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The paper was presented at the 33rd European Symposium on Computer Aided Process Engineering (ESCAPE33) June 18-21, 2023, in Athens, Greece.
This study characterized and modeled heterogeneous surface wettability in sandstone and investigated the role of spatial heterogeneity and correlation length of surface wettability on relative permeability in a supercritical CO 2 (scCO 2 )-brine-rock system. Understanding the role of wettability heterogeneity on relative permeability is essential to geological CO 2 sequestration, oil and gas recovery, and contaminated groundwater remediation. Although numerous studies have attempted to understand the influences of surface wettability, capillary number (Ca), and viscosity ratio, the role of the spatial variation and correlation length of surface wettability on two-phase flow in three-dimensional (3D) porous media has not been unraveled due to the challenges in the measurement and representation of realistic rock surface wettability. In this work, we conducted in-situ measurements of surface contact angle (CA) in a Bentheimer sandstone after CO 2 flooding using micro-computed tomography (micro-CT), and found that the pore-scale CA distribution on rock surfaces followed a log-normal distribution associated with a spatial correlation length. Based on the statistical information from CT scanning, a Gaussian random field was used to model CA distributions that had desired standard deviations and spatial correlation lengths, which were then adjusted within a certain range of values for sensitivity analyses to study their combined effects on the two-phase flow in the porous medium using the lattice Boltzmann (LB) method. The LB two-phase flow simulation was accelerated using hybrid, multicore parallel computing to overcome the challenges in simulating multiphase flow in a large 3D domain having 800 × 800 × 600 nodes. The simulation results showed that the surface wettability heterogeneity (i.e., standard deviation of CA) had a lesser effect on the relative permeability of the wetting fluid (water) but a more significant impact on the relative permeability of the non-wetting fluid (scCO 2 ). The Corey model was used to fit the LB-simulated relative permeability curves of water and scCO 2 and showed that the variations in the relative permeability curves for both water and scCO 2 increased as the standard deviation and spatial correlation length of CA increased. This study illustrated that the assumption of homogeneous surface wettability may cause errors in multiphase flow simulations. Furthermore, the impacts of both the standard deviation and spatial correlation length of CAs should be accounted for. This is the first study that explored the spatial correlation lengths associated with CA distributions on sandstone surfaces and comprehensively investigated the roles of both spatial variation and correlation length of CA on two-phase flow properties in 3D porous media. The optimized LB multiphase flow model was proved a powerful tool to study the interplays and combined effects of these statistical parameters, which had critical applications in numerous natural and engineering processes that involved multiphase flow in porous media.
Outdoor photoautotrophic algal growth is limited by light attenuation and attendant respiratory CO 2 losses during dark periods, limiting its productivity potential and carbon use efficiency. Developing a system that leverages mixotrophic growth (combining the benefits of both heterotrophic and photoautotrophic growth) has the potential to dramatically improve the total productivity and economics of the system. However, it is unknown if the productivity gains offset the added costs of outdoor mixotrophic cultivation using cellulosic hydrolysate as the feedstock. In this study, corn stover-derived cellulosic sugars were evaluated as the mixotrophic organic carbon source for the cultivation of Galdieria sulphuraria, which can metabolize both glucose and xylose from corn stover hydrolysate. A techno-economic analysis (TEA) and a life-cycle assessment (LCA) were conducted based on a detailed engineering process model for both glass helical photobioreactor and covered pond cultivation platforms, coupled with downstream conversion and upgrading to renewable diesel through hydrothermal liquefaction. Results show the minimum biomass selling price for cultivation in the photobioreactor design assuming a productivity of 1.575 kg m -3 day -1 and a substrate yield of 0.57 g g -1 is $2869 per dry metric ton. The costs are dramatically reduced in the covered pond design which assumes a productivity of 0.8 kg m -3 day -1 and a substrate yield of 0.7 g g -1 , 921 dollars per dry metric ton. Expanding the system boundary to include downstream processing results in a minimum fuel selling price of 8.24 dollars and 3.32 dollars dm 3 GE -1 for the photobioreactor and covered pond systems, respectively. Finally, life-cycle results demonstrate a global warming potential of 339 and 9.1 gCO 2 -eq. MJ -1 on a well-to-wheels basis and a net energy ratio of 2.21 and 0.25 MJ MJ -1 for the photobioreactor and covered pond systems, respectively. Discussion focuses on a required co-product selling price as a function of biomass diversion to meet economic parity with conventional fuels.
Here, small modular nuclear reactors (SMRs) represent a robust opportunity to develop low-carbon and reliable power with the potential to meet cost parity with conventional power systems. This study presents a detailed, bottom-up economic evaluation of a 12 × 77 MW e (924 MW e total) light-water SMR (LW-SMR) plant, a 4 × 262 MW e (1,048 MW e ) gas-cooled SMR (GC-SMR) plant, and a 5 × 200 MW e (1,000 MW e total) molten salt SMR (MS-SMR) plant. Cost estimates are derived from equipment costs, labor hours, material inputs, and process-engineering models. The advanced SMRs are compared to natural gas combined cycle plants with and without post-combustion carbon capture and a conventional large nuclear reactor. Overnight capital cost (OCC) and levelized cost of energy (LCOE) estimates are developed. The OCC of the LW-SMR, GC-SMR, and MS-SMR are found to be $\$4,844$/kW, $\$4,355$/kW, and $\$3,985$/kW respectively. The LCOE of the LW-SMR, GC-SMR, and MS-SMR are found to be $\$89.6$/MWh, $\$81.5$/MWh, and $\$80.6$/MWh respectively. A Monte Carlo analysis is performed, for which the OCC and construction time of the LW-SMR is found to have a lower mean and standard deviation than a conventional large reactor. The LW-SMR OCC is found to have a mean of $\$5,233$/kW with a standard deviation of $\$658$/kW and a 90 % probability of remaining between $\$4,254$/kW and $\$6,399$/kW, while the construction duration is found to have a mean of 4.5 years with a standard deviation of 0.8 years and a 90 % probability of remaining between 3.4 and 6.0 years. The economic impact of economies of scale, simplification, modularization, and construction time for SMRs are discussed. Additionally, policy implications for direct SMR capital subsidies and the impact of a carbon tax on natural gas emissions are explored.
Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics — where are boundaries located? what segments are logically similar? — change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. Here, we apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, serves as a post-processing engine that extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero-shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L).
Global plastic production has reached 413.8 million metric tons in 2024 and is forecasted to surpass 1.2 billion metric tons by 2050. Polyolefins, mainly polyethylene (PE) and polypropylene (PP), dominate single-use packaging and account for approximately 55% of global plastic waste. The chemical inertness that makes these materials desirable for commercial applications also renders them persistent in the environment. Current recycling technologies have proven to be insufficient to divert plastic waste from landfills or environmental loss due to technical limitations and poor economic incentives. Conventional mechanical recycling is a form of downcycling, in which the polymer remelting process results in products with inferior material properties and reduced market value. Pyrolysis, a thermochemical route used for the chemical recycling of plastic wastes into refinery feedstock, requires severe conditions, typically 400–600 °C in an oxygen-free environment. The high temperature drives up energy costs and produces a wide range of poorly defined products, including undesirable light gases and heavy tars that pose challenges for downstream processing. As an alternative, catalytic chemical recycling offers a promising route for converting waste polyolefins back into value-added hydrocarbons—such as fuels, lubricant base oils, and other chemical feedstocks—at temperatures typically below 300 °C. This opinion article focuses on two dominant pathways in heterogeneous catalysis that are used to cleave the C–C bonds of polyolefins: hydrogenolysis and hydrocracking. Both catalytic pathways have the potential to selectively convert polyolefin waste into valuable fuels and chemical feedstocks under mild conditions. Notably, while hydrocracking primarily yields branched hydrocarbon products, hydrogenolysis predominantly produces linear hydrocarbons. Here, we compare the mechanisms and catalyst designs for hydrocracking and hydrogenolysis, analyze critical technical challenges from catalyst stability to process engineering, and provide an outlook on how these complementary pathways can be used to repurpose plastic waste into valuable products.
Synthetic natural gas (SNG) is of great interest in reducing fossil energy consumption while maintaining compatibility with existing NG infrastructure and end-use applications equipment. SNG can be produced using clean H 2 generated from renewable or nuclear energy and CO 2 captured from stationary sources or the atmosphere. In this study, we develop an engineering process model of SNG production using Aspen Plus® and production scales reported by the industry. We examine the levelized cost and life cycle greenhouse gas (GHG) emissions of SNG production under various CO 2 supply scenarios. Considering the higher cost of H 2 transportation compared with CO 2 transportation, we assume that CO 2 feedstock is transported via pipeline to the H 2 production location, which is collocated with the SNG plant. We also evaluate the cost of CO 2 captured from the atmosphere, assuming the direct air capture process can occur near the SNG facility. Depending on the CO 2 supply chain, the levelized cost of SNG is estimated to be in the range of $\$45–76$ per million British thermal units (MMBtu) on a higher heating value (HHV) basis. The SNG production cost may be reduced to $\$27–57$/MMBtu-HHV by applying a tax credit available in the United States for low-carbon H 2 production (45V). With a lower electricity price of 3¢/kWh for water electrolysis and accounting for a 45V tax credit, the SNG cost reaches parity with the cost of fossil NG. Depending on the CO 2 supply chain, SNG can reduce life cycle GHG emissions by 52–88% compared with fossil NG.
Capacitors, as the basic building block of power electronics and electrical systems, are the major constraint of the increasingly integrated power systems that require new capable polymer dielectric films operating at higher temperatures >125 °C. Despite tremendous research efforts on the lab scale, there remains a considerable barrier and knowledge basis for converting polymeric films to functioning capacitors desired in actual applications. Here, the authors tackled the polyetherimide (PEI) film scale up issues and developed various engineering processes for film de-wrinkling, optimal metallization, static elimination, and capacitor fabrication improvement. The authors discovered various fabrication challenges such as the electrostatic charge, metallization scheme, winding tension, and end electrode disconnects. To fabricate a high yield of PEI capacitor bobbins, it is necessary to utilize static eliminators (radioactive ionizers), moderately thick aluminum metallization (15–30 Ω/sq), and winding tension (60–100 g). It is also effective to evaluate capacitance, dielectric loss, equivalent series resistance, and thermal cycling stability of capacitors. This work sheds some light on converting PEI films and other polymeric films to capacitors on a scale up fabrication effort. The practical learning from film handling to capacitor fabrication in this work provided the necessary knowledge for manufacturing high-temperature polar film capacitors.
Fluids under nanoconfinement control many environmental and engineering processes. In particular, nanoconfined ethane commonly occurs in hydrocarbon production and gas separation applications. While previous research has measured the pore critical properties and self-diffusivity of nanoconfined ethane, the relationship between molecular organization and phase behavior in nanoporous media persists as a knowledge gap. Through experimental adsorption measurements and molecular dynamics simulations, we report the impact of pore wall proximity, pore size, and temperature on the structure of nanoconfined ethane. Finally, we offer evidence for an energetically favorable rearrangement of ethane molecules in the adsorption layer that may contribute to more accurate gas-in-place estimates and more efficient design of gas separation membranes.
Membranes are among the most promising technologies for energy-efficient and highly selective separations, and the surface-charge property of membranes plays a critical role in their broad applications. Atomic layer deposition (ALD) can deposit materials uniformly and with high precision and controllability on arbitrarily complex and large substrates, which renders it a promising method to tune the electrostatics of water/solid interfaces. However, a systematic study of surface-charge properties of ALD-grown films in aqueous environments is still lacking. In this work, 17 ALD-grown metal-oxide films are synthesized, and a comprehensive study of their water stability, wetting properties, and surface-charge properties is provided. Furthermore, this work represents a resource guide for researchers and ultimately for materials and process engineers, seeking to tailor interfacial charge properties of membranes and other porous water treatment components.
Upon extraction/injection of a large quantity of gas from/into a subsurface system in shale gas production or carbon sequestration, the gas pressure varies remarkably, which may significantly change the wettability of porous media involved. Mechanistic understanding of such changes is critical for designing and optimizing a related subsurface engineering process. Using molecular dynamics simulations, in this work we have calculated the contact angle of a water droplet on various solid surfaces (kerogen, pyrophyllite, calcite, gibbsite, and montmorillonite) as a function of CO 2 or CH 4 gas pressure up to 200 atm at a temperature of 300 K. The calculation reveals a complex behavior of surface wettability alteration by gas pressure variation depending on surface chemistry and structure, and molecular interactions of fluid molecules with surfaces. As the CO 2 gas pressure increases, a partially hydrophilic kerogen surface becomes highly hydrophobic, while a calcite surface becomes more hydrophilic. Considering kerogen and calcite being the major components of a shale formation, we postulate that the wettability alteration of a solid surface induced by a gas pressure change may play an important role in fluid flows in shale gas production and geological carbon sequestration.
The transition to hydrogen as a green reductant in metal production is critical for decarbonizing the metallurgical industry, yet atomic-scale mechanisms governing reduction pathways and phase evolution remain unresolved. Using in-situ environmental transmission electron microscopy, we identify a hidden pathway that reveals dynamic formation of amorphous metallic iron (Fe) during the hydrogen-driven reduction of ferrous oxides of Fe 3 O 4 and FeO. Real-time imaging uncovers three coexisting transformation routes: (i) Fe 3 O 4 → FeO, (ii) Fe 3 O 4 → amorphous Fe, and (iii) FeO → amorphous Fe. The resulting amorphous Fe exhibits fluid-like mobility, enabling its rapid aggregation and crystallization into core-shell nanostructures, with a crystalline core enveloped by an amorphous shell. Complementary ab initio molecular dynamics simulations trace the amorphous Fe formation to interfacial strain at the metal/oxide interfaces, where large lattice mismatches destabilize the metal lattice during initial metallization. This interplay between thermodynamics and kinetics governs phase evolution: thermodynamics favors a self-limiting amorphous Fe overlayer, while rapid oxide reduction kinetics drives amorphous overgrowth. Our findings demonstrate that amorphous intermediates bypass rate-limiting crystalline steps, providing mechanistic insights to optimize H 2 -based processes for sustainable steelmaking. In conclusion, these insights bridge the gap between macroscopic process engineering and atomic-scale dynamics, with broader implications for catalysis and nanostructured material synthesis, where oxide reduction pathways critically shape functional phases and microstructures.
Direct air capture (DAC) of carbon dioxide is one approach among many proposed that is capable of offsetting hard-to-avoid emissions. In previous work, we developed the alkalinity concentration swing (ACS) method, which is driven through concentrating an alkaline solution that has been loaded with atmospheric CO 2 by desalination technologies, such as reverse osmosis or capacitive deionization. Though the ACS is promising in terms of energy usage and implementation, its absorption rate and water requirements are infeasible for a large-scale DAC process. Here, we propose an improvement on the ACS, the bicarbonate-enriched alkalinity concentration swing (BE-ACS), which selects bicarbonate ions from a stream of aqueous alkaline solution that has absorbed atmospheric CO 2 . The bicarbonate-rich stream is then concentrated, which greatly increases its CO 2 partial pressure, and then CO 2 is extracted from solution. We experimentally investigate the use of pressure-driven nanofiltration (NF) membrane-based separation to select bicarbonate ions over carbonate ions. We screen commercial membranes and select one high-performance membrane for detailed studies, quantifying its bicarbonate-carbonate selectivity factor and bicarbonate-passage factor. Feed pH, the combined concentration of aqueous CO 2 , bicarbonate, and carbonate species (or dissolved inorganic carbon), alkalinity, and permeation flux are systematically varied to study NF separation properties. We find that the selectivity factor, which exceeds 30 times in certain regimes, increases with higher feed pH and higher alkalinity. Lastly, the performance metrics of the selected NF membrane are input into a theoretical BE-ACS cycle analysis, and the required energy input and cycle capacity output are evaluated. Ideal cycle energy is found to be as low as around 250 kJ/mol, with opportunities identified for further decreases through process engineering and forward osmosis energy recovery.
Here the trapping of paraffins is beneficial compared to selective olefin adsorption for adsorptive olefin purification from a process engineering point of view. Here we demonstrate the use of a series of Zn 2 (X-bdc) 2 (dabco) (where X-bdc 2- is bdc 2- = 1,4-benzenedicarboxylate with substituting groups X, DM-bdc 2- = 2,5-dimethyl-1,4-benzenedicarboxylate or TM-bdc 2- = 2,3,5,6-tetramethyl-1,4-benzenedicarboxylate and dabco = diazabicyclo[2.2.2.]octane) metal-organic frameworks (MOFs) for the adsorptive removal of ethane from ethylene streams. The best performing material from this series is Zn2(TM-bdc)2(dabco) (DMOF-TM), which shows a high ethane uptake of 5.31 mmol g -1 at 110 kPa, with a good IAST selectivity of 1.88 towards ethane over ethylene. Through breakthrough measurements a high productivity of 13.1 L/kg per breakthrough is revealed with good reproducibility over five consecutive cycles. Molecular simulations show that the methyl groups of DMOF-TM are forming a van der Waals trap with the methylene groups from dabco, snuggly fitting the ethane. Further, rarely used high pressure coadsorption measurements, in pressure regimes that most scientific studies on hydrocarbon separation on MOFs ignore, reveal an increase in ethane capacity and selectivity for binary mixtures with increased pressures. The coadsorption measurements reveal good selectivity of 1.96 at 1000 kPa, which is verified also through IAST calculations up to 3000 kPa. This study overall showcases the opportunities that pore engineering by alkyl group incorporation and pressure increase offer to improve hydrocarbon separation in reticular materials.
Mineral precipitation caused by fluid mixing presents complex control and predictability challenges in a variety of natural and engineering processes, including carbon mineralization, geothermal energy, and microfluidics. Precipitation dynamics, particularly under the influence of fluid flow, remain poorly understood. Combining microfluidic experiments and three-dimensional reactive transport simulations, we demonstrate that fluid inertia controls mineral precipitation and clogging at flow intersections, even in laminar flows. We observe distinct precipitation regimes as a function of Reynolds number (Re). At low Reynolds numbers (Re < 10), precipitates form a thin, dense layer along the mixing interface, which shuts precipitation off, while at high Reynolds numbers (Re > 50), strong three-dimensional flows significantly enhance precipitation over the entire intersection, resulting in rapid clogging. When injection rates from two inlets are uneven, flow symmetry-breaking leads to unexpected flow bifurcation phenomena, which result in enhanced concurrent precipitation in both downstream channels. Finally, we extend our findings to rough channel networks and demonstrate that the identified inertial effects on precipitation at the intersection scale are also present and even more dramatic at the network scale. This study sheds light on the fundamental mechanisms underlying mixing-induced mineral precipitation and provides a framework for designing and optimizing processes involving mineral precipitation.
This work addresses how analysts of a high-valued system (e.g., nuclear reactor, aircraft turbine designs) can extract findable, accessible, interoperable, and reusable scientific data for public dissemination to artificial intelligence and machine-learning (AI/ML) researchers in a manner that cannot be reverse-engineered, potentially compromising sensitive or proprietary information. State-of-the-art methods address this problem through data masking techniques, which allow access to a subset of the information while obfuscating private and potentially identifying information (e.g., personally identifying medical data). These methods are unsuitable for industrial engineering processes, where AI/ML tools need explicit access to all the data available to draw the best inference about the system to help optimize its performance and identify its vulnerabilities, etc. Our novel deceptive infusion of data paradigm provides a solution to this conundrum by developing a mathematical approach capable of concealing the identity of the system while providing full access to all the features employed by AI/ML tools to ensure their optimal performance.
Modern information technology (IT) is a broad term with vague and somewhat arbitrary boundaries which can depend on any given novice or subject matter expert’s notional understanding. For the purposes of this article, IT can be understood as standards in communication, computational power, and storage. Additionally, impacts of modern IT extend beyond its products of hardware and software to include associated work practices and engineering processes.
Liquid metals and magnetic fields are used in many technical applications such as metallurgy, crystal growth and nuclear fusion reactors. When an electrically conducting fluid moves in a magnetic environment, electric currents and electromagnetic forces are generated that affect velocity and pressure losses in the flow. These magnetohydrodynamic (MHD) interactions have to be investigated to optimize the engineering processes. The characteristics of MHD flows depend on the geometrical configuration, the strength of the applied magnetic field, the electrical properties of fluid and structural materials and the thermal conditions. In the so-called blankets for fusion reactors, where liquid metals are used to breed the plasma fuel component tritium and to extract the generated heat, magneto-convective flows play a crucial role in determining heat and mass transfer. Therefore, the availability of numerical codes to simulate this type of flow is mandatory and their validation is a necessary step to guarantee the reliability of the results. For that reason, a benchmark problem has been defined to simulate liquid metal flows in a horizontal rectangular duct heated from below and exposed to a non-uniform magnetic field. Results obtained by five research groups using different codes are compared.