Prediction of molecular-type analysis of petroleum fractions and coal liquids

1986 ◽  
Vol 25 (4) ◽  
pp. 1009-1015 ◽  
Author(s):  
Mohammad R. Riazi ◽  
Thomas E. Daubert
Keyword(s):  
Molecular Type ◽  
Type Analysis ◽  
Petroleum Fractions ◽  
Coal Liquids

Novel equations for quantitative hydrocarbon-type analysis of petroleum fractions

Fuel ◽  
1990 ◽  
Vol 69 (3) ◽  
pp. 340-343 ◽  
Author(s):  
C.A. Nwadinigwe ◽  
K.A. Okoroji
Keyword(s):  
Type Analysis ◽  
Petroleum Fractions

Characterisation of heavy petroleum fractions using modified molecular-type homologous series (MTHS) representation

Energy ◽  
2008 ◽  
Vol 33 (6) ◽  
pp. 974-987 ◽  
Author(s):  
J GOMEZPRADO ◽  
N ZHANG ◽  
C THEODOROPOULOS
Keyword(s):  
Homologous Series ◽  
Molecular Type ◽  
Heavy Petroleum ◽  
Petroleum Fractions

Structure and composition of metal particles in Pt-Sn/Al2O3 bimetallic catalysts

1990 ◽  
Vol 48 (4) ◽  
pp. 278-279
Author(s):  
A. Sachdev ◽  
J. Schwank
Keyword(s):  
Bimetallic Catalysts ◽  
Bimetallic Catalyst ◽  
Size Structure ◽  
High Surface ◽  
High Surface Area ◽  
Metal Particles ◽  
Alloy Formation ◽  
Particle Size Range ◽  
Oxide Supports ◽  
Petroleum Fractions

Platinum - tin bimetallic catalysts have been primarily utilized in the chemical industry in the catalytic reforming of petroleum fractions. In this process the naphtha feedstock is converted to hydrocarbons with higher octane numbers and high anti-knock qualities. Most of these catalysts contain small metal particles or crystallites supported on high surface area insulating oxide supports. The determination of the structure and composition of these particles is crucial to the understanding of the catalytic behavior. In a bimetallic catalyst it is important to know how the two metals are distributed within the particle size range and in what way the addition of a second metal affects the size, structure and composition of the metal particles. An added complication in the Pt-Sn system is the possibility of alloy formation between the two elements for all atomic ratios.

CHARGE ORDERING IN 1-D MOLECULAR TYPE CRYSTALS

1983 ◽  
Vol 44 (C3) ◽  
pp. C3-1561-C3-1564
Author(s):  
D. Feinberg ◽  
J. Ranninger
Keyword(s):  
Charge Ordering ◽  
Molecular Type

Study of hydrogenation of petroleum asphaltenes

1981 ◽  
Vol 46 (2) ◽  
pp. 409-418 ◽  
Author(s):  
Gustav Šebor ◽  
Salvador Reynoso ◽  
Milan Hájek ◽  
Otto Weisser ◽  
Jiří Mostecký
Keyword(s):  
Temperature Region ◽  
Catalytic Hydrogenation ◽  
Starting Material ◽  
Type Analysis ◽  
H Nmr ◽  
Petroleum Asphaltenes

Isolated petroleum asphaltenes were subjected to model thermal and catalytic hydrogenation experiments in the temperature region 300-450° C. The course of hydrogenation of the asphaltenes studied is discussed based on 1H-NMR type analysis of the starting material and the hydrogenation products.

scholarly journals Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

2020 ◽  
Vol 10 (7) ◽  
pp. 463 ◽  
Author(s):  
Muhaddisa Barat Ali ◽  
Irene Yu-Hua Gu ◽  
Mitchel S. Berger ◽  
Johan Pallud ◽  
Derek Southwell ◽  
...  
Keyword(s):  
Molecular Subtypes ◽  
Negative Impact ◽  
Molecular Classification ◽  
Tumor Segmentation ◽  
Test Accuracy ◽  
Low Grade ◽  
Idh Mutation ◽  
Molecular Type ◽  
Low Grade Gliomas ◽  
Domain Mapping

Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document