An advanced passive diffusion sampler for the determination of dissolved gas concentrations

2009 ◽  
Vol 45 (6) ◽  
Author(s):  
P. Gardner ◽  
D. K. Solomon
Keyword(s):  
Passive Diffusion ◽  
Dissolved Gas ◽  
Diffusion Sampler

scholarly journals Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chun Yan ◽  
Meixuan Li ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Determination ◽  
Oil Analysis ◽  
Dissolved Gas ◽  
Adaboost Algorithm ◽  
Diagnosis Model ◽  
The Stability ◽  
Transformer Fault

Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.

Determination of uptake rate of sensitive diffusion sampler for formaldehyde in air

2004 ◽  
Vol 238 (1-4) ◽  
pp. 14-17 ◽  
Author(s):  
Yoshika Sekine ◽  
Daisuke Oikawa ◽  
Michio Butsugan
Keyword(s):  
Uptake Rate ◽  
Formaldehyde In Air ◽  
Diffusion Sampler

A Passive Diffusion Sampler for HT- and HTO-in-Air

2005 ◽  
Vol 48 (1) ◽  
pp. 393-396
Author(s):  
R. A. Surette ◽  
J. C. Nunes
Keyword(s):  
Passive Diffusion ◽  
Diffusion Sampler

Oil formation volume factor modeling: Traditional vs. Stochastically optimized neural networks

2013 ◽  
Vol 5 (4) ◽  
Author(s):  
Parisa Bagheripour ◽  
Mojtaba Asoodeh ◽  
Ali Asoodeh
Keyword(s):  
Neural Network ◽  
Pattern Search ◽  
Model Assessment ◽  
Local Minima ◽  
Dissolved Gas ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Data Points ◽  
Oil Formation

AbstractOil formation volume factor (FVF) is considered as relative change in oil volume between reservoir condition and standard surface condition. FVF, always greater than one, is dominated by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. In addition to limitations on reliable sampling, experimental determination of FVF is associated with high costs and time-consumption. Therefore, this study proposes a novel approach based on hybrid genetic algorithm-pattern search (GA-PS) optimized neural network (NN) for fast, accurate, and cheap determination of oil FVF from available measured pressure-volume-temperature (PVT) data. Contrasting to traditional neural network which is in danger of sticking in local minima, GA-PS optimized NN is in charge of escaping from local minima and converging to global minimum. A group of 342 data points were used for model construction and a group of 219 data points were employed for model assessment. Results indicated superiority of GA-PS optimized NN to traditional NN. Oil FVF values, determined by GA-PS optimized NN were in good agreement with reality.

Developing a new, passive diffusion sampler suite to detect helium anomalies associated with volcanic unrest

2015 ◽  
Vol 77 (3) ◽  
Author(s):  
Brittany E. Dame ◽  
D. Kip Solomon ◽  
William C. Evans ◽  
Steven E. Ingebritsen
Keyword(s):  
Passive Diffusion ◽  
Volcanic Unrest ◽  
Diffusion Sampler

Determination of catecholamine permeability coefficients for passive diffusion across phospholipid vesicle membranes

1981 ◽  
Vol 60 (1) ◽  
pp. 73-76 ◽  
Author(s):  
Amy Bochain ◽  
Lorrie Estey ◽  
Grace Haronian ◽  
Michael Reale ◽  
Camilo Rojas ◽  
...  
Keyword(s):  
Passive Diffusion ◽  
Phospholipid Vesicle ◽  
Permeability Coefficients ◽  
Vesicle Membranes
Sign in / Sign up

Export Citation Format

Share Document