Adsorption of radioactive iodine and krypton from off-gas stream using continuous flow adsorption column
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Fast pyrolysis of lignocellulosic biomass yields little sugar or anhydrosugars compared to pyrolysis of pure polysaccharides because naturally abundant alkali and alkaline earth metals (AAEM) in biomass catalyze the fragmentation of pyranose and furanose rings. Sugar yields can be increased dramatically by pretreating the biomass with sulfuric acid prior to pyrolysis, which passivates the catalytic activity of the metals by converting them into thermally stable salts. However, depolymerization of lignin in biomass also depends on the catalytic activity of AAEM. Thus, passivating AAEM has the unintended consequence of slowing the rate of depolymerization and volatilization of lignin, resulting in a transient melt phase that agglomerates and can foul pyrolysis reactors. Here, to overcome this problem, various non-alkali metal sulfates were tested as replacements for sulfuric acid. Iron in the form of ferrous sulfate proved the most effective in depolymerizing lignin without fragmenting pyranose rings. Conventional nitrogen-blown pyrolysis of ferrous sulfate pretreated corn stover achieved WHSV of 4 h –1 compared to only 0.6 h –1 for acid pretreated corn stover. Autothermal (air-blown) pyrolysis of ferrous sulfate pretreated corn stover showed even more dramatic improvement, increasing WHSV from 1 h –1 to 10 h –1 compared to acid pretreated corn stover under autothermal operation. Fermentable sugar yields from the pyrolysis of corn stover increased from 0.9 wt% to 11.8 wt% on a biomass basis, a 13-fold increase as a result of the ferrous sulfate pretreatment. These advantages combine to increase volumetric sugar productivity from 62 g L –1 h –1 for conventional pyrolysis of untreated corn stover to 2041 g L –1 h –1 for autothermal pyrolysis of ferrous sulfate treated corn stover.
his study describes the first direct in-situ 3-D observation of a steady-state melting process by imaging a laboratory-scale slurry-fed glass melter in operation by x-ray computed tomography. Features of the reacting glass-feed, the foam layer underneath, and cavities in the glass melt pool are reconstructed in three-dimensional images. A slurry pool formed in a deep central caldera of dense dried feed, which penetrated into the glass melt. Slurry overflow from the caldera led to fast-dried and highly porous feed structure. A thin layer of foam separated the caldera from the melt. Bubbles ?5-15 mm in diameter were seen to grow beneath the reacting feed and move through the melt to escape at the edge. Pore morphology is benchmarked against computed tomography scans of a pellet of reacting simulated waste glass feed, and evolved gas analysis describes the gases generated as a function of temperature. Cooling artifacts are imaged and compared to previous studies of quenched cold caps. Detailed understanding of processes occurring during the conversion process in and below the reacting feed layer is necessary for the development of representative models of the melting process.
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During high-speed X-ray imaging of laser surface polishing experiments of specimens of 316L stainless steel at Argonne National Lab's Advanced Photon Source, it was discovered that the induced keyhole changes shape and dimensions while crossing an engineered surface feature without altering process parameters. It was observed that the post-surface feature keyhole was deeper than that of the pre surface feature keyhole. Here, this work reports on the first in-situ observation of the effect of localized surface geometry on underlying melt pool behavior. This has implications for defect formation mechanisms during laser melting processes that rely on melt pool geometry.
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Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.
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