Design of the new couplers for C-ADS RFQ

Shi, Aimin; Sun, Lin; Zhang, Zhouli; Xu, Xianbo; Shi, Liting; Li, Chen-Xing; Wang, Wenbin

doi:10.1088/1674-1137/39/4/047004

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…A key factor to note is that the effect of adding an acid (or base) to the aqueous phase could lead to different results than changing the acidic (or basic) properties of crude oil (as observed with model oils 8,17,19,20,37,38 ). Question to address here is how does varying crude TAN and changing acidity of the aqueous phase affect emulsion stability?…”

Section: Energy and Fuelsmentioning

confidence: 99%

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Functional Molecules and the Stability of Water-in-Crude Oil Emulsions

2017

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In the production and transportation of crude oil, undesirable water-in-oil emulsions may form. Undesirable water-in-oil emulsions may also form in hydraulic fracking when acid is added to the injection water. We investigate the role of acid−base interactions in the stability of water-in-oil emulsions. Asphaltenes and resins, referred to as functional molecules, constitute the polar fractions of crude oil. They contain heteroatoms such as N, O, and S, which lead to acidic and basic functionalities in petroleum fluids and contribute to the stability of water-in-oil emulsions. We have quantified the acidity and the basicity of nine petroleum fluids (light, medium, heavy, and extra-heavy crude oils) by measuring their total acid number (TAN), total base number (TBN), and hydroxyl number (HN). Except in extra heavy oils where the TAN and TBN are close, all the other oils are basic (i.e., TBN > TAN). We observe that all the light crude oils form stable emulsions with deionized (DI) water, while medium, heavy, and extra-heavy oils do not. Addition of a strong acid (HCl, 4−37 wt %) to the aqueous phase enhances emulsion stability in light oils, induces the formation of water-in-oil emulsions in medium oils, and does not lead to emulsions in heavy and extra-heavy oils. Addition of a strong base (NaOH, 4−20 wt %) to the aqueous phase destabilizes emulsions in light oils, but leads to emulsions in a medium-density oil. There is no effect on heavy and extra heavy oils. The effect of salts on the stability of water-in-oil emulsions is also studied. At the salt concentrations studied (4−30 wt %), we observe that salts destabilize emulsion; multivalent salt solutions are more potent at weakening emulsions, compared with mono-and divalent salt solutions. These results are attributed to the preferential hydration of ions by water molecules, as compared to interactions of asphaltenes with water.

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Section: Energy and Fuelsmentioning

confidence: 99%

“…37 Shi et al discuss that emulsion stability is enhanced if the aqueous phase has a low pH. 38 Addition of base to the aqueous phase is shown to destabilize the emulsion. 39 The effect of salt on the formation and stability of emulsions is not well understood and at times contradictory.…”

Section: Introductionmentioning

confidence: 99%

Functional Molecules and the Stability of Water-in-Crude Oil Emulsions

2017

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“…For example, Wang(Xin Wang, Lin Duan, Aiye Shi, et al, 2021) et al used inter-layer fusion to obtain richer features. However, this approach requires down sampling using a pooling operation in order to keep the spatial resolution consistent, but the pooling operation leads to the loss of some of the key information (Shi et al, 2021). In addition, feature pyramid networks (FPN) are also widely used in the field of remote sensing image classification (Lin et al, 2017), and although they can more fully utilize different levels of features, FPN are prone to loss of details because they use nearest-neighbor or bilinear interpolation to generate high-resolution feature maps.…”

Section: Multi-scale Feature Fusionmentioning

confidence: 99%

A lightweight enhanced branching attention model for remote sensing scene image classification

Wang,

et al. 2024

Preprint

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Unlike natural images, remote sensing images exhibit significant spatial complexity and minimal intra-class differences, presenting considerable challenges in the field of remote sensing scene image classification (RSSC). Although existing convolutional neural networks have achieved some progress in this domain, they often fail to fully account for the unique characteristics of remote sensing images. Additionally, these networks typically suffer from excessive parameter redundancy, resulting in substantial computational burdens. This is particularly problematic given the difficulty in obtaining and labeling remote sensing data. To address these issues, this paper proposes a lightweight method (AEBANet) featuring an attention branching structure specifically designed for RSSC. First, we construct an overall feature extraction framework based on depth-wise separable convolution (DS-Conv) to ensure efficient feature extraction while maintaining accuracy. Then, we propose the Adaptive Enhanced Branch Attention (AEBA) module, a lightweight structural design that enhances the model's capability to capture key features in both channel and spatial domains. Second, we develop the Multi-Level Feature Fusion (MLFF) module to integrate features at different levels, thereby improving information flow between features and utilizing detailed shallow information to supervise the deep global information. Finally, the proposed AEBANet achieves the highest overall accuracy of 93.12%, 96.76%, and 99.52% on the NWPU, AID, and UCM datasets, respectively. Ablation studies on these datasets validate the effectiveness and necessity of each module. Additionally, the proposed method is characterized by low complexity and computational cost.

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“…For a single disaster, the analytical methods of supporting body vulnerability mainly include determining the regional vulnerability according to historical data, the regional vulnerability assessment based on an index system and an actual survey [10]. There are delays for the occurrence of disasters in the disaster chain, and there are overlaps in the impact area.…”

Section: Risk Assessment Model Of Disaster Chainmentioning

confidence: 99%

Research on Risk Assessment Model of Industrial Park Earthquake Disaster Chain

Ma¹,

Jian-yu²,

Chen³

2015

Advances in Intelligent Systems Research

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Abstract. The factors influencing disaster chain risk were analyzed, and the risk assessment model of earthquake disaster chain was established. The methods to determine relevant parameters in the model were given. An example that analyzed the risk of earthquake-fire disaster chain in a petrochemical system was presented to illustrate the applicability and effectiveness of the model. This provides a basis to prevent and control the earthquake disaster chain risk of industrial park.

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Design of the new couplers for C-ADS RFQ

Abstract: Design of the new couplers for C-ADS RFQ) 1,2 LI Chen-Xing( ) 1,2 WANG Wen-Bin( ) 1

Cited by 5 publications

References 2 publications

Functional Molecules and the Stability of Water-in-Crude Oil Emulsions

Functional Molecules and the Stability of Water-in-Crude Oil Emulsions

A lightweight enhanced branching attention model for remote sensing scene image classification

Research on Risk Assessment Model of Industrial Park Earthquake Disaster Chain

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