Pressure Swing Adsorption Process Research Articles

A multi-scale method for the study of deep drying of ethylene with 3A zeolite

Coal-based ethanol to ethylene (ETE) is an emerging production pathway with the advantages of a short construction period, high product purity, and few by-products. N2 is commonly used in industry to regenerate zeolites during the ETE process, with the problems of excessive material consumption, energy consumption and environmental pollution. To solve this problem, this paper proposed a method of using the ethylene product as regeneration gas to regenerate the zeolite and recover the desorbed gas. In this work, we adopted a multi-scale simulation method combining microscale apparent diffusivity and macroscale mass transfer models. Reactive force field and molecular dynamics (ReaxFF-MD) were used to obtain an equation for the diffusion coefficient versus temperature. The start-up of the twin-tower temperature and pressure swing adsorption (TPSA) process was simulated using Computational Fluid Dynamics (CFD). The TPSA process achieved an outlet water content below 5 ppm, fulfilling the process specifications.

Simulation and experimental demonstration of helium purification from He/N2 mixtures by pressure swing adsorption

Terrestrial helium is an indispensable non-renewable resource used in medicine, space, electronics, and nuclear research. This paper reports a high-pressure five-step pressure swing adsorption (PSA) cycle using Zeolite 5A for purification of He from dilute He/N2 mixtures. A Dual-Site Langmuir (DSL) model fitted both the low-pressure N2 and high-pressure N2 isotherm data. Dynamic column breakthrough (DCB) experiments were used to confirm volumetric equilibrium loadings. The DCB model predicted the experimental breakthrough curves for a wide range of feed compositions (100% N2, 74.5% N2, and 51.5% N2) and feed pressures (1 bar and 11.2 bar). A large experimental campaign studied the impact of various operating conditions on the He purity and recovery achievable from the PSA process. The study showed that a single-stage PSA experiment could enrich He up to 13% purity with greater than 90% recovery from a feed containing 1% He. Helium purity of 95% and a recovery of 90% can be achieved from a feed containing 10% He. All trends were predicted well by numerical simulations. A multi-objective optimization was performed to maximize helium purity and recovery for various He feed compositions. The study also showed that points from the Pareto curve can be realized experimentally.

Using ligand regulation, metal replacement, and ligand doping strategies on Zr-FUM to improve methane separation from coalbed gas

Developing adsorbents suitable for industrial applications that can effectively enhance the separation of methane (CH4) from nitrogen (N2) in coalbed gas is crucial to improve energy recovery and mitigate greenhouse gas emissions. In this study, three modification strategies were implemented on Zr-FUM, including ligand regulation, metal replacement, and ligand doping, to synthesize Zr-FDCA, Al-FUM, and Zr-FUM-FA, with the aim of improving the performance of CH4/N2 separation under humid conditions. The results demonstrated that the promotion of robust orbital overlap and strengthened electrovalent bonding on adsorbents can selectively enhance CH4 adsorption. As a result, Zr-FUM-FA achieved a saturated CH4 adsorption capacity of 1.37 mmol/g, a CH4 working window of 307 s, and a CH4/N2 sorbent selection parameter (Ssp) of 47.31, exceeding the performance of most reported adsorbents. Analyses of the pore structure, surface morphology, and functional groups revealed that the presence of an ultramicropore proximity to CH4, reduced static resistance, and enhanced electrovalent bond were key factors for CH4 separation. Grand Canonical Monte Carlo and Density Functional Theory studies indicated that the introduction of -C-H- in FA played a crucial role in enhancing CH4 adsorption. Optimization of adsorption parameters using the Aspen adsorption package showed that in a dual-adsorbent bed system, the recovery and purity of CH4 in Zr-FUM-FA reach 99.5% and 97.3%, respectively, providing important theoretical support for the improvement of CH4 recovery in the pressure swing adsorption process from coalbed gas.

A PLC-Embedded Implementation of a Modified Takagi–Sugeno–Kang-Based MPC to Control a Pressure Swing Adsorption Process

The paper presents a case study that applies a model predictive control (MPC) approach in a Micro850 programmable logic controller (PLC) to a laboratory pressure swing adsorption (PSA) process used for separating gas mixtures of CO2 and CH4. PLC is an industrial hardware characterized by its robustness to hazardous environments and limited computational capacities, which poses computational challenges for MPC implementation. This paper’s main contribution is the application of the modified Takagi–Sugeno–Kang-based MPC (MTSK-MPC) algorithm to this PSA unit, which provides features to investigate and implement feasible MPC designs in PLCs. The investigation consists of a sensitivity analysis of how some design parameters influence the PLC memory and the MPC implementation and a comparative evaluation of the computational processing from different MPC algorithms and simulations. The comparison comprises software-in-the-loop simulations with three algorithms in the PC: an implicit MPC, an explicit MPC, and the MTSK-MPC. Additionally, it includes a hardware-in-the-loop simulation with the implemented MTSK-MPC in Micro850. The results show that the MPC algorithms achieve close performance, tracking setpoint changes and rejecting output disturbances, with the MTSK-MPC presenting the lower processing time among the MPCs in the PC. The study concludes that the implementation of MTSK-MPC in the Micro850 is feasible.

Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data

Pressure swing adsorption (PSA) modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior. This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process. To approximate the latent solutions of partial differential equations (PDEs) in the specific steps of pressurization, adsorption, heavy reflux, counter-current depressurization, and light reflux, the system's network representation is decomposed into five lightweight sub-networks. On this basis, we propose a parameter-based transfer learning (TL) combined with domain decomposition to address the long-term integration of periodic PDEs and expedite the network training process. Moreover, to tackle challenges related to sharp adsorption fronts, our method allows for the inclusion of a specified amount of labeled data at the boundaries and/or within the system in the loss function. The results show that the proposed method closely matches the outcomes achieved through the conventional numerical method, effectively simulating all steps and cyclic behavior within the PSA processes.

Multi-objective optimization of ANN-based vacuum pressure swing adsorption process for ethane and ethylene separation

A bilevel optimization methodology was developed for separating ethane and ethylene using vacuum pressure swing adsorption. Data generated through Latin hypercube sampling and normalization were employed to construct a neural network at a lower level, serving as a surrogate model for the comprehensive first-principle adsorption process. Following sensitivity analysis based on Monte Carlo simulation, optimization, data resampling, and reconciliation were performed at an upper level. Two cases were performed to optimize the ethane and ethylene separation process. In the first scenario, ethylene recovery was optimized under a purity constraint, resulting in an enhancement from 65.28 % to 87.19 %. In the second scenario, both ethylene recovery and energy consumption were simultaneously optimized with the purity constraint, leading to the generation of a Pareto front. From this Pareto front, two operating conditions were determined: one using TOPSIS and the other aimed at reducing energy consumption from a conventional distillation column to 0.733 MJ/kg-ethylene. Compared to conventional distillation, the vacuum pressure swing adsorption (VPSA) process showed 82.8 % recovery with 0.747 MJ/kg-ethylene and 72.21 % recovery with 0.683 MJ/kg-ethylene. A dynamic analysis and an economic analysis of scaling up VPSA process were performed to compare with C2 splitter.

Optimizing medical oxygen concentrators: Efficiency, flexibility, and patient-centric solutions

Medical oxygen concentrators (MOCs) play a vital role in providing oxygen therapy to patients with respiratory diseases. This paper offers a comprehensive evaluation of different adsorbents, including 13X, LiLSX, 5A, CaX, Ag-ETS-10, and AgLiLSX, utilized in MOCs. Process modeling and multi-objective optimization are conducted to assess the effectiveness of these adsorbents using the Skarstrøm cycle, employing pressure swing adsorption (PSA), vacuum swing adsorption (VSA), and pressure vacuum swing adsorption (PVSA) processes. The optimization result indicates that LiLSX is the top choice, providing medical-grade oxygen at a total product flow rate exceeding 15 SLPM. Additionally, 13X and CaX are identified as economical alternatives, particularly suitable for employment in the PSA and VSA processes, respectively. This study highlights the significance of establishing a connection between the Medical and Chemical Engineering communities through the direct incorporation of medical performance indicators, such as the fraction of inspired oxygen (FIO2), into the optimization process. The findings serve as a guide for selecting an appropriate design based on the patient’s requirements and demonstrate LiLSX’s capability to meet a diverse range of treatments, achieving FIO2 values up to 0.75. Furthermore, we explore the flexibility of MOC designs in meeting the demands of multiple patients simultaneously and propose energy-efficient design options based on patient needs. Overall, this study provides valuable insights into optimizing MOCs, ensuring effective and reliable oxygen therapy for patients, while also addressing challenges like the COVID-19 pandemic and potential global lithium scarcity.

Research on Oil and Gas Adsorption Optimization Based on CFD Modeling and Process Simulation.

In the process of oil extraction and refining, some of the liquid light hydrocarbon components will inevitably evaporate into the atmosphere, causing serious air pollution and safety hazards. This paper is focused on oil and gas adsorption systems to comprehensively optimize key parameters by combining computational fluid dynamics (CFD) modeling with process simulation, enabling the efficient treatment of hazardous materials. First, a CFD model of the hydrocarbon adsorption process is established to the porous media model by a user-defined function (UDF). Subsequently, the mass transfer process of oil and gas in porous media is successfully simulated to obtain the gas distribution in an industrial fixed bed adsorption tower. The adsorption tank is intensified, and the gas distribution in the tank is improved by optimizing the height-to-diameter ratio of the equipment and the design of the intake distributor. Third, the cyclic two-tank adsorption model of the pressure swing adsorption (PSA) process is established for key parameters optimization. Finally, the operating parameters and conditions of the PSA process are suspected by considering five factors affecting the adsorption efficiency: adsorption time, adsorption pressure, adsorption temperature, feed flow rate, and purge ratio of the washing step.

Efficient hybrid strategy for simultaneous design of refinery hydrogen networks and pressure swing adsorption unit

Refineries face increasing hydrogen demand due to the consumption of heavy and sour crude oil. This study presents a novel hybrid method to optimize hydrogen network configurations with a rigorous pressure swing adsorption (PSA) process model. This method utilizes Bayesian optimization to address the upper-level unknown constraints black-box optimization problem while employing deterministic algorithms to manage the lower-level nonlinear programming problem. Furthermore, infeasible regions are transformed into feasible ones by introducing a dummy stream, and the costs incurred during the transformation process are accurately calculated. The proposed method is validated using two literature cases. In Case 1, Bayesian optimization outperformed the Kriging model, resulting in a 7.84% reduction in total annual cost (TAC). In Case 2, a comparison of four search strategies revealed the developed two-phase strategy as the most effective one. This method offers a powerful and practical tool for optimizing real-world hydrogen networks with PSA processes.

Multi-objective optimization of a hybrid carbon capture plant combining a Vacuum Pressure Swing Adsorption (VPSA) process with a Carbon Purification unit (CPU)

The imperative challenge posed by climate change requires urgent actions to counteract the harmful effects of greenhouse gas emissions, particularly CO2, which contributes to approximately 80 % of emissions responsible for global warming. A hybrid system combining Vacuum Pressure Swing Adsorption (VPSA) unit with a Cryogenic Carbon Purification Unit (CPU) is evaluated to enhance recovery and purity of CO2captured from flue gas containing CO2concentration ranging from 5 % to 20 %. VPSA preconcentrates the CO2and CPU completes the separation and purifies the CO2. The study uses surrogate models for multi-objective optimization, considering energy consumption, cost, and CO2recovery, providing a time-efficient approach for investigating computationally demanding processes. Results from the study indicate that the hybrid system achieves over 90 % recovery for flue gas concentration range considered, while ensuring the production of high-purity CO2(>99.99 %) suitable for transportation. A trade-off analysis reveals the balance between recovery, electricity consumption, and economic viability. A sensitivity analysis identifies parameters influencing recovery and energy consumption, providing guidance for future optimization efforts. The techno-economic analysis highlights the impact of electricity prices and carbon taxes on total costs, identifying an optimum towards higher recovery values under rising carbon taxes. Furthermore, the research underscores concentration-dependent economic feasibility, emphasizing the attractiveness of concentrations above 10 % compared with other technologies, which require higher concentrations. For an electricity price of 75 €.MWh−1, the total cost of the CO2 capture hydride system considering CO2 emissions with carbon tax of 100 €.tCO2−1 for concentrations ranging from 10 % to 20 % is from 123 to 80 €.tCO2−1, respectively. The analysis of the electricity source shows the importance of a low-carbon emission energy mix for optimal carbon emission reduction.

Vacuum pressure swing adsorption intensification by machine learning: Hydrogen production from coke oven gas

Hydrogen is a vital resource in the fight against climate change, and it has the potential to revolutionize the energy sector. Our research focused on optimizing the production of high-purity hydrogen using coke oven gas (COG), a valuable hydrogen source in the steel industry. By leveraging advanced artificial neural networks (ANNs), we can predict the performance and exergy efficiency of a 6-bed 12-step vacuum pressure swing adsorption (VPSA) process accurately and efficiently. The Pareto fronts were addressed by combining the evolutionary algorithm with ANNs, and the effects of operating parameters were discussed in detail. Importantly, we found that our VPSA process can achieve a hydrogen purity of 99.99% with 45.2% exergy efficiency. We also demonstrated that using ANNs can significantly enhance VPSA process optimization, making it a valuable tool for extracting high-purity hydrogen from COG.

Separation of CO2/N2 onto Shaped MOF MIL-160(Al) Using the Pressure Swing Adsorption Process for Post-combustion Application

Separation of CO₂/N₂ onto Shaped MOF MIL-160(Al) Using the Pressure Swing Adsorption Process for Post-combustion Application

Biogas upgrading using shaped MOF MIL-160(Al) by pressure swing adsorption process: Experimental and dynamic modelling assessment

Biogas has been introduced as a sustainable source of energy, which is considered as a promising alternative for conventional fossil fuels. Indeed, biogas requires to be upgraded from the impurities, specifically, carbon dioxide to be commercially utilized. In this study, the potential of shaped form MIL-160(Al) as a water stable Al dicarboxylate microporous MOF has been assessed concerning the biogas upgrading application. To this end, firstly, the dynamic fixed-bed adsorption of carbon dioxide and methane was investigated at 313 K and 4.0 bar. The measured breakthrough outcomes were simulated with a developed mathematical model, which the results confirmed an acceptable potential of model predictions. Afterwards, a pressure swing adsorption (PSA) process with 5-steps was designed relying on dynamic equilibrium results, and experimentally validated by a lab-scale PSA set-up for a 50:50 CO2/CH4 mixture. Finally, an industrial PSA process was designed to have a precise knowledge on the potential of MIL-160(Al) for biogas upgrading for large scale applications. The results demonstrated the purity and recovery of methane around 99 % and 63 %, respectively, which indicated the appealing capacity of this adsorbent for such a purpose.

Techno-economic analysis of vacuum pressure swing adsorption process for a sustainable upgrading of biogas

Vacuum pressure swing adsorption (VPSA) has demonstrated promising features for the upgrading of biogas to biomethane. In this study, a biogas upgrading plant, comprised of a hybrid of an N-column VPSA unit (2 ≤N ≤ 6) with a combined heat and power (CHP) engine, was developed and its techno-economic characteristics were assessed via a mathematical approach. Moreover, the techno economic analysis was used for the state-of-the-art VPSA configuration (a sophisticated configuration) and compared with the developed hybrid process. The prominent parameters including feedstock transport, biogas production, desulfurization, drying, upgrading, combustion, and grid injection were considered in the analyses of the plant for the upgrading capacity in the range of 100–6500 Nm3/h. Sensitivity analysis of the most influencing parameters, i.e., electricity price, gas price, and feed processing revenues, was conducted for the developed models. Beside comparing upgrading cost of the sophisticated VPSA with other upgrading technologies, a detailed comparison with the best available membrane unit for biogas upgrading was conducted. The limitations of adsorption process and VPSA in reducing the upgrading cost were also investigated. The results showed that in the absence of subsidies and requirements for CO2 capture, the hybrid plant outperforms the sophisticated VPSA units. Also, higher market price of natural gas or feedstock processing revenues were necessary in order to render the plant profitable. The results showed that, at flowrates larger than 175 Nm3/h, the sophisticated VPSA unit required a lower investment cost than the membrane unit for identical outputs. The results also show that even at the most idealistic conditions in the adsorption process, the upgrading is not economically favorable without subsidies. The findings of this study shed light on the importance of process design for biogas upgrading.

Thermodynamic and thermoeconomic analysis and optimization of a renewable-based hybrid system for power, hydrogen, and freshwater production

To address environmental pollution effectively, it is crucial to promote the increased utilization of renewable energy sources. Furthermore, an appealing opportunity arises from enhancing the efficiency of renewable-based power plants while diversifying their product output. This study introduces a hybrid system that revolves around renewable resources, with a primary focus on evaluating power generation, hydrogen production, and freshwater extraction. The designed system seamlessly integrates a flash-binary geothermal power plant with a solar system, incorporating cutting-edge phase-change material storage technology. Hydrogen is generated through a combination of steam-methanol reforming and pressure swing adsorption processes. Freshwater is procured utilizing humidification-dehumidification and multi-effect desalination units. In terms of power generation, the system leverages the capabilities of the geothermal power plant alongside a modified Kalina cycle. The performance of this integrated system is rigorously evaluated through a combination of thermodynamic and thermoeconomic approaches. An exergy-economic optimization scenario is employed to determine the most efficient operational mode. The results of this comprehensive analysis reveal that the system can produce 0.0224 kg/s of hydrogen, 8.017 kg/s of freshwater, and generate 215.9 W of net power. Impressively, it achieves an exergy efficiency of 58.3% at a unit cost of $32.23/GJ in the base mode. Furthermore, the optimal operating state boosts efficiency to 60.59%, with a unit cost of $32.22/GJ. Notably, adjustments in the selling price of hydrogen have a significant impact on the system's financial metrics. As the price of hydrogen rises from $5 to $6/kg, the payback period reduces from 4 to 3 years, and the net present value surges from $5.81 million to $10.16 million.

Uncovering the potential of MSC CT-350 for CO2/CH4 separation toward the optimization of a Pressure Swing Adsorption process for biogas upgrading

A laboratory-scale fixed-bed column is employed to study the dynamic behavior of the carbon molecular sieve MSC CT-350 for CO2/CH4 separation. Breakthrough adsorption tests in single-component systems are carried out at different pressures (1, 3, 5, 6.5 and 8 bar) and constant temperature (20 °C). Moreover, an additional test is conducted with a 40% CO2/60% CH4 binary mixture at 3 bar. Desorption tests are performed by varying the purge-to-feed ratio (P/F) at 50%, 30% and 20%, optionally using a vacuum pump. Experimental results show that MSC CT-350 has a good CO2 adsorption capacity for each pressure, considerably higher than CH4. In the binary test, very slight differences are experimentally found in the adsorption kinetics and equilibrium adsorption capacity with respect to single-compound tests, which results equal to 2.16 mol kg−1 for CO2 and 0.302 mol kg−1 for CH4 at 3 bar, compared with 2.29 mol kg−1 for CO2 and 0.262 mol kg−1 for CH4 for the single-compoound counterparts. The time required for a complete regeneration decreases with the increase in purge flowrate and with the simultaneous use of the vacuum pump. Finally, CO2 adsorption is a reversible process as the CO2 adsorption capacity of the adsorbent is not significantly reduced when utilized in subsequent adsorption-desorption cycles.

Techno-economic analysis of carbon molecular sieve membranes to produce oxygen enrichment air

Oxygen Enriched Air (OEA) is essential for medical applications, metal production, chemical production, the petrochemical industry, and energy processes. In this work, carbon molecular sieve membranes (CMSM) and membrane systems are analyzed concerning their technical and economic feasibility to produce OEA for medical, post-combustion, and oxy-combustion applications. Process parameters, such as stage-cut, feed-to-permeate pressure ratio, membrane area, and the number of stages, were studied to assess the technical and economic feasibility of this technology. In this work, it was observed that for each membrane, there is a permeating oxygen concentration that minimizes the specific oxygen cost. The production of 56 tonO2·day−1 for a combustion enhancement process with a concentration of 72 % was obtained in a single-stage with a specific cost of 61.0 €·tonO2−1. A single-stage is also effective for producing OEA for medical applications (required concentration of 82 %) at a minimum separation cost of 45.8 €·tonO2−1. However, a two-stage system is required to produce OEA with a concentration higher than 95 % for oxy-combustion with an OEA production cost of 83.8 €·tonO2−1. These production costs are lower than those obtained with two different commercial membranes and are competitive with medium-scale cryogenic distillation and pressure swing adsorption processes.

Multidisciplinary high-throughput screening of metal–organic framework for ammonia-based green hydrogen production

This study presents a comprehensive, multidisciplinary approach to high-throughput screening (HTS) of metal–organic frameworks (MOFs) for efficient ammonia-based green hydrogen production. Previous HTS methods, relying on molecular simulations, often estimate adsorption uptakes under constant conditions, leading to inadequate evaluation of MOFs for practical applications where competitive adsorption and varying operating conditions are critical. To address this limitation, we propose a multidisciplinary HTS method employing an efficient computational algorithm that minimizes the number of molecular simulations while generating isotherm models across a wide pressure range. Our algorithm strategically selects simulations for further analysis based on initial results. Using this innovative approach, we screened 12,020 MOFs. Furthermore, as the next step of the multidisciplinary approach, we performed pressure swing adsorption (PSA) process simulations to assess MOFs’ potential for purifying green hydrogen derived from decomposed ammonia, which can be produced by sustainable energy sources and transported intercontinentally. The top-performing MOFs were evaluated for stability based on literature data and MOFSimplify, a neural network model estimating thermal stability. By considering adsorption process operation and MOF stability, the proposed HTS methodology identified 10 high-performing materials, with RAVXIX ranking as the top adsorbent. This comprehensive evaluation advances the search for efficient MOFs in green hydrogen production, contributing to the development of a more sustainable hydrogen-based economy.

High throughput screening of pure silica zeolites for CF4 capture from electronics industry gas.

The capture and separation of CF4 from CF4/N2 mixture gas is a crucial issue in the electronics industry, as CF4 is a commonly used etching gas and the ratio of CF4 to N2 directly affects process efficiency. Utilizing high-throughput computational screening techniques and grand canonical Monte Carlo (GCMC) simulations, we comprehensively screened and assessed 247 types of pure silicon zeolite materials to determine their adsorption and separation performance for CF4/N2 mixtures. Based on screening, the relationships between the structural parameters and adsorption and separation properties were meticulously investigated. Four indicators including adsorption selectivity, working capacity, adsorbent performance score (APS), and regenerability (R%) were used to evaluate the performance of adsorbents. Based on the evaluation, we selected the top three best-performing zeolite structures for vacuum swing adsorption (LEV, AWW and ESV) and pressure swing adsorption (AVL, ZON, and ERI) processes respectively. Also, we studied the preferable adsorption sites of CF4 and N2 in the selected zeolite structures through centroid density distributions at the molecule level. We expect the study may provide some valuable guidance for subsequent experimental investigations on adsorption and separation of CF4/N2.

High adsorption selectivity of activated carbon and carbon molecular sieve boosting CO2/N2 and CH4/N2 separation

High adsorption selectivity of activated carbon and carbon molecular sieve boosting CO2/N2 and CH4/N2 separation