the difference in their operating pressure levels. The highest operating pressure in a VSA process is atmospheric, while it can be more than atmospheric in a PSA process. The performance of these two processes can be significantly different for different feed compositions and flow rates, product specifications, and adsorbents.
Technologies focusing on adsorption-based processes do not consider material selection as an integral part of the investigation, though alternative materials have the potential to require less energy and cost, even for a fixed process type and configuration. In fact, very few technologies consider effectiveness of a material when used in a process. While adsorption selectivity, equilibrium saturation capacity and minimum parasitic energy are good screening metrics for identifying a sorbent, the common fallibility of these metrics is that they are all evaluated at equilibrium conditions. However, in practice, an adsorption-based process often operates distantly from equilibrium. The selection of a material depends on, among others, its selectivity and affinity toward the target molecule, sorption equilibrium and kinetics, regeneration and cost.
Transient breakthrough simulations of several highly adsorption-selective zeolites and MOFs are used to identify zeolites and MOFs which would exhibit large adsorption selectivities for a given separation target. However, large adsorption selectivity does not always guarantee the most cost-effective separation. In many studies, the equilibrium saturation capacities of the targeted molecules in different materials are used to select the so-called “best” material for adsorption. Some other technologies outline methods to calculate the minimum parasitic energy required to separate a molecule (e.g., CO2 from flue gases) with an intention to screen materials. When applied to an adsorption-based process, this metric is calculated by taking into account the energy consumption due to heating, the heat of desorption during regeneration, and associated operations. These technologies do not consider the performance of materials when used in actual process configurations.
In addition, the industrial scale deployment of adsorption-based systems would require both the material and process development. Therefore, identifying attainable process configurations with realistic and optimum operational conditions are crucial in evaluating a material's performance. Often it is the case that a material shows great promise with high adsorption capacity or very low theoretical parasitic energy or a high adsorption selectivity, but when put in a real process cannot even separate CO2 with the required purity and recovery. Among those materials, which are able to attain the required purity and recovery, some exhibit high energy penalty and process cost.
It is important to optimize the cost impact of a separation process. To realize the full potential of a process, it is crucial to optimize its performance. However, the rigorous optimization of a complex separation process is challenging. For instance, the adsorption-based processes such PSA and VSA are usually multi-step and adsorbent-packed distributed processes that undergo a transient state before reaching a cyclic steady state. PSA and VSA are cyclic processes for which the performance varies with the column size, cycle configuration, step durations and pressure levels in each step. Adsorption-based process models include nonlinear algebraic and partial differential equations (NAPDEs).
Contributions have been made for the optimization of PSA and VSA processes. For instance, a mixed-integer nonlinear optimization (MINLP) model with time averaged mass and energy balances has been used for the design of a PSA process for the minimum annualized cost. Some studies have proposed partial or full discretization of the NAPDEs describing a PSA model. The resulting large-scale nonlinear programming (NLP) model is usually solved using commercial solvers. The efficacy of such approach has been demonstrated for the air separation using rapid PSA and modified PSA processes. Sequential quadratic programming (SQP)-based approaches and direct optimization approaches have been also used to optimize PSA processes. However, solving a detailed NAPDE model for PSA/VSA optimization has remained as a challenge.

DETAILED DESCRIPTION

Advances in molecular separation have now been made. Disclosed herein are new methods for separating molecules by determining better materials and process optimization conditions. Thanks to the disclosed advances, better carbon dioxide capture and better flue gas treatments have been developed.
Advances in identifying sorbents for molecular separations have now been made. For example, better sorbents can be obtained by using a computational screening method that selects microporous structures (e.g. zeolites and metal-organic frameworks) from a database of materials with the greatest potential for cost-effective separations. The disclosed methods are the first to consider both the size and shape of the adsorbent material. This is also the first disclosure to consider the process application, process design, and process optimization when selecting which material to use.
Screening microporous structures by both pore size and shape has proven useful for identifying new microporous materials for separating molecules. The disclosed method considers both the material as well as the process, so it indicates both the feasibility and performance of a material and the optimal process parameters for performing the separation. The disclosed methods consider several metrics, including process cost, when selecting the best materials from large databases of possibilities through an efficient screening procedure.
The disclosed pore characterization methods for zeolites and metal-organic frameworks provide better information about the geometry and topology of the porous networks, including a three-dimensional visualization and quantitative data including portals, channels, cages, connectivity, pore size distribution, accessible volume, accessible surface area, largest cavity diameter, and pore limiting diameter. (First, E. L., Gounaris, C. E., Wei, J., and Floudas, C. A. Phys. Chem. Chem. Phys., 13:17339-17358, 2011; First, E. L., Floudas, C. A. Micropor. Mesopor. Mater., 165:32-39, 2013).
The shape-selective screening approach has applications for both separations as well as catalysis applications, where the selectivity data can be extended to reactants, products, and transition state structures. The disclosed methods are demonstrated for carbon capture, but the methodology is applicable to any separation application, and can be combined with other process models beyond pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA).
The above methods provide the first holistic approach to identifying the optimal sorbents for separation applications. Such methods effectively combine material selection with process optimization to generate a short list of candidate sorbents from a large database of microporous materials.
The disclosed methods build on existing methods for evaluating microporous materials for separation applications (adsorption selectivity, Henry constant, working capacity, total equilibrium capacity, and minimum parasitic energy), while also introducing new metrics that are demonstrated to select good candidates for cost-effective materials. One such metric is shape selectivity, which is a measure of the degree to which the zeolite can separate one molecule from another (or multiple others) based on shape and size exclusion, i.e., “shape selectivity” and “size selectivity”.
Shape selectivity considers the hindrance to molecular transport through the most dominant pathway of a microporous structure. Shape selectivity emphasizes finding a material capable of high throughput, where diffusion through the main pores is much faster for one molecule compared to another, and may be particularly appropriate for membrane or diffusion-limited applications. (Gounaris, C. E., Floudas, C. A., and Wei, J. Chem. Eng. Sci., 61:7933-7948, 2006; Gounaris, C. E., Wei, J., and Floudas, C. A. Chem. Eng. Sci., 61:7949-7962, 2006; Gounaris, C. E., Wei, J., Floudas, C. A., AIChE J., 56:611-632, 2009; First, E. L., Gounaris, C. E., and Floudas, C. A. Langmuir, 29:5599-5608, 2013).
The disclosed methods are the first to utilize the metric of size selectivity, which takes into account the entire distribution of pore sizes. Size selectivity is a measure of relative difference in accessible pore volume between two molecules. In adsorption processes, side channels can play a key role, as molecules may fill into smaller pores of a structure, making size selectivity particularly appropriate. (Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013).
The methods of this disclosure apply the new metric of pore selectivity, which combines the shape-based energetic calculations of shape selectivity with pore accessibility calculations of size selectivity. It is a measure of the energetically-weighted accessibility of the pore system for one molecule compared to the other. (First, E. L., Hasan, M. M. F., and Floudas, C. A. Unpublished manuscript)
One advantage of the disclosed methods is that they combine material selection with process optimization by calculating the cost of the optimal process utilizing each material. This is achieved via a detailed nonlinear algebraic and partial differential equation (NAPDE)-based non-isothermal adsorption model that describes the overall separation process. (Hasan, M. M. F., Baliban, R. C., Elia, J. A., and Floudas, C. A. Ind. Eng. Chem. Res., 51:15665-15682, 2012; Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013). The disclosed methods optimize the separation process based on major independent variables, which may include but are not limited to column length, adsorption pressure, blowdown pressure, desorption/evacuation pressure, and the step durations for adsorption, blowdown, and desorption/evacuation, to minimize the total annualized cost subject to purity and recovery constraints.
The methods of this disclosure leverage an efficient Kriging-based grey-box optimization formulation. (Hasan, M. M. F., Baliban, R. C., Elia, J. A., and Floudas, C. A. Ind. Eng. Chem. Res., 51:15665-15682, 2012; Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013). The result is that we not only select cost-effective materials for a separation application, but also simultaneously provide the optimal process conditions.
One advantage of the above method is identifying new, better materials for separating molecules. This disclosure provides new sorbents. This disclosure provides new zeolites and metal-organic framework (MOF) sorbents. This disclosure provides new methods for using these sorbents in molecular separations. This disclosure provides new methods of treating flue gases, including a new dehydration method. This disclosure provides new methods for purifying methane.
In one embodiment, the disclosed methods are used for post-combustion carbon capture from power plant flue gases (separation of CO2 from primarily N2). Disclosed are 13 zeolite framework types with a process cost for carbon capture and compression up to 150 bar that is lower than the process cost using zeolite 13X, the most popular and commercially available zeolite for this application. These framework types are: AHT, NAB, MVY, ABW, AWO, WEI, VNI, TON, OFF, ITW, LTF, ERI, and MOZ. These new sorbent materials provide better, more cost effective carbon capture benefits than the state of the art zeolites.
We have also identified a number of novel sorbents for methane purification (separation of CO2 from primarily CH4). For example, we have identified 10 zeolite framework types that are both feasible (meaning that the purity of the methane product is at least 97% and at least 95% of the methane is recovered) and cost-effective for some feed conditions. Eight of these zeolites are feasible for all feed conditions in the range of 5%-50% CO2 content. The process cost for these zeolites includes the recovery and compression of methane, and the capture and compression of CO2.
This disclosure provides the following 10 new zeolite framework types for separating carbon dioxide from methane: WEI, AHT, AEN, ABW, APC, BIK, JBW, LTJ, MON, and NSI. The materials consistently in the top 5 for each feed condition are WEI, AHT, and AEN.
In one embodiment, this disclosure provides cost-effective materials for adsorption-based separation of carbon dioxide (CO2) from nitrogen (N2). The processes and compositions are useful for capturing CO2 from flue gas. The applications include (but are not limited to) separating CO2 from the following sources: coal-fired power plants, natural gas-fired power plants, oil-fired power plants, power plants that burn any carbonaceous fuels, agricultural processing plants, ammonia plants, asphalt plants, cement plants, refineries, natural gas processing plants, ethanol plants, petrochemical plants, iron and steel plants, paper and wood plants, sugar plants, and utility plants.
In one embodiment, this disclosure provides cost-effective materials for adsorption-based separation of carbon dioxide (CO2) from methane (CH4). The processes and compositions are useful for purifying methane. Methane purification includes (but is not limited to) purifying the following: natural gas, shale gas, coalbed methane, enhanced oil recovery (EOR) gas, biogas, and landfill gas.
The improved adsorbent materials were identified using a novel computational framework that combines material screening and process optimization. This in silico framework selects the most cost-effective materials for a separation application from a diverse range of feed conditions, including composition, pressure, temperature, and flow rate. The selected materials minimize investment and operating costs while satisfying stringent purity and recovery constraints.
In one embodiment, the disclosed methods include the following: geometric-level pore topology characterization via pore characterization; unique metrics including shape, size, and pore selectivities; atomistic-level molecular simulations on only a subset of the original databases; and adsorption selectivity as a screening stage rather than the final ranking.
In one embodiment, the process optimization portion of the disclosed methods has advantages including the following: feed dehydration to remove water, either pressure swing or vacuum swing adsorption modes to achieve the optimal adsorption; CO2 capture coupled with compression for sequestration; independently operating multiple and identical adsorption columns; blowdown to an intermediate pressure to increase CO2 purity.
In one embodiment, the process optimization portion of the disclosed methods has advantages including the following: either compression or expansion of the feed to achieve the optimal adsorption; CO2 capture coupled with compression for sequestration; independently operating multiple and identical adsorption columns; and product compression and power integration between the feed expander and the product compressor to minimize energy consumption.
The disclosed methods provide the first disclosure of using the above identified zeolites for carbon capture from flue gas (separation of CO2 from N2) or methane purification (separation of CO2 from methane). These zeolites perform these separations with minimal cost.
In one embodiment, the disclosed methods include a new material screening metric (“pore selectivity”) to accommodate molecules with a non-circular footprint.
In one embodiment, the disclosed methods include screening criteria for filtering materials from a zeolite database.
In one embodiment, the disclosed methods include different force field parameters for adsorption calculations.
In one embodiment, the disclosed methods include an improved isotherm fitting algorithm. (Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013).
In one embodiment, the disclosed methods include dehydration of the feed.
In one embodiment, the disclosed methods include process choice, allowing pressure swing or vacuum swing adsorption modes.
In one embodiment, the disclosed methods include process choice, allowing feed expansion or compression.
In one embodiment, the disclosed methods include expansion through an expansion turbine.
In one embodiment, the disclosed methods include selecting a pressure reducing means.
In one embodiment, the disclosed methods include a tool for determining the cost-effectiveness of using an expansion turbine.
In one embodiment, the disclosed methods include a blowdown step to an intermediate pressure to increase CO2 purity.
In one embodiment, the disclosed methods include process conditions including two products (both CO2 and methane are products) that are extracted from the adsorption column at different steps in the cycle.
In one embodiment, the disclosed methods include conditions wherein each product is compressed to a different pressure.
In one embodiment, the disclosed methods include maximum operating pressure in the range of 1-60 bar.
In one embodiment, the disclosed methods include a means for selecting cost-effective materials for carbon capture for a range of feed conditions, including CO2 levels from 5% to 50%.
In one embodiment, the disclosed methods include a means for selecting cost-effective materials for methane purification for a range of feed conditions, including CO2 levels from 5% to 50%.
Disclosed herein is a method of separating molecules comprising:

- identifying molecules in need of separation;
- identifying potential adsorbents for separating the molecules;
- characterizing the pore structure of the said potential adsorbents;
- minimizing the cost of carbon dioxide capture and/or compression for potential adsorbents by solving a mathematical model;
- ranking potential adsorbents based on cost;
- treating the molecules in need of separation with a ranked potential adsorbent.

As used herein, the term “molecules in need of separation” means two or more molecules that are present in a mixture such that it is desired to separate the mixture into two or more portions of different compositions such that the concentration of one or more of the molecules is higher in one or more of the portions than it is in the original mixture. In one embodiment, the molecules in need of separating are two components of a binary mixture that are desired to be separated into two portions such that one of the molecules has a higher concentration in one of the portions than in the original mixture.
As used herein, the term “potential adsorbents” means a set of adsorbents that may or may not be able to perform a desired separation for molecules in need of separation.
As used herein, the term “adsorbent” means a material capable of adsorbing other molecules onto its surface. For example, adsorbents may include zeolites, metal-organic frameworks, zeolitic imidazolate frameworks, silicates, aluminosilicates, titanosilicates, activated carbons, carbon molecular sieves, and covalent-organic frameworks.
As used herein, the term “separating the molecules” means a process by which a mixture containing molecules in need of separation is separated into two or more portions of different compositions such that the concentration of one or more of the molecules is higher in one or more of the portions than it is in the original mixture.
As used herein, the term “characterizing the pore structure” means analysis of the crystal structure or other description of a porous material to describe quantitatively or qualitatively the pore structure of that material. In one embodiment, characterizing the pore structure is the process by which the geometry and topology of the pores of a porous material are described, and this description is used to calculate derived quantities, such as pore limiting diameter, largest cavity diameter, and other data.
As used herein, the term “minimizing the cost” means the application of optimization with the objective of identifying the lowest cost within specified limitations.
As used herein, the term “minimizing the cost of carbon dioxide capture and/or compression” means minimizing the cost of a process to separate carbon dioxide from a mixture of molecules and compressing the carbon dioxide to increase its pressure.
As used herein, the term “treating the molecules” means a process by which a mixture containing molecules in need of separation is separated by putting the mixture into contact with an adsorbent.
In one embodiment of the method, the molecules in need of separating are chosen from hydrocarbons, nitrogen, oxygen, carbon dioxide, and water.
As used herein, the term “hydrocarbons” means the group of molecules with composition containing only carbon and hydrogen, as well as derivatives of such molecules, including those additionally containing oxygen or sulfur.
As used herein, the term “nitrogen” means a gas with the molecular formula N2.
As used herein, the term “oxygen” means a gas with the molecular formula O2.
As used herein, the term “carbon dioxide” means a gas with the molecular formula CO2.
As used herein, the term “water” means a compound with the molecular formula H2O.
In one embodiment of the method, the molecules in need of separating are chosen from CH4, CO2, N2, O2, and H2O.
In one embodiment of the method, the molecules in need of separating are chosen from CH4, CO2, and N2.
In one embodiment, the method of separating molecules comprises identifying minimum purity standards.
As used herein, the term “minimum purity standards” means the lowest acceptable concentration of a molecule of interest in a mixture of molecules.
In one embodiment, the method of separating molecules comprises identifying minimum recovery standards.
As used herein, the term “minimum recovery standards” means the lowest acceptable amount of a molecule of interest that must be present in the purified portion of an original mixture after separation.
In one embodiment, the method of separating molecules comprises ranking the potential adsorbents according to shape and size.
As used herein, the term “ranking the potential adsorbents” means a process of selecting and/or ordering a set of adsorbents based on a metric.
In one embodiment, the method of separating molecules comprises generating adsorption isotherms for said potential adsorbents.
As used herein, the term “adsorption isotherms” means a relationship between the equilibrium adsorption capacity and the partial pressure or concentration of a molecule when it is adsorbed onto the surface of an adsorbent at a given temperature.
In one embodiment, the method of separating molecules comprises calculating the adsorption selectivity for said potential adsorbents.
As used herein, the term “calculating the adsorption selectivity” means a process to quantitatively assess and compare the adsorption affinity of a molecule onto an adsorbent surface relative to the adsorption affinity of another molecule onto the same adsorbent surface.
In one embodiment, the method of separating molecules comprises identifying process conditions.
Disclosed herein is a method for selecting process conditions comprising: minimizing the cost of carbon dioxide capture and/or compression for a database of potential sorbents by solving a mathematical model for process and material parameters.
As used herein, the term “database” means a collection of information.
As used herein, the term “mathematical model” means a set of mathematical expressions and/or equations intended to describe the behavior of a system.
As used herein, the term “process parameters” means the properties, actions, and/or operational decisions that affect a process. In one embodiment of the method, the process parameters include one or more chosen from: column length, adsorption pressure, blowdown pressure, evacuation pressure, step duration for adsorption, step duration for blowdown, and step duration for evacuation.
As used herein, the term “material parameters” means the physical and chemical properties associated with a material.
As used herein, the term “column” means a vessel containing an adsorbent.
As used herein, the term “column length” means the length of a column.
As used herein, the term “adsorption pressure” means the pressure at which a mixture of molecules in need of separation enters the column, some of which are retained within the adsorbent while others pass through for a period of time.
As used herein, the term “blowdown pressure” means the pressure at which the column is retained for a period of time to purge some of the undesired molecules retained inside the column.
As used herein, the term “evacuation pressure” means the pressure at which the column is retained for a period of time to purge some of the desired molecules retained inside the column.
As used herein, the term “step duration for adsorption” means a period of time in which the column is maintained at the adsorption pressure.
As used herein, the term “step duration for blowdown” means a period of time in which the column is transitioning to and maintained at the blowdown pressure.
As used herein, the term “step duration for evacuation” means a period of time in which the column is transitioning to and maintained at the evacuation pressure.
In one embodiment, the method for selecting process conditions comprises selecting a process chosen from pressure-swing adsorption, vacuum-swing adsorption, temperature-swing adsorption, pressure-and-temperature-swing adsorption, and vacuum-and-temperature-swing adsorption, simulated moving bed adsorption, membrane-based separation, or any separation process utilizing said microporous materials or their derivatives.
As used herein, the term “pressure-swing adsorption” means a process by which a mixture of molecules separated by contacting it with an adsorbent at one pressure and desorbing the adsorbed molecules at another pressure.
As used herein, the term “vacuum-swing adsorption” means a pressure-swing adsorption process in which the desorption occurs at a pressure at or lower than atmospheric pressure.
As used herein, the term “temperature-swing adsorption” means a process by which a mixture of molecules separated by contacting it with an adsorbent at one temperature and desorbing the adsorbed molecules at another temperature.
As used herein, the term “pressure-and-temperature-swing adsorption” means a process by which a mixture of molecules separated by contacting it with an adsorbent at one pressure and temperature and desorbing the adsorbed molecules at another pressure and temperature.
As used herein, the term “vacuum-and-temperature-swing adsorption” means a pressure-and-temperature swing adsorption process in which the desorption occurs at a pressure at or lower than atmospheric pressure.
As used herein, the term “simulated moving bed adsorption” means a process by which a mixture of molecules separated based on concentration gradient and sequential switching of ports between adsorbent beds.
As used herein, the term “membrane-based separation” means a process by which a mixture of molecules separated using a membrane.
In one embodiment of the method, the database is filtered by one or more material or process metrics.
In one embodiment the method comprises choosing material metrics from pore limiting diameter, largest cavity diameter, accessible pore volume, accessible surface area, shape selectivity, size selectivity, pore selectivity, adsorption selectivity, permeation selectivity, adsorption isotherms, diffusion coefficient, permeability, minimum parasitic energy, and working capacity.
As used herein, the term “pore limiting diameter” means the largest characteristic guest molecule size for which there is a non-zero accessible adsorbent volume.
As used herein, the term “largest cavity diameter” means the maximum of the pore size distribution for adsorbent pores.
As used herein, the term “accessible pore volume” means the volume of pores of an adsorbent which is accessible to a guest molecule.
As used herein, the term “accessible surface area” means the surface area of pores of an adsorbent which is accessible to a guest molecule.
As used herein, the term “selectivity” means capability of a material to preferentially interact with one molecular species over another.
As used herein, the term “shape selectivity” means selectivity derived from a difference in molecular shape.
As used herein, the term “size selectivity” means selectivity derived from a difference in molecular size.
As used herein, the term “pore selectivity” means selectivity derived from a difference in molecular accessibility in the pores.
As used herein, the term “adsorption selectivity” means selectivity derived from a difference in adsorption.
As used herein, the term “permeation selectivity” means selectivity derived from a difference in permeability.
As used herein, the term “diffusion coefficient” means a constant which is defined as the ratio of the molar flux due to molecular diffusion and the gradient in the concentration of the molecule.
As used herein, the term “permeability” means the degree of permeation of a molecule relative to the permeation of another molecule through a membrane.
As used herein, the term “minimum parasitic energy” means the minimum electric load imposed on a power plant when an additional process is installed and operated. In one embodiment, the minimum parasitic energy is the minimum electric load imposed on a power plant by a carbon dioxide separation process.
As used herein, the term “working capacity” means the net adsorption capacity of an adsorbent when separating a molecule from a mixture of molecules in need of separation. In one embodiment, the working capacity is the difference between the adsorption capacity during adsorption and desorption.
In one embodiment, the method for selecting process conditions comprises choosing process metrics from purity, recovery, energy penalty, and cost.
As used herein, the term “purity” means the degree to which a molecule is mixed or unmixed with other molecules in a given mixture of molecules. In one embodiment, purity means the concentration of a molecule present in a mixture of molecules.
As used herein, the term “recovery” means the amount of a molecule of interest present in the purified portion of an original mixture after separation.
As used herein, the term “energy penalty” means the energy load imposed to a power plant by a separation process.
As used herein, the term “cost” means the expense (monetary, energetic, or other resources) of constructing and/or operating a process. In one embodiment, cost is the total monetary expense incurred in the form of investment, operating, maintenance, material and others. In one embodiment, cost is the energy penalty required to operate the separation process.
Disclosed herein is a molecular separation optimization system comprising:

- a database of porous materials;
- a pore characterization means;
- a separation process model;
- a means for minimizing the cost of a model process; and
- a means for presenting the results to a system user.

In one embodiment of the molecular separation optimization system, the porous materials are chosen from zeolites, metal-organic frameworks, zeolitic imidazolate frameworks, silicates, aluminosilicates, titanosilicates, germanosilicates, activated carbons, and carbon molecular sieves.
As used herein, the term “zeolites” means an open three-dimensional framework structure composed of tetrahedrally-coordinated atoms (“T-atoms”) connected with oxygen atoms. Typically, the T-atoms include silicon and aluminum, but may also include phosphorus, titanium, beryllium, germanium, and other metals. The structure may include extra-framework cations, such as hydrogen, sodium, potassium, barium, calcium, magnesium, iron, gallium, germanium, and others. The zeolite may include defects, such as the result of dealumination and desilication processes. The zeolite may include adsorbed material, including water, gases, and organic materials, such as the result of chemical vapor deposition, chemical liquid deposition, coking, and adsorption processes. The zeolite may include mesopores. The zeolite may be present in a binder, such as to form a powder or pellet.
As used herein, the term “metal-organic frameworks” means a compound composed of metal ions or clusters coordinated to organic ligands, called linkers, to form a regular structure.
As used herein, the term “zeolitic imidazolate frameworks, silicates” means metal organic frameworks composed of metal ions tetrahedrally coordinated to imidazole ligands.
As used herein, the term “aluminosilicates” means a mineral composed of aluminum, silicon, oxygen, and cations.
As used herein, the term “titanosilicates” means a mineral composed of titanium, silicon, oxygen, and cations.
As used herein, the term “germanosilicates” means a mineral composed of germanium, silicon, oxygen, and cations.
As used herein, the term “activated carbons” means carbon processed to introduce pores.
As used herein, the term “carbon molecular sieves” means carbon processed to introduce pores of a precise and uniform size.
In one embodiment of the molecular separation optimization system, the porous materials are chosen from zeolites and metal-organic frameworks.

EXAMPLES

The following examples are illustrative only, and are not intended to be limiting of the invention, as claimed.

Example 1

Process Optimization

Potential zeolites and metal-organic frameworks were identified based on their pore sizes using ZEOMICS and MOFomics, three-dimensional pore characterization methods. (First, E. L., Gounaris, C. E., Wei, J., and Floudas, C. A. Phys. Chem. Chem. Phys., 13:17339-17358, 2011; First, E. L., Floudas, C. A. Micropor. Mesopor. Mater., 165:32-39, 2013). The identified materials were ranked based on shape selectivity and size selectivity. The top structures were selected. Adsorption isotherms were generated for those top structures. The Henry constants were calculated for those top structures. Those top structures were further filtered based on adsorption selectivity. For each sorbent on the short list of remaining candidates, a PSA/VSA process was optimized (using the algorithm depicted in FIG. 1) to obtain the minimum process cost, and the corresponding purity, recovery and energy penalty, using a detailed mathematical model. (Hasan, M. M. F., Baliban, R. C., Elia, J. A., and Floudas, C. A. Ind. Eng. Chem. Res., 51:15665-15682, 2012; Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013). FIG. 3 shows a flow chart representation of the material selection and process optimization method.

Example 2

Carbon Capture from Power Plant Flue Gas

New, better zeolites for carbon capture from power plant flue gases were identified by using the disclosed methods and systems. A flow chart of the material screening and process optimization method is shown in FIG. 3. The accessible volume, accessible surface area, pore limiting diameter (PLD), and largest cavity diameter (LCD) data were calculated for each of 199 silica zeolite structures with pore characterizations from our database, ZEOMICS. (First, E. L., Gounaris, C. E., Wei, J., and Floudas, C. A. Phys. Chem. Chem. Phys., 13:17339-17358, 2011) Shape selectivity was calculated for CO2 versus N2 for each material. (Gounaris, C. E., Floudas, C. A., and Wei, J. Chem. Eng. Sci., 61:7933-7948, 2006; Gounaris, C. E., Wei, J., and Floudas, C. A. Chem. Eng. Sci., 61:7949-7962, 2006; Gounaris, C. E., Wei, J., Floudas, C. A., AIChE J., 56:611-632, 2009; First, E. L., Gounaris, C. E., and Floudas, C. A. Langmuir, 29:5599-5608, 2013). Size selectivity was calculated for CO2 versus N2 for each material. (Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013).
In a screening process, zeolites with shape selectivity greater than 0 or size selectivity greater than 0.15 were selected for further consideration. CO2 and N2 adsorption isotherms were generated for each of these structures at 25° C. to calculate adsorption selectivity. (Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013). Zeolites with adsorption selectivity less than 10 were filtered out. CO2 and N2 adsorption isotherms at four additional temperatures (50° C., 75° C., 100° C., and 125° C.) were calculated for the remaining structures, which were fit to a dual-site Langmuir model.
The equilibrium performance of the selected zeolites was evaluated by using a dual-site Langmuir model fitted with equilibrium isotherm data generated using grand canonical Monte Carlo (GCMC) simulations. (Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013).
To determine whether the transport into the solid phase is controlled by micropore or macropore diffusion, the mass transfer resistances in micropores and macropores were estimated and compared at linear equilibrium.
The calculated micropore and macropore resistances suggested that macropore resistance controls the rate of intraparticle mass transfer, which in the case of zeolite-based CO2 capture, also controls the inter-phase mass transfer. The mass transfer rate between the gas and solid phases was attained by using a linear driving force (LDF) model. (Hasan, M. M. F., Baliban, R. C., Elia, J. A., and Floudas, C. A. Ind. Eng. Chem. Res., 51:15665-15682, 2012; Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013).

Example 3

Capturing Carbon Dioxide from Flue Gas

To evaluate the performance of the top adsorption-selective zeolites in an adsorption process, the following were used: (a) PSA cycle model, (b) a process configuration model, and (c) a detailed adsorption/desorption model for the process.
FIG. 7 shows four steps in a cycle along with their usual pressure profiles. FIG. 6 shows a process diagram. Feed gas is used in step 1 for pressurizing the bed and in step 2 for CO2 adsorption at the adsorption pressure. In the forward blowdown step (step 3), adsorbed N2 is purged by reducing the column pressure to an intermediate pressure. In the last step, the adsorbed CO2 is desorbed by further reducing the column pressure. Starting from an initial bed condition, the process undergoes a transient state for a number of cycles before reaching a cyclic steady state, which is where the initial and final conditions for a cycle appear to be the same. In this example, when the process is started with sorbent saturated with N2, cyclic steady state is usually reached after about 50 cycles.

Example 4

Configuration of a CO2 Capture Process

As shown in FIG. 6, the above 4-step PSA cycle (shown in FIG. 7) can operate in either PSA or VSA mode. In PSA mode, feed gas enters through inlet a where a compressor compresses the gas to the adsorption pressure, while in VSA mode, an inlet is used to feed the gas at atmospheric pressure. The feed is considered to be a mixture of 14% CO2 and 86% N2 at 1 kmol/s. Multiple columns packed with adsorbent zeolites are used, and the total number of columns is calculated using the procedure outlined. (Hasan, M. M. F., Baliban, R. C., Elia, J. A., and Floudas, C. A. Ind. Eng. Chem. Res., 51:15665-15682, 2012; Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013).

Example 5

Separating Carbon Dioxide from Nitrogen

FIG. 7 shows a 4-step PSA cycle with corresponding pressure profiles. FIG. 6 shows a process diagram. Each column has three different outlets. One outlet is used to vent the non-adsorbed gas during the adsorption step; a second outlet is used to purge mostly N2 during the blowdown step; a third outlet is used to recover the product CO2 during the evacuation step. A vacuum pump is placed at an outlet to purge N2 out of the system. Similarly, a second vacuum pump is placed at an outlet to extract CO2 at the lowest pressure. Lastly, the extracted CO2 is compressed to send to the sequestration site using a 6-stage compressor system with interstage cooling.
A detailed nonlinear algebraic and partial differential equation (NAPDE)-based non-isothermal adsorption model was used to describe the overall PSA process. (Hasan, M. M. F., Baliban, R. C., Elia, J. A., and Floudas, C. A. Ind. Eng. Chem. Res., 51:15665-15682, 2012; Hasan, M. M. F., First, E. L., and Floudas, C. A. Phys. Chem. Chem. Phys., 15:17601-17618, 2013). The model was used to evaluate a multi-component adsorption system in an adsorbent-packed column with non-isothermal adsorption/desorption including frictional pressure drop. Temperature and pressure/velocity effects and heat transfer resistance across the column wall were also evaluated.

Example 6

Optimizing the PSA Process for Carbon Capture

The disclosed methods were used to minimize the total annualized cost of CO2 capture and compression using the 4-step PSA process to obtain CO2 at 150 bar for storage. In this example, the minimum purity and recovery were both set to be 90%. The NAPDE model included seven major independent variables, namely column length, adsorption pressure, blowdown pressure, evacuation pressure, and the durations for adsorption, blowdown, and evacuation.
The optimization method is depicted in FIG. 1. The following process steps were performed: (i) solving the original NAPDE model at several fixed conditions to generate input-output data (samples), (ii) developing a Kriging-based surrogate model using the samples, (iii) optimizing the surrogate model for subsequent samplings, and (iv) repeating steps (ii) and (iii) until convergence to an optimal solution. The PSA optimization was performed for each selected zeolite generating a rank-ordered list of cost-effective sorbents. Combinatorial optimization-based techniques were applied for the simultaneous selection of materials and processes for cost-effective separation.

Example 7

Separation of Carbon Dioxide from Methane

The material screening and process optimization method is shown in FIG. 2. Candidate zeolites were identified for separating CO2 from methane. Zeolites with shape selectivity greater than 0, size selectivity greater than 0.1, or pore selectivity greater than 0.1 were selected for further consideration. For zeolites meeting these requirements, CO2 and methane adsorption isotherms were generated at 25° C. to calculate adsorption selectivity. Zeolites with adsorption selectivity less than 10 were filtered out from further consideration. For the remaining structures, CO2 and methane adsorption isotherms were constructed at four additional temperatures (50° C., 75° C., 100° C., and 125° C.), which were fit to a dual-site Langmuir model.
A dual-site Langmuir model fitted with equilibrium isotherm data generated using grand canonical Monte Carlo (GCMC) simulation was used to evaluate the equilibrium performance of the selected zeolites. To determine whether the transport into the solid phase was controlled by micropore or macropore diffusion, the mass transfer resistances in micropores and macropores at linear equilibrium were evaluated.
The process diagram is depicted in FIG. 4. The feed can be expanded or compressed to reach the optimal adsorption pressure. One or multiple identical zeolite-packed adsorption columns were used for adsorption. Since CO2 is selectively adsorbed over methane in many zeolites, most of the methane fed into a column passed through without being adsorbed. The clean methane was then compressed to 60 bar to meet the specification for pipeline transportation.
A desorption (evacuation) vacuum pump was used for column regeneration. The desorption vacuum pump was placed at the feed-end of a column to purge most of the residual CO2 and methane at moderate or low vacuum. CO2 from the vacuum pump was compressed to 150 bar for sequestration by using a 6-stage compression train with intercoolers and a pressure ratio of 2.3 at each stage.
The PSA process was optimized to minimize the total annualized cost of CO2/methane separation and compression using the 3-step PSA process (illustrated in FIG. 5) to obtain methane at 60 bar for transportation and CO2 at 150 bar for transportation for utilization or sequestration.
A NAPDE model using the following five independent variables was used: column length, adsorption pressure, desorption pressure, and the step durations for adsorption and desorption.

Example 8

Determining Conditions for Separating CO2 from a Flue Gas Comprising a Mixture of CO2 and N2

From a database of microporous materials, potential zeolites were identified based on their pore sizes using a three dimensional characterization method. These materials were ranked based on shape selectivity and size selectivity. Next, zeolites with highest rankings from both lists of shape-selective and size-selective materials were selected. Complete adsorption isotherms were calculated for CO2 and N2 by using the crystal structures of the selected materials. The Henry constants were calculated for these materials. The materials were additionally screened based on adsorption selectivity. Each selected zeolite was represented by its isotherm model. For the zeolites meeting the cut-off criteria for shape or size selectivity and adsorption selectivity, PSA/VSA process was optimized to obtain the minimum capture and compression cost and the corresponding purity, recovery and energy penalty using a detailed mathematical model. The minimum cost of capture and compression was used as the final metric to compare those zeolites that can capture CO2 with at least 90% purity and 90% recovery.

Example 9

Capturing CO2 from Coal-Fired Power Plant Flue Gas

In this example, the feed gas was composed of 15% H2O and 85% non-aqueous gases. Of the non-aqueous gases, 14% was CO2 and 86% was N2 and O2. The flow rate was 1 kmol/s.
Notably, the operating conditions remain the same for other flow rates, except that the number of columns or number of trains (identical process configurations operating in parallel) may change. In all cases, feed dehydration is independent of sorbent.
The following process conditions were used:


Unit	Operating conditions	Description

Direct contact	Temp.: 55 → 35° C.	Removes water from gas
cooler		mixture (up to 5.5%)
TEG-absorber	Temp. 35° C.,	Removes water from gas
		mixture (up to 0.1%)
Heater	Temp.: 73° C.	Heats water-rich solvent
Vacuum flash	P: 0.04 bar	Removes water from solvent
unit		using vacuum flash
Cooler	Temp.: 65° C.	Cools water-lean solvent

All sorbents utilized a pressurization step time of 20 s. In each case, the pressurization step brought the column pressure from the evacuation step pressure to the adsorption step pressure. All sorbents used two adsorption columns. However, any number of columns could be used by applying the disclosed optimization methods.


		Adsorp-	Adsorp-	Blow-	Blow-	Evac-	Evac-
		tion	tion	down	down	uation	uation
	Column	step	step	step	step	step	step
Zeo-	length	pressure	duration	pressure	duration	pressure	duration
lite	(m)	(bar)	(s)	(bar)	(s)	(bar)	(s)

AHT	1	2.01	33.73	0.65	49.26	0.03	74.45
NAB	1.08	5.86	20.26	0.61	35.83	0.05	68.59
MVY	1.03	1.94	23.66	0.38	39.43	0.01	82.2
ABW	1	2.34	31.88	0.48	50.77	0.02	69.97
AWO	1	2.2	50	0.35	55.13	0.02	100
WEI	1.03	1.91	20.47	0.39	43.32	0.01	80.74
VNI	1	3.32	24.79	0.59	63.06	0.02	79.91
TON	1	2.92	20	0.39	72.43	0.03	92.8
OFF	1	2.91	30.16	0.36	72.2	0.03	100
ITW	1	2.8	30.22	0.32	72.66	0.03	98.63
LTF	1	2.82	29.76	0.47	82.75	0.02	100
ERI	1	3.08	27.25	0.45	66.54	0.02	87.79
MOZ	1	1.67	28.73	0.29	74.22	0.01	100

The CO2 compression process is independent of sorbent. The CO2 compression process is as follows:


Unit	Operating conditions	Description

6-stage CO ₂	6 stages with intercoolers, each	Compresses CO₂
compression train	having a pressure ratio of 2.33	up to 150 bar

Example 10

Purification of Natural Gas

In this example, the feed was a mixture of CH4 and CO2 at 0.1 kmol/s.
The same operating conditions can be used for other flow rates, except that the number of columns or number of trains (identical process configurations operating in parallel) may change. Such changes could be recognized and applied by an ordinary artisan using the disclosed methods.
In this example, all sorbents and conditions have pressurization step time of 20 s. In this example, all sorbents and conditions use a column length of 1 m.
The following conditions were applied to purifying methane:


	Number	Adsorption	Adsorption	Desorption	Desorption
	of	step	step	step	step
Zeolite	columns	pressure (bar)	duration (s)	pressure (bar)	duration (s)

Feed is 5% CO2, 95% CH4

ABW	2	3	77.22	0.09	69.4
AHT	2	2.64	100	0.08	80
APC	2	3.74	87.67	0.1	80
WEI	2	3.42	61.24	0.1	52.93
AEN	2	2.63	83.28	0.07	80
BIK	2	2.52	100	0.07	80
JBW	2	2.75	100	0.08	80
LTJ	2	5	46.18	0.07	61.34
MON	2	3.26	100	0.09	80
NSI	2	4.24	70	0.01	80

Feed is 10% CO2, 90% CH4

WEI	2	5	54.78	0.1	68.11
ABW	2	2.92	70	0.08	80
AEN	2	2.91	70	0.08	72.25
AHT	2	4.01	40	0.1	46.34
APC	2	3.61	70	0.1	80
BIK	2	2.99	70	0.08	80
MON	2	3.24	70	0.1	80
JBW	2	3.35	70.27	0.08	80

Feed is 20% CO2, 80% CH4

AHT	2	5	40	0.1	56.33
WEI	2	3	40	0.09	55.74
AEN	2	3.62	70	0.09	79.08
APC	2	2.26	48.59	0.06	64.19
BIK	2	2.56	63.54	0.06	80
JBW	2	2.69	50.15	0.09	60.31
MON	2	2.27	70	0.07	80
ABW	2	1.99	40	0.05	42.86

Feed is 30% CO2, 70% CH4

WEI	2	3.6	40	0.1	51.65
AEN	2	3	64.55	0.07	80
APC	2	1.97	41.13	0.06	60.04
AHT	1	3.61	47.9	0.1	127.78
JBW	2	2.6	44.75	0.08	60.94
MON	2	1.89	70	0.05	80
BIK	1	3	40	0.05	120.48
ABW	2	1.65	40	0.02	54.72

Feed is 40% CO2, 60% CH4

WEI	2	3	45.89	0.06	62.65
AHT	2	2.06	40	0.05	55.9
AEN	2	3	40	0.08	49.56
MON	1	3.08	40	0.07	150
APC	1	2.29	40	0.05	143.12
BIK	1	2.18	40	0.05	145.08
JBW	2	2.98	40.55	0.07	43.62
ABW	1	1.35	42.55	0.02	135.65

Feed is 50% CO2, 50% CH4

WEI	2	4.5	40	0.1	57.41
AEN	1	2.9	40	0.07	135.08
MON	1	2.14	40	0.04	150
JBW	1	2.65	40	0.07	150
AHT	1	3	40	0.02	137.03
BIK	1	1.48	42.85	0.03	129.87
APC	1	1.69	40	0.04	80
ABW	1	1.25	41.14	0.01	150

Claims

1. A method for separating molecules comprising:

identifying molecules in need of separation;

identifying potential adsorbents for separating the molecules;

characterizing the pore structure of the said potential adsorbents;

minimizing the cost of carbon dioxide capture and/or compression for potential adsorbents by solving a mathematical model;

ranking potential adsorbents based on cost;

treating the molecules in need of separation with a ranked potential adsorbent.

2. The method of claim 1, wherein the molecules in need of separating are chosen from hydrocarbons, nitrogen, oxygen, carbon dioxide, and water.

3. The method of claim 2, wherein the molecules in need of separating are chosen from CH₄, CO₂, N₂, O₂, and H₂O.

4. The method of claim 2, wherein the molecules in need of separating are chosen from CH₄, CO₂, and N₂.

5. The method of claim 1, comprising identifying minimum purity standards.

6. The method of claim 1, comprising identifying minimum recovery standards.

7. The method of claim 1, comprising ranking the potential adsorbents according to shape and size.

8. The method of claim 1, comprising generating adsorption isotherms for said potential adsorbents.

9. The method of claim 1, comprising calculating the adsorption selectivity for said potential adsorbents.

10. The method of claim 1, comprising identifying process conditions.

11. A method for selecting process conditions comprising:

minimizing the cost of carbon dioxide capture and/or compression for a database of potential sorbents by solving a mathematical model for process and material parameters.

12. The method of claim 11, wherein the process parameters include one or more chosen from column length, adsorption pressure, blowdown pressure, evacuation pressure, step duration for adsorption, step duration for blowdown, and step duration for evacuation.

13. The method of claim 12, wherein the process parameters include all of the following: column length, adsorption pressure, blowdown pressure, evacuation pressure, step duration for adsorption, step duration for blowdown, and step duration for evacuation.

14. The method of claim 12, wherein the process parameters include all of the following: column length, adsorption pressure, evacuation pressure, step duration for adsorption, and step duration for evacuation.

15. The method of claim 11, comprising selecting a process chosen from pressure-swing adsorption, vacuum-swing adsorption, temperature-swing adsorption, pressure-and-temperature-swing adsorption, and vacuum-and-temperature-swing adsorption, simulated moving bed adsorption, membrane-based separation, or any separation process utilizing said microporous materials or their derivatives.

16. The method of claim 11, wherein the database is filtered by one or more material or process metrics.

17. The method of claim 16, wherein the material metrics are chosen from limiting diameter, largest cavity diameter, accessible pore volume, accessible surface area, shape selectivity, size selectivity, pore selectivity, adsorption selectivity, permeation selectivity, adsorption isotherms, diffusion coefficient, permeability, minimum parasitic energy, and working capacity.

18. The method of claim 16, wherein the process metrics are chosen from purity, recovery, energy penalty, and cost.

19. A molecular separation optimization system comprising:

a database of porous materials;

a pore characterization means;

a separation process model;

a means for minimizing the cost of a model process; and

a means for presenting the results to a system user.

20. The system of claim 19, wherein the porous materials are chosen from zeolites, metal-organic frameworks, zeolitic imidazolate frameworks, silicates, aluminosilicates, titanosilicates, activated carbons, carbon molecular sieves, and covalent-organic frameworks.

21. (canceled)