A technical and economic evaluation of two different oxygen sources for a small oxy-combustion unit

Pressure swing adsorption of Li exchange hierarchical X zeolite for pure hydrogen from binary gas mixture

Simulation of integrated novel PSA/EHP/C process for high-pressure hydrogen recovery from Coke Oven Gas

Scalable solvent-free preparation of [Ni3(HCOO)6] frameworks for highly efficient separation of CH4 from N2

Process intensification in duplex pressure swing adsorption

The enrichment of useful trace components in air was carried out using a new two-bed pressure swing adsorption (PSA) process that utilized an enriching reflux (ER) cycle designed specifically for enriching the heavy component. Another new feature of this PSA process was the use of parallel equalization (PEQ), which not only saved recompression energy, but also favorably exchanged the axial concentration distribution between the two columns. These new PSA concepts were demonstrated by experimentally enriching Xe and CO2 in air using 13X MS zeolite. ER PSA substantially enriched both gases to values as high as 80 times the feed concentration with 90% recovery using a pressure ratio of only 12.5. This enrichment was far superior to that achieved with a conventional stripping reflux (SR) PSA cycle, which was limited thermodynamically to the pressure ratio and was typically less than half of it. A parametric study revealed the effects of the pressure ratio, half-cycle time, feed flow rate, flow rate ratio (enriched gas/feed), and several different PEQ schemes on the enrichment of each gas. Increases in the flow rate and pressure ratios resulted in increases in the enrichments of both gases, whereas increases in the feed flow rate and half-cycle time revealed maxima in the enrichments. Partial PEQ through the upper half of the column nearer to the feed (lean) end was also determined to be superior to other PEQ schemes.

A two-bed pressure swing adsorption (PSA) process using activated carbon was studied to recover hydrogen from the effluent gas (H2/CO/CO2, 39.3:35.4:25.3 vol.%) of a melting incineration process. The adsorption dynamics of the activated carbon bed were investigated by breakthrough experiments. Since the product purity needs depend on demands of the incinerator site, various PSA operating conditions, such as purge to feed (P/F) ratio, adsorption (AD) step time, adsorption pressure, and feed flow rate, were investigated to recover H2 with a wide range of purities. The purity varied with P/F ratio, and the recovery varied with AD step time asymptotically because bed purification by H2 product purge approached a limitation. On the other hand, the variations in purity and recovery with P/F ratio, AD step time, and adsorption pressure were almost linear or only slightly curved. The variation in purity with feed flow rate was similar to the variation with P/F ratio while the recovery trend was more similar to the variation with the AD step time. Because the propagation velocity was significantly different for CO and CO2, the PSA performance was mainly affected by CO propagation, but the contribution of the CO2 heat of adsorption to the bed should be considered. The PSA process in this study produced hydrogen with a purity of 75.43–99.99% and a recovery of 90.99–49.29%. When a syngas (H2 and CO) is needed, the PSA process can result in high recovery and productivity because the major impurity in the product is CO.

Layered two- and four-bed PSA processes for H2 recovery from coal gas

GHG emission factors developed for the collection, transport and landfilling of municipal waste in South African municipalities

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass‐based biofuel production in Michigan. Using three life cycle assessment (LCA) databases—US lifecycle inventory (USLCI) database, GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real‐world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.

Emission of greenhouse gases from waste incineration in Korea

High-level moisture removal is often encountered in the pressure swing adsorption (PSA) for air prepurification. The effects of high-concentration water vapor adsorption on the heat and mass transport characteristics of PSA should be described in detail for further design and optimization of air prepurification processes. In this work, a mathematical model of an alumina/13X-layered two-bed Skarstrom-type PSA cycle for the removal of high-level moisture along with CO2 from air is established to study the heat and mass transport characteristics during the process. The maximum increase and decrease in temperature are related to the water vapor concentration in the feed air, and two simplified formulas are proposed to estimate their magnitudes. The mass transport characteristics, especially the penetration depths of the two impurities, are examined under different inlet temperatures, adsorption pressures, purge-to-adsorption flow rate ratios, inlet flow rates, and cycle times. A relation between the penetration depth of water vapor and the five operating parameters is developed and can be readily used to predict the location of the water vapor adsorption front in the PSA design for air prepurification and other purification processes involving high-level moisture.

In this study, seasonal greenhouse gas (GHG) emission factors were developed to realize the true CO2 reduction potential of a small scale renewable energy technology. The new hourly greenhouse gas emission factors based on hour-by-hour demand of electricity in Ontario, and the average Greenhouse Gas Intensity Factor (GHGIF J were estimated by creating a series of emission factors and their corresponding profiles that could be easily incorporated into simulation software. The use of regionally specific climate-modeled factors, such as those identified, allowed for a more accurate representation of the benefits associated with GHG reducing technologies, such as photovoltaic, wind,etc. It was determined that using Time Dependent Valuation (TDV) emission factors provided an upper limit 'while using hourly emission factors provided a lower limit. In addition, since there is a correlation between the electricity generated and emissions from utilities, several neural network (NN) models were developed in order to predict the hourly emission factor for the province of Ontario. Two methodologies were explored and resulted in good predictions. However, methodology 2 proved to be more accurate in predicting the hourly emission factor for the Province of Ontario.

In this study, seasonal greenhouse gas (GHG) emission factors were developed to realize the true CO2 reduction potential of a small scale renewable energy technology. The new hourly greenhouse gas emission factors based on hour-by-hour demand of electricity in Ontario, and the average Greenhouse Gas Intensity Factor (GHGIF J were estimated by creating a series of emission factors and their corresponding profiles that could be easily incorporated into simulation software. The use of regionally specific climate-modeled factors, such as those identified, allowed for a more accurate representation of the benefits associated with GHG reducing technologies, such as photovoltaic, wind,etc. It was determined that using Time Dependent Valuation (TDV) emission factors provided an upper limit 'while using hourly emission factors provided a lower limit. In addition, since there is a correlation between the electricity generated and emissions from utilities, several neural network (NN) models were developed in order to predict the hourly emission factor for the province of Ontario. Two methodologies were explored and resulted in good predictions. However, methodology 2 proved to be more accurate in predicting the hourly emission factor for the Province of Ontario.

Multi-product carbon footprint assessment for low-rank coal-based acetylene manufacturing process

The energy mix serves as the basis for calculating the greenhouse gas (GHG) emission factor of electricity, and therefore has a significant impact not only on the evaluation of GHG-related policies but also on the carbon footprint of industries that use electricity in life cycle assessments (LCA). As such, there is a need to annually publish GHG emission factors that reflect changes in the energy mix. The purpose of this study is to present IPCC- and LCA-based GHG emission factors and GHG emissions for the energy sector based on changes in the energy mix, and to assess whether the nationally determined contribution (NDC) can be achieved under these conditions. To achieve this, the study applies Intergovernmental Panel on Climate Change (IPCC) and LCA-based GHG emission factors for each power source to the energy mix outlined in the 10th Basic Plan for Electricity Supply and Demand, calculating annual (2018~2036) national electricity GHG emission factors and emissions, and analyzing the feasibility of meeting the NDC target. The analysis revealed that GHG emission factors fluctuate significantly with changes in the energy mix, underscoring the need for annual calculations. Under the planned energy mix, GHG emissions from the energy transition sector are projected to reach 159.9 million tons CO2eq, which exceeds the NDC target of 149.9 million tons CO2eq. However, reducing coal-fired power generation by 10% and replacing it with offshore wind and solar power could make achieving the target feasible. Additionally, the LCA-based GHG emission factors indicate that expanding offshore wind and solar power instead of relying on hydrogen and ammonia in the energy mix could achieve a 2.5% reduction. Therefore, adopting methodologies such as those used in this study to calculate annual GHG emission factors would allow efforts to transition the energy mix to be immediately reflected. Furthermore, when planning the energy mix, an LCA-based approach rather than an IPCC-based approach would provide a more effective means of responding to practical environmental regulations