Solubility study of different gases in mineral and ester-based transformer oils

2012 ◽  
Author(s):  
A. Muller ◽  
M. Jovalekic ◽  
S. Tenbohlen
Keyword(s):  
Solubility Study ◽  
Transformer Oils ◽  
Different Gases

Application evaluation of iron-containing composite for purification of power transformer oils

2019 ◽  
Vol 20 (7) ◽  
pp. 312-316
Author(s):  
Sergey Kulyukhin ◽  
◽  
Vadim Komarov ◽  
Alexandr Seliverstov ◽  
Yuliya Zakharova ◽  
...  
Keyword(s):  
Power Transformer ◽  
Transformer Oils

Effect of Oleic Acid Surface Modification on Dispersibility of TiO2 Nanoparticles in Transformer Oils

2013 ◽  
Vol 28 (6) ◽  
pp. 594-598 ◽  
Author(s):  
Yu-Zhen LÜ ◽  
Sheng-Nan ZHANG ◽  
Yue-Fan DU ◽  
Mu-Tian CHEN ◽  
Cheng-Rong LI
Keyword(s):  
Surface Modification ◽  
Oleic Acid ◽  
Tio2 Nanoparticles ◽  
Transformer Oils

Formulation and Evaluation of Solubility Enhanced Ciprofloxacin

2013 ◽  
Vol 6 (3) ◽  
pp. 2131-2136
Author(s):  
R S Thakur ◽  
A Nayaz ◽  
Y Koushik
Keyword(s):  
Oral Absorption ◽  
Polymeric Micelles ◽  
In Vitro Release ◽  
Microemulsion System ◽  
Salt Form ◽  
Solubility Study ◽  
Ternary Phase Diagrams ◽  
Blood Ph ◽  
Vitro Release

In the case of solubility limited absorption, creating supersaturation in the GI fluid is very critical as supersaturation may provide great improvement of oral absorption. The techniques to create the so-called supersaturation in the GI fluid include microemulsions, emulsions, liposomes, complexations, polymeric micelles, and conventional micelles. Ciprofloxacin was chosen because it is practically insoluble in water; hence its salt form is used commercially, which is soluble in water. The objective of the present investigation was to enhance the solubility of Ciprofloxacin by formulating it into microemulsion system. For this purpose, initially, surfactant and cosurfactant were selected based on their HLB value, followed by pseudo-ternary phase diagrams to identify the microemulsion existing zone. Different formulations were developed and evaluated for pH, conductivity, in vitro release and stability. Solubility study was performed for optimized formulation. The pH of the designed formulations varied from 6.02-7.04. This was ideal and near blood pH 7.4. Conductivity data indicated that the microemulsion was of the o/w type. In vitro release of optimized formulation(FM3) was 95.2% as compared to pure drug 46.61% after 90 min and marketed product(salt form) 93.9%. Hence, by formulating into microemulsion, the solubility of ciprofloxacin is significantly enhanced.    

Thermal Ageing of Some Insulating Fluids in Contact with Insulation Paper and Copper

2018 ◽  
Vol 68 (12) ◽  
pp. 2881-2885
Author(s):  
Iosif Lingvay ◽  
Gabriela Oprina ◽  
Livia Carmen Ungureanu ◽  
Alexandra Pica ◽  
Valerica Stanoi
Keyword(s):  
Experimental Data ◽  
Dissolved Oxygen ◽  
Copper Foil ◽  
Atmospheric Oxygen ◽  
Thermal Ageing ◽  
Dissolved Oxygen Concentration ◽  
Mineral Oils ◽  
Synthetic Ester ◽  
Cu2o Film ◽  
Transformer Oils

The behaviour of copper and insulation paper in various electrical insulating fluids (transformer oils) exposed to thermal ageing at 110�30C for 1000 hours in closed vessels (without access to atmospheric oxygen) has been studied. The processing of the comparative experimental data revealed in all cases that the concentration of dissolved oxygen in the investigated oils decreases exponentially during the heat treatment. In the presence of the copper foil, the oxygen is almost depleted (the dissolved oxygen concentration is approaching zero), indicating a higher affinity of the copper to oxygen than the affinity to oxygen of the investigated oils. In the presence of the copper foil and / or of the insulation paper, the degradation processes of the mineral oils have a pronounced character, explained by the catalytic activity of the Cu2O film that has been formed and by the paper degradation, respectively. A high thermo-oxidative stability was noticed in the case of natural triglyceride oils, particularly for the synthetic ester-based oil.

scholarly journals On the Use of MOFs and ALD Layers as Nanomembranes for the Enhancement of Gas Sensors Selectivity

Nanomaterials ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 1552 ◽  
Author(s):  
Weber ◽  
Graniel ◽  
Balme ◽  
Miele ◽  
Bechelany
Keyword(s):  
Thin Films ◽  
Gas Sensors ◽  
Gas Sensing ◽  
Metal Organic Frameworks ◽  
Atomic Layer ◽  
Operational Performance ◽  
Layer Deposition ◽  
Metal Organic ◽  
Further Development ◽  
Different Gases

Improving the selectivity of gas sensors is crucial for their further development. One effective route to enhance this key property of sensors is the use of selective nanomembrane materials. This work aims to present how metal-organic frameworks (MOFs) and thin films prepared by atomic layer deposition (ALD) can be applied as nanomembranes to separate different gases, and hence improve the selectivity of gas sensing devices. First, the fundamentals of the mechanisms and configuration of gas sensors will be given. A selected list of studies will then be presented to illustrate how MOFs and ALD materials can be implemented as nanomembranes and how they can be implemented to improve the operational performance of gas sensing devices. This review comprehensively shows the benefits of these novel selective nanomaterials and opens prospects for the sensing community.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

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