Clays impact on bitumen-extraction processes

2018 ◽  
Author(s):  
Elco Hollander ◽  
Oladipo Omotoso
Keyword(s):  
Extraction Processes ◽  
Bitumen Extraction

scholarly journals Modeling Effect of Bitumen Extraction Processes on Oil Sands Tailings Ponds

2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Silawat Jeeravipoolvarn ◽  
Warren Miller ◽  
Don Scott ◽  
Louis Kabwe ◽  
Ward Wilson
Keyword(s):  
Oil Sands ◽  
Extraction Processes ◽  
Oil Sands Tailings ◽  
Tailings Ponds ◽  
Bitumen Extraction ◽  
Oil Sands Tailings Ponds

Experimental and Modeling Study of Residual Liquid Recovery from Spent Sand in Bitumen Extraction Processes from Oil Sands

2013 ◽  
Vol 47 (4) ◽  
pp. 2109-2116 ◽  
Author(s):  
Moosa Rabiei Faradonbeh ◽  
Mingzhe Dong ◽  
Thomas G. Harding ◽  
Jalal Abedi
Keyword(s):  
Oil Sands ◽  
Residual Liquid ◽  
Extraction Processes ◽  
Bitumen Extraction ◽  
Modeling Study

Evaluation of green extraction processes, lipid composition and antioxidant activity of pomegranate seed oil

2021 ◽  
Author(s):  
E. Rojo-Gutiérrez ◽  
O. Carrasco-Molinar ◽  
J. M. Tirado-Gallegos ◽  
A. Levario-Gómez ◽  
M. L. Chávez-González ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Lipid Composition ◽  
Seed Oil ◽  
Green Extraction ◽  
Pomegranate Seed Oil ◽  
Extraction Processes ◽  
Pomegranate Seed

Syntheses and evaluation of new hydrophilic azacryptands used as masking agents of technetium in solvent extraction processes

2021 ◽  
Vol 50 (5) ◽  
pp. 1620-1630
Author(s):  
Alexiane Thevenet ◽  
Ana Miljkovic ◽  
Sonia La Cognata ◽  
Cécile Marie ◽  
Christelle Tamain ◽  
...  
Keyword(s):  
Nitric Acid ◽  
Solvent Extraction ◽  
Acid Medium ◽  
Separation Process ◽  
Nitric Acid Medium ◽  
Masking Agents ◽  
Extraction Processes

New azacryptands with the addition of hydrophilic groups were synthesized to bind selectively the pertechnetate anion in nitric acid medium, and avoid its extraction in the separation process.

scholarly journals Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

2021 ◽  
Vol 11 (14) ◽  
pp. 6524
Author(s):  
Andrés Pérez-González ◽  
Álvaro Jaramillo-Duque ◽  
Juan Bernardo Cano-Quintero
Keyword(s):  
Research Work ◽  
Solar Photovoltaic ◽  
Boundary Extraction ◽  
Pv Systems ◽  
Periodic Maintenance ◽  
Planning Algorithm ◽  
Extraction Processes ◽  
Inspection And Maintenance ◽  
Photovoltaic Plants ◽  
Path Planning Algorithm

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.

Bitumen Extraction from Oil Sands using CaO (Lime) and Ozone

2008 ◽  
Author(s):  
Tayfun Babadagli ◽  
Z. Burkus ◽  
Speros Moschopedis ◽  
Baki Ozum
Keyword(s):  
Oil Sands ◽  
Bitumen Extraction

scholarly journals Optimization of the extraction of galactoglucomannans from Pinus halepensis

Holzforschung ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Nacera Benouadah ◽  
Andrey Pranovich ◽  
Djamel Aliouche ◽  
Jalel Labidi ◽  
Stefan Willför
Keyword(s):  
High Performance ◽  
Pinus Halepensis ◽  
Hot Water ◽  
Water Extraction ◽  
Chemical Degradation ◽  
Extraction Time ◽  
Size Exclusion ◽  
Hot Water Extraction ◽  
Extraction Processes ◽  
Extraction Parameters

AbstractThe effectiveness of pressurized hot-water extraction conditions for obtaining galactoglucomannans (GGMs) from Pinus halepensis suitable for applications like coatings and films packaging was investigated. For this purpose, high molar masses with high yields are required, presenting a serious challenge for hot-water extraction processes. The extraction of GGMs was carried out in an accelerated solvent extractor (ASE) and the isolation was performed by precipitation in ethanol. Three temperatures in the range 160–180 °C and five extraction times 5–90 min were tested in order to optimize extraction parameters of GGMs, avoiding thermal and chemical degradation in hot-water. Total dissolved solids (TDS) were determined gravimetrically after freeze-drying and weight average molar masses (Mw) were determined by high-performance size exclusion chromatography (HPSEC). Total non-cellulosic carbohydrates were determined by gas chromatography (GC) after acid methanolysis. Free monomers were additionally analyzed by GC. Lignin in water extracts was measured by an ultraviolet (UV) method. Acetic acid was determined after alkaline hydrolysis of acetyl groups and analyzed by HPSEC. The main parameters influencing the extraction processes of the GGMs, namely, extraction time and temperature were studied. Optimal extraction parameters of GGMs were identified at 170 °C and 20 min extraction time, with average Mw of extracted fraction of 7 kDa leading to a GGM yield of approximately 56 ${\text{mgg}}_{\text{o}.\text{d}.\text{m}}^{-1}$, corresponding to 6% on dry wood basis.

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