SPICE_MARS: A Process Synthesis Framework for Membrane-Assisted Reactive Separations
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One sentence should be included in this paper in the “Acknowledgement” section: “We thank Richard Celestre for his technical support of the experiment performed on BL5.3.1 at the ALS.”
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We develop an integrated strategy for the production of ethanol from lignocellulosic biomass.
Hybrid yeast strain co-produces isobutanol and ethanol at high yields. Reducing hydrolysis enzyme loading and enhancing xylose conversion greatly impact the economic potential of the biorefinery.
This project aims to develop technology that converts domestic United States (US) raw coal to high quality, high value, and marketable carbon fiber. More specifically, the project aims to significantly improve the selectivity and yield of carbon fiber produced per ton of coal over conventional coal pitch-based production by using low-severity direct coal conversion technology to maximize the yield of pitch from coal, suitable for production of carbon fiber. To meet the stated objective of developing a technology platform, capable of producing high quality, high-value and marketable carbon fiber from domestic US coal, the proposed scope of work involves testing of a low-severity direct coal liquefaction (LS-DCL) process approach. The isotropic pitch produced from the LS-DCL process will be thermally treated using conventional processes to convert to a liquid crystal or “mesophase” pitch as a precursor for structural carbon fibers. This mesophase pitch will then be melt-spun into fiber, oxygen stabilized, and carbonized using conventional processes. The resulting carbon fiber will be evaluated for mechanical properties and suitability for structural applications such as automotive parts and spars for wind turbine blades. The overall process is expected to significantly lower the cost of fiber compared to state of the art fibers produced from polyacrylonitrile (PAN) precursors.
The quantitative characterization of surface structures captured in scanning electron microscopy (SEM) images has proven to be effective for discerning provenance of an unknown nuclear material. Recently, many works have taken advantage of the powerful performance of convolutional neural networks (CNNs) to provide faster and more consistent characterization of surface structures. However, one inherent limitation of CNNs is their degradation in performance when encountering discrepancy between training and test datasets, which limits their use widely. The common discrepancy in an SEM image dataset occurs at low-level image information due to user-bias in selecting acquisition parameters and microscopes from different manufacturers. Therefore, in this study, we present a domain adaptation framework to improve robustness of CNNs against the discrepancy in low-level image information. Furthermore, our proposed approach makes use of only unlabeled test samples to adapt a pretrained model, which is more suitable for nuclear forensics application for which obtaining both training and test datasets simultaneously is a challenge due to data sensitivity. Through extensive experiments, we demonstrate that our proposed approach effectively improves the performance of a model by at least 18% when encountering domain discrepancy, and can be deployed in many CNN architectures.
As aerospace applications become ever more demanding, novel insulation materials with lower thermal conductivity, lighter weight and higher use temperature are required to fit the aerospace application needs. Having nanopores and high porosity, aerogels are superior thermal insulators, among other things. The use of silica aerogels in general is quite restricted due to their inherent fragility, hygroscopic nature, and poor mechanical properties, especially in extereme aerospace environments. Our research goal is to develop aerogels with better mechanical and environmental stability for a variety of aeronautic and space applications including space suit insulation for planetary surface missions, insulation for inflatable structures for habitats, inflatable aerodynamic decelerators for entry, descent and landing (EDL) operations, and cryotank insulation for advance space propulsion systems. Different type of aerogels including organic-inorganic polymer reinforced (hybrid) silica-based aerogels, polyimide aerogels and inorganic aluminosilicate aerogels have been developed and examined.
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Preparation, characterization, and an electrochemical study of Mg 0.1 V 2 O 5 prepared by a novel sol-gel method with no high-temperature post-processing are disclosed. Cyclic voltammetry showed the material to be quasi-reversible, with improved kinetics in an acetonitrile-, relative to a carbonate-, based electrolyte. Galvanostatic test data under a C/10 discharge showed a delivered capacity >250 mAh/g over several cycles. Based on these results, a magnesium anode battery, as disclosed, would yield an average operating voltage ˜3.2 Volts with an energy density ˜800 mWh/g for the cathode material, making the newly synthesized material a viable cathode material for secondary magnesium batteries.
Synthesis is a proposed systematic process for rapidly creating different members of a program family. Family members are described by variations in their requirements. Requirements variations are mapped to variations on a standard design to generate production quality code and documentation. The approach is made feasible by using principles underlying design for change. Synthesis incorporates ideas from rapid prototyping, application generators, and domain analysis. The goals of Synthesis and the Synthesis process are discussed. The technology needed and the feasibility of the approach are also briefly discussed. The status of current efforts to implement Synthesis methodologies is presented.
Process intensification (PI) has been gaining increasing momentum in the chemical engineering research community and the chemical/energy industry. While many PI alternative technologies and their conventional counterparts exist, systematic approaches and tools to decide on the most promising intensified process solutions are currently rather lacking. Process Systems Engineering (PSE) can contribute uniquely to this challenge by leveraging process synthesis, design, analysis, and optimization tools. However, key open questions in this area still remain, including: (i) how to efficiently address the combinatorial design space and systematically deliver intensified designs, and (ii) how to ensure the operablility performances of the derived intensified structures at an early design stage. Here, this paper provides a brief review of recent progress towards the synthesis of operable process intensification systems, highlights major challenges, and discusses future research opportunities.
Advances in both the underlying theory and in the practical implementation of molecular modeling techniques have increased their value in the advanced materials development process. The objective is to accelerate the maturation of emerging materials by tightly integrating modeling with the other critical processes: synthesis, processing, and characterization. The aims of this report are to summarize the state of the art of existing modeling tools and to highlight a number of areas in which additional development is required. In an effort to maintain focus and limit length, this survey is restricted to classical simulation techniques including molecular dynamics and Monte Carlo simulations.