You search for stuff every day, sometimes all day. And sometimes, one of your
searches is so productive that you want to do it again.
For example, let’s say you perform the following Google® Scholar search, and
you like what you see …
extractive
desulfurization of model fuel
On the results page, in the left hand column (at least, that’s where I find it
in Internet Explorer on my laptop) is a link labeled Create alert. Click on the link to create
your alert. It’s that simple.
TIP: Be selective! It is so easy to create Google alerts,
that you can easily find yourself wading through wads of search results that
show up in your inbox every day. That has happened to me. I recommend pruning
your alerts from time to time, as your interests change and your inbox fills
up.
Here are examples of items I recently found in my inbox, resulting from two of
my Google® Scholar alerts …
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Recent Results from Google® Scholar alerts
using the following two search statements:
Aramco
"King Abdullah University" of Science and Technology
Development of modified siloxane membrane
materials for enhanced NGL recovery from
natural gas
J. Yang* 1, D. Harrigan 1, J. Vaughn 1, M. Vaidya 2, V. Tammana 2
1 Aramco Services Company, USA, 2 Saudi Aramco, Saudi Arabia
Natural gas is of strategic interest to Saudi Aramco, including to reduce the
Kingdom’s reliance
on liquid fuel for power generation and to provide for further economic growth
and
diversification. However, many of the Kingdom’s natural gas reservoirs contain
a complex gas
mixture (e.g. higher value hydrocarbons, acidic gases, inert gases, and trace
components of
many other compounds). The higher hydrocarbon (C3+) removal from natural gas is
required to
reduce the dew point and heating value of natural gas to pipeline
specifications, prevent
condensation during transportation, and recover valuable C3+ hydrocarbons for
use as chemical
feedstock.
Presently, Saudi Aramco is interested in developing new high flux and selective
polymeric
membranes to remove C3+ hydrocarbon from raw natural gas. Silicone polymers, in
particular
poly(dimethylsiloxane) (PDMS), have received considerable attention for this
application
because they are cheaper, readily available, and have high intrinsic
permeability to gases1-2.
Although commercial PDMS membranes can achieve C3+/methane separation in real
gas
streams, improved gas selectivity while maintaining productivity would enhance
profitability. In
this talk, a series of modified siloxane rubbery membrane materials that
display higher
permeability and C3+/methane selectivity under single and or/
industrially-relevant feed streams
will be discussed, as compared to commercial PDMS membranes. The effect of
polymer
structures and crosslinking and their impacts on membrane physical properties,
permeation
performance, and membrane stability will be presented. Ultimately, this work
will enable the
rapid development of novel rubbery membrane materials for enhanced C3+
hydrocarbon
removal from natural gas.
References:
1. Freeman, B. D. et al, “Separation of gases using solubility-selective
polymers”, Trends
Polym. Sci., 5, 167-173 (1997).
2. Bake, R. W., “Membrane technology and applications”, 2nd Ed, John Wiley
& Sons, New
York, 2004
3. Schultz, J. et al, “Membrane for separation of higher hydrocarbons from
methane”, J.
Membr. Sci., 110, 37-45 (1996)
Keywords: Hydrocarbon removal, Rubbery membranes, Gas separation, Siloxane
source: https://elsevier.conference-services.net/viewsecurePDF.asp?conferenceID=4142&abstractID=972536
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Computer-Aided Gasoline Compositional Model Development Based on GC-FID
Analysis
Chen Cui†‡, Linzhou Zhang*† , Yongjian Ma†, Triveni Billa‡, Zhen Hou‡, Quan
Shi† , Suoqi Zhao† , Chunming Xu†, and Michael T. Klein*‡§
† State Key Laboratory of Heavy Oil Processing, China University of Petroleum,
Beijing 102249, People’s Republic of China
‡ Department of Chemical and Biomolecular Engineering, University of Delaware,
Newark, Delaware 19716, United States
§ Center for Refining and Petrochemicals, King Fahd University of Petroleum and
Minerals, Dhahran 31261, Saudi Arabia
Energy Fuels, Article ASAP
DOI: 10.1021/acs.energyfuels.8b01953
Publication Date (Web): July 24, 2018
Copyright © 2018 American Chemical Society
*E-mail: lzz@cup.edu.cn , *E-mail: mtk@udel.edu
Cite this:Energy Fuels XXXX, XXX,
XXX-XXX
Abstract
The demand for improved gasoline product quality has helped make
molecular-level models become more preferred for the modern refinery. Building
the molecular compositional model is an essential first step for this
quantitative molecular management of gasoline streams. Gas chromatography
equipped with flame ion detection (GC-FID) is commonly used in the gasoline
detailed hydrocarbon analysis (DHA). The combination of GC-FID analysis and
molecular-level modeling is thus very attractive. In the present study, we
developed a gasoline compositional model based solely on GC-FID as input. To
suppress the negative influence of peak coelution, we developed a
statistics-based peak tuning algorithm to obtain individual compound resolution
at higher carbon number range. Using the tuned result as input, the
molecular-level gasoline compositional model was built by estimating the
quantitative percentages of the species in a predefined molecular library (573
molecules). The molecular-level compositional model has good extensibility and
can link to the molecule-based physical properties prediction model. The model
has been verified via applications on various gasoline samples. The prediction
of research octane number for large-scale gasoline samples was also revealed.
source: https://pubs.acs.org/doi/abs/10.1021/acs.energyfuels.8b01953
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DeepSimulator: a deep Nanopore sequencing simulator
Yu Li 1, Renmin Han 1, Chongwei Bi 2, Mo Li 2, Sheng Wang 1*, Xin Gao 1*
1 Computational Bioscience Research Center (CBRC),
2 Biological and Environmental Sciences and Engineering (BESE) Division,
King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900,
Saudi Arabia
* realbigws@gmail.com or xin.gao@kaust.edu.sa
Abstract
Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing
technology in recent years. To keep pace with the explosion of the downstream
data analytical tools, a versatile Nanopore sequencing simulator is needed to
complement the experimental data as well as to benchmark those newly developed
tools. However, all the currently available simulators are based on simple
statistics of the produced reads, which have difficulty in capturing the
complex nature of the Nanopore sequencing procedure, the main task of which is
the generation of raw electrical current signals.
Results: Here we propose a deep learning based simulator, DeepSimulator, to
mimic the entire pipeline of Nanopore sequencing. Starting from a given
reference genome or assembled contigs, we simulate the electrical current
signals by a context-dependent deep learning model, followed by a base-calling
procedure to yield simulated reads. This workflow mimics the sequencing
procedure more naturally. The thorough experiments performed across four
species show that the signals generated by our context-dependent model are more
similar to the experimentally obtained signals than the ones generated by the
official context-independent pore model. In terms of the simulated reads, we
provide a parameter interface to users so that they can obtain the reads with
different accuracies ranging from 83\% to 97\%. The reads generated by the
default parameter have almost the same properties as the real data. Two case
studies demonstrate the application of DeepSimulator to benefit the development
of tools in de novo assembly and in low coverage SNP detection.
Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator
source: https://www.ijcai-boom.org/uploads/5/1/6/8/51680821/sheng_wang.pdf
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Jean Steinhardt served as Librarian,
Aramco Services, Engineering Division, for 13 years. He now heads Jean
Steinhardt Consulting LLC, producing the same high quality research that he
performed for Aramco.
Follow Jean’s blog at: http://desulf.blogspot.com/ for continuing tips on effective online
research
Email Jean at research@jeansteinhardtconsulting.com with questions on research, training, or
anything else
Visit Jean’s Web site at http://www.jeansteinhardtconsulting.com/ to see examples of the services we can
provide
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