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Petroleum Processing Efficiency Improvement
A series of volatile crude oils was characterized using the Asphaltene Determinator on-column precipitation and re-dissolution method developed at Western Research Institute (WRI). Gravimetric asphaltenes and polars fractions from silica gel chromatography separation of the oils were characterized also. A study to define the differences in composition of asphaltenes in refinery desalter rag layer emulsions and the corresponding feed and desalter oils was conducted. Results indicate that the most polar and pericondensed aromatic material in the asphaltenes is enriched in the emulsions. The wax types and carbon number distributions in the two heptane-eluting fractions from the Waxphaltene Determinator separation were characterized by repetitive collection of the fractions followed by high temperature gas chromatography (GC) and Fourier transform infrared spectroscopy (FTIR). High resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICRMS) was conducted by researchers at the Florida State University National High Magnetic Field laboratory in a no-cost collaboration with the study.
Reconstruction of sparse, nanoscale metrology data for efficient process control.
Abstract not provided.
Enterprise Software for Interactive and Super Efficient Processing of Large Multi-Dimensional Datasets
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High flux Ti nanofiltration membrane for efficient processing of bioproducts.
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Efficient Process for the Production of High Conductivity, Carbon-rich Materials from Coal
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High efficiency process and high protein feed co-product
A process is disclosed for producing a feed co-product by ethanol fermentation of plant matter comprising starch and another polysaccharide selected from the group consisting of hemicellulose and cellulose. The process comprises: (i) carrying out a primary fermentation process, distilling the primary fermentation mixture to form a primary feed co-product derived from the fermentation of the primary fermentation mixture and a primary distillate product comprising ethanol and (ii) carrying out a secondary fermentation process and distilling the secondary fermentation mixture to form the modified feed co-product and a secondary distillate product comprising ethanol.
Computatationally-Redundant Energy-Efficient Processing for Y'all (CREEPY).
Abstract not provided.
Efficient Process for the Production of High Conductivity, Carbon-rich Materials from Coal
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Efficient Process for the Production of High Conductivity, Carbon-rich Materials from Coal
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DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING
Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.
2025 ASMS Investigation of the Collision-Induced Dissociation Mechanism of Protonated TODGA with IRIS
Title (20 words): Investigation of the Collision-Induced Dissociation Mechanism of Protonated TODGA with IRIS Introduction (120 words): One of the challenges facing wide-spread adoption of nuclear power is the development of efficient separation processes for used nuclear fuel. The molecules in separation processes are subjected to an extreme environment due to the high radiation fields from the used fuel and highly acidic media used for fuel dissolution, which results in significant molecular degradation, leading to reduced process efficiency. These degradation products must be identified and studied so mitigation strategies can be developed to maintain process efficiency. However, complex systems can have many degradation products, complicating identification. Untargeted analysis tools could be used to understand radiation chemistry in complex systems. However, this would necessitate improved understanding of the gas-phase fragmentation mechanisms of fuel cycle molecules like tetraoctyldiglycolamide (TODGA). Methods (120 words): The gas-phase fragmentation of protonated TODGA was investigated using collision-induced dissociation (CID), resonance ejection, and infrared ion spectroscopy (IRIS). CID and resonance ejection experiments were conducted using a Bruker Daltonics (Bremen, Gemany) SolariX XR fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer. IRIS spectra of protonated TODGA and its two CID fragmentation products were measured using a modified Bruker amaZon Speed ETD 3D quadrupole ion trap mass spectrometer coupled to the Free Electron Lasers for Infrared eXperiments (FELIX) free electron laser. Measured spectra were compared with density functional theory (DFT) calculations using the Gaussian 16, Revision C.02 software package with the ?B97X-D functional and def2-TZVPP basis sets. Candidate structures were generated using the CREST 3.0 conformational sampling software tool. Preliminary Data (300 words): Collision-induced dissociation of protonated TODGA ([C36H73N2O3]+, m/z=581.562) results two fragment ions, one at m/z=340.285 assigned as [C20H38NO3]+ and the other at m/z=312.290, assigned as [C19H38NO2]+. Based on the assigned formula and the structure of protonated TODGA, the fragment at m/z=340.285 is likely formed from elimination of neutral dioctylamine. Comparison of the IRIS spectrum of m/z=340.285 with DFT predictions suggests it contains a ring structure, and is assigned as N-octyl-N-(6-oxo-1,4-dioxan-2-ylidene)octan-1-aminium. Based on this structure and the structure of protonated TODGA, we hypothesize this fragment formed from elimination of neutral dioctylamine followed by a ring closure mechanism. Comparison of the IRIS spectrum of the fragment at m/z=312.290 with DFT predictions also indicated the presence of a ring structure, assigned as N-(1,3-dioxolan-4-ylidene)-N-octyloctan-1-aminium. This product could be formed from elimination of carbon monoxide from the ring of m/z=340.285 as a sequential fragmentation or formed directly from protonated TODGA via elimination of neutral N,N-dioctylformamide followed by a ring closure. Resonance ejection experiments where m/z=340 was continuously ejected from the IRC cell showed no decrease in intensity of m/z=312.290 across several collision energies, suggesting that the later, direct formation mechanism, dominates. The location of the ionizing proton in protonated TODGA is important for modeling the fragmentation mechanisms. DFT calculations suggested that the position of bands involving the coupled vibrations of the amide C—N and C=O bonds in TODGA are the most sensitive to proton location. Evaluation of the IRIS spectrum of protonated TODGA suggests that the ionizing proton is located between the two amid oxygens. This protonation location was calculated to lie approximately 30 kJ/mol lower in energy than the next lowest energy location, with the proton located solely on one of the amide oxygens. Novel aspect (20 words): Infrared ion spectroscopy combined with resonance ejection experiments and density functional theory to probe the collision-induced dissociation mechanism of tetraoctyldiglycolamide.
Real-Time Measurement of Machine Efficiency during Inertia Friction Welding.
Process efficiency is a crucial parameter for inertia friction welding (IFW) that is largely unknown at the present time. A new method has been developed to determine the transient profile of the IFW process efficiency by comparing the workpiece torque used to heat and deform the joint region to the total torque. Particularly, the former is measured by a torque load cell attached to the non-rotating workpiece while the latter is calculated from the deceleration rate of flywheel rotation. The experimentally-measured process efficiency for IFW of AISI 1018 steel rods is validated independently by the upset length estimated from an analytical equation of heat balance and the flash profile calculated from a finite element based thermal stress model. The transient behaviors of torque and efficiency during IFW are discussed based on the energy loss to machine bearings and the bond formation at the joint interface.
High efficiency PEM water electrolysis: enabled by advanced catalysts, membranes, and processes
Not Available
Efficient implementation of effective core potential integrals and gradients on graphical processing units
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Computational Fluid Dynamics Simulations to Support Efficiency Improvements in Aluminum Smelting Process
Smelting is broadly described as the extraction of a metal from its ore. In the United States, aluminum is commonly produced by smelting alumina in bauxite using the Hall-Héroult process. Optimization of equipment and processes in conventional smelting is crucial to enhancing process efficiency and productivity, is necessary for improving the techno-economic feasibility, which directly manifests as the growth of the American economy. To achieve optima, insightful data on the multiphysics phenomena that are inherent to the process must be obtained through physical investigation or high-fidelity numerical simulations. The resolution of relevant scales in time and space for smelting operations requires intensive, high-performance computing (HPC) simulations. Hostile operating conditions limit physical data acquisition to specific techniques; therefore, these data do not describe the multiscale interaction of simultaneous effects. Fortunately, in recent decades, significant advancements in computing hardware and computational methods have made the numerical resolution of such a complex process possible. In this study, a high-fidelity simulation of aluminum smelting was performed using an open-source tool, OpenFOAM, which analyzed many parameters characteristic to underlying phenomena. A multiphysics model based on the Eulerian-Eulerian multifluid approach was adopted. This model can resolve critical issues in the electrolytic smelting of aluminum, such as bubbling of carbon dioxide from the anode(s), magnetohydrodynamics from electromagnetic effects, ionic dissolution of the alumina in the electrolyte, and the evolution of thermal profiles. This study provides valuable connectivity for characteristic data that can direct the future designs of efficient smelters. A basic framework to model and simulate the smelting process using OpenFOAM is presented for user modification in keeping with process development. Of relevance to the flow field, a detailed investigation of vortices produced by bubble motion and electromagnetics is discussed, along with their impact on the evolution of thermal profiles. The predictions show small-scale vortices in the clearance between the anode and cathode caused by magnetic forces. Predictions also indicate relatively large-scale vortices in the inter-anode space resulting from carbon dioxide rising through the electrolytic flow field. The formation of vortices at the edges of anodes was shown to direct alumina charged by the feeder to the bottom of the anodes, thus preventing the entrapment of gas bubbles in the periphery of the bottom of the anode. Symmetry was observed in the location of cold spots in the electrolytic mixture in the vicinity of the feeder. Cold spots were also observed in the clearance between the anode and cathode due to the flow’s transmission of unconverted alumina to this region.
Lower bound for the neutrino magnetic moment from kick velocities induced at the birth of neutron stars
We show that the neutrino chirality flip, which can take place in the core of a neutron star at birth, is an efficient process to allow neutrinos to anisotropically escape, thus providing a to induce the neutron star kick velocities. The process is not subject to the no-go theorem since although the flip from left- to right-handed neutrinos happens at equilibrium, the reverse process does not take place given that right-handed neutrinos do not interact with matter and therefore detailed balance is lost. For simplicity, we model the neutron star core as being made of strange quark matter. We find that the process is efficient when the neutrino magnetic moment is not smaller than [Formula: see text], where [Formula: see text] is the Bohr magneton. When this lower bound is combined with the most stringent upper bound, which uses the luminosity data obtained from the analysis of SN 1987A, our results set a range for the neutrino magnetic moment given by [Formula: see text]. The obtained kick velocities for natal conditions are consistent with the observed ones and span the correct range of radii for typical magnetic field intensities.