“If you pick up a starving dog and make him prosperous, he will not bite you; that is the principal difference between a dog and a man.” -- Mark Twain (American Humorist, Writer and Lecturer. 1835-1910)
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This post is being composed while I am on the road, so it may have some rough edges. The basic point is to be aware of the subject categories used by each of your online sources, and to use them to your advantage.
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Three points:
1) Database overlap … for comprehensive results, search multiple sources
2) Subject field … experts have assigned subject tags to each item … use their tags to focus your search results
3) vive la différence … every database has its own set of subject categories
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Google Scholar Subject Collections
Biology, Life Sciences, and Environmental Science
Medicine, Pharmacology, and Veterinary Science
Business, Administration, Finance, and Economics
Physics, Astronomy, and Planetary Science
Chemistry and Materials Science Social Sciences, Arts, and Humanities
Engineering, Computer Science, and Mathematics
EXAMPLE:
Google Scholar Search String: aromatic heterocyclic sulfur
Google Scholar Subject Collection: Engineering, Computer Science, and Mathematics
ONE RESULT:
Paper Number 69500-MS
DOI What's this? 10.2118/69500-MS
Title Artificial Neural Networks Applied to the Operation of VGO Hydrotreaters
Authors R. Lopez, J.R. Perez, C.G. Dassori, A. Ranson, PDVSA Intevep
Source SPE Latin American and Caribbean Petroleum Engineering Conference, 25-28 March 2001, Buenos Aires, Argentina
Copyright 2001,. Society of Petroleum Engineers Inc.
Language English
Preview Abstract
Introduction
Nowadays several new mathematical techniques have been implemented for process modeling and advanced process control in refineries, chemical plants, and manufacturing industries. Artificial neural network applications are among these new technologies.
If a control system simulates a process just following the principle of minimizing the deviation against the desired objective function value, reduce the process energy consumption, or increasing the production level, in all these cases, the process profit must be increased. If the simulation software tool can predict operating failures or by the same token, generate a failure diagnosis procedure, one thing is for sure, there will be an improvement over the process operational capacity.
Artificial neural network is a system loosely modeled on the human brain. The field goes by many names, such as connectionism, parallel distributed processing, neuro-computing, natural intelligent systems, machine learning algorithms, and artificial neural networks1-3. It is an attempt to simulate within specialized hardware or sophisticated software, the multiple layers of simple processing elements called neurons. Each neuron is linked to certain of its neighbors with varying coefficients of connectivity that represent the strengths of these connections. Learning is accomplished by adjusting these strengths to cause the overall network to output appropriate results1,3.
This paper reports the practical application of artificial neural networks to the simulation of a vacuum gas oil hydrotreater process operating variables, feedstock and product quality properties. Furthermore, several ANNs models were proposed for:
1.Petroleum fractions composition
2.Quality properties predictions
3.Simulation of process operating variables
These models were trained with data bases built from the VGO hydrotreater data acquisition system. Moreover, the ANN model performance was compared with conventional quality property predictor models available in the technical literature.
Number of Pages 10
File Size 158 KB
Price SPE Member Price: US $ 7.50
SPE NonMember Price: US $ 25.00
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ScienceDirect Subject Categories
Agricultural and Biological Sciences
Arts and Humanities
Biochemistry, Genetics and Molecular Biology
Business, Management and Accounting
Chemical Engineering
Chemistry
Computer Science
Decision Sciences
Earth and Planetary Sciences
Economics, Econometrics and Finance
Energy
Engineering
Environmental Science
Immunology and Microbiology
Materials Science
Mathematics
Medicine and Dentistry
Neuroscience
Nursing and Health Professions
Pharmacology, Toxicology and Pharmaceutical Science
Physics and Astronomy
Psychology
Social Sciences
Veterinary Science and Veterinary Medicine
EXAMPLE:
ScienceDirect Search String: aromatic AND heterocyclic AND sulfur
ScienceDirect Subject: Engineering
ONE RESULT:
Applied Energy
Volume 87, Issue 4, April 2010, Pages 1269-1272
Oil shale pyrolysis kinetics and variable activation energy principle
Omar S. Al-Ayeda, M. Matouqa, b, Z. Anbara, b, Adnan M. Khaleela, b and Eyad Abu-Namehb
osalayed@fet.edu.jo
a Faculty of Engineering Technology, Department of Chemical Engineering, P.O. Box 15008, Marka 11134, Jordan
b Department of Basic Sciences, Prince Abdullah Bin-Ghazi of Science and Information, Al-Balqa Applied University, Jordan
Abstract
A modified first order kinetic equation with variable activation energy is employed to model the total weight loss of Ellajjun oil shale samples. Fixed bed retort with 400 g of oil shale sample size is used in this study in 350–550 °C temperature range. Variable heating rate, h, in the range 2.6–5 °C min−1 are tested.
Activation energy was allowed to vary as a function of oil shale conversion. The value of the activation energy increased from 98 to 120 kJ mol−1 while the corresponding frequency factor changed from 9.51 × 105 to 1.16 × 106. Fischer Assay analysis of the studied samples indicated 12.2 wt.% oil content. The oil shale decomposition ranged from 3.2% to 28.0%. The obtained kinetic data are modeled using variable heating rate, pyrolysis temperature and variable activation energy principle in a nitrogen sweeping medium. Good fit to the obtained experimental data is achieved.
Keywords: Variable activation energy; Oil shale; Variable heating rate; Pyrolysis kinetics
source: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V1T-4WS8600-2&_user=10&_coverDate=04%2F30%2F2010&_alid=1419175102&_rdoc=13&_fmt=high&_orig=search&_cdi=5683&_st=5&_docanchor=&_ct=14&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=2b65ccd4c46f84cd44279b1a7631ff59
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EbscoHost Subject Terms
A Small Example: Engineering
Engineering design
Engineering drawing
Engineering inspections
Engineering instrument
Engineering libraries
Engineering mathematics
Engineering meteorology
Engineering model
EXAMPLE
EbscoHost Academic Select Search String: sulfur
EbscoHost Academic Select Subject: Engineering
ONE RESULT:
Sulfur Recovery Units: Adaptive Simulation and Model Validation on an Industrial Plant.Citation Only Available By: Stefano Signor; Flavio Manenti; Maria Grazia Grottoli; Paolo Fabbri; Sauro Pierucci. Industrial & Engineering Chemistry Research, Jun2010, Vol. 49 Issue 12, p5714-5724, 11p; (AN 51410231)
Subjects: SULFUR; INDUSTRIAL engineering; ADAPTIVE control systems; FURNACES; CATALYSIS; HEURISTIC algorithms; PARAMETER estimation; SIMULATION methods; Industrial Process Furnace and Oven Manufacturing
Database: Academic Search Complete
The paper is aimed at discussing and fixing issues in providing a generalized approach to the simulation of sulfur recovery units (SRUs). The main goal is to get a simulation that is at the same time (i) reasonably detailed and robust to properly characterize SRUs and (ii) so generalized to provide a tool that is not only specific for the case in study. To achieve point (i), standard libraries belonging to commercial process simulators are coupled to specific heuristic relations coming from the industrial experience for modeling the thermal furnace and the catalytic Claus converters; this allows us to infer with a certain reliability those measures that are often missing or unavailable online in these processes. To achieve point (ii), a series of adaptive parameters are filled in the process simulation by making it more flexible and yet preserving all model details. The most recent techniques and numerical methods, to tune the adaptive simulation parameters, are implemented in Visual C and interfaced to PRO/II (by SimSci-Esscor) to obtain a robust parameter estimation solved by means of the BzzMath library. At last, the detailed and tuned adaptive simulation is validated along a period of 2 months on a large-scale SRU (TECHNIP-KTI SpA technology) operating in Italy. [ABSTRACT FROM AUTHOR]
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