• Decarbonization Potential of E-Gasoline and E-Diesel for Road Transportation in Saudi Arabia by Life-Cycle Assessment

  Zhao, Chengcheng (2023-09-13) [Poster]

  This paper provides a thorough evaluation of emerging e-fuel technologies, namely e-FT (Fischer-Tropsch) fuels and e-MTG (Methanol-to-Gasoline), as credible substitutes for traditional fossil fuels in Saudi Arabia's energy landscape. Amid a global pivot towards more sustainable and low-carbon energy solutions, this study aims to quantify the environmental benefits of adopting these e-fuels within the framework of Saudi Arabia's initiatives to diversify its energy portfolio and curtail greenhouse gas emissions. Utilizing a comprehensive well-to-wheel analysis, the research incorporates various methods for hydrogen production as well as carbon capture techniques. We present two distinct technology scenarios, targeting the years 2030 and 2060, to evaluate the capacity of e-fuels in aiding Saudi Arabia's transition towards a low-carbon energy infrastructure. In addition, the paper compares the carbon reduction efficacy of e-fuels against that of conventional diesel/gasoline fuels. This study offers invaluable perspectives for policymakers, energy analysts, and investors, shedding light on the viability of e-FT and e-MTG technologies in mitigating the environmental footprint of Saudi Arabia's energy sector.
• E-fuel Utilization :OMEs

  khan, Md Zafar Ali (2023-09-13) [Poster]

  General chemical structure CH3 O [ CH2O ]n CH3 with n as the number of CH2O groups characterizes oxygenated methyl ethers (OMEs), which can be produced through CO2-neutral processes. OMEs offer promising alternatives as engine fuels due to their unique structural features, lacking direct C C bonds, leading to reduced soot formation during combustion. This research investigates the reactivity of OMEs, specifically those with long-chain alkyl groups, in comparison to fossil-derived diesel fuels. Our study utilizes the shock tube as an ideal reactor to explore a range of engine-relevant conditions, focusing on the autoignition of OMEs. To aid in this investigation, we employ advanced laser diagnostics, including an external-cavity quantum cascade laser for CO2 measurements and a MIRcat-QTTM laser system for tracking fuel decay. Both ignition delay time (IDT) experiments and laser diagnostics contribute to a comprehensive understanding of OME oxidation kinetics. We validate our experimental findings by comparing them to predictions from literature mechanisms and further enhance our understanding through sensitivity analysis. Our research demonstrates IDT measurements and constant volume simulations for various fuel/oxygen/argon mixtures across different pressures and equivalence ratios (?).
• Diesel and HFO Emulsion Testing

  Sarvothaman, Varaha P (2023-09-13) [Poster]

  Water emulsified fuels produced by controlled cavitation technology offer a stable and homogeneous fuel for the combustion of heavy fuel oils (HFOs). The emulsified fuel has advantages such as secondary atomization, high level of combustion, and lower emission of pollutants. This project is aimed at characterizing emulsions produced by the controlled cavitation technology (hydrodynamic cavitation) method, by Pacific Green. Two pairs of fuels/emulsions ULSFO (before and after) and VLSFO (before and after) were tested. Techniques employed for characterization of these fuel/emulsions were combustion (TGA, Pyro GC x GC, FTICR-MS), microscopy (cryo-SEM, optical), and engine-based testing. The TGA revealed that the ULSFO (before and after) exhibit no changes in behaviour, whereas the VLSFO (before and after) exhibit changes in thermal behaviour. Further testing with Pyro GC x GC a pyrolysis based technique, revealed the same trend: where the ULSFO exhibited no changes and VLSFO exhibits changes. Further it was seen that the reactivity/emission of S was lower for the VLSFO emulsion. Microscopy based techniques showed a non-uniform distribution of water droplets, which is suggestive of lesser intense cavitation in creating the emulsion. Two broad classes of characterization (combustion and microscopy) indicate that samples of VLSFO are distinguishable, and there is a lower emission. And the cavitation-based method has no observable effect on ULSFO. Further work, on Engine testing will be carried out at Uni. of Lund in the coming months on a six-cylinder Scania D13 to complete the characterization of these emulsions.
• Non-intrusive Microwave Sensor for In Situ Multiphase Flow Sensing in Oil Production

  Mansori, Hassan (2023-08-09) [Poster]

  Efficient oil production requires accurate flow rate and phase distribution monitoring to ensure safety and optimization. Traditional intrusive sensors have limitations, necessitating a novel approach. This work proposes a non-intrusive microwave T-resonator sensor to detect water and gas contents in oil production wells. The sensor's distinct behavior for different fluids is achieved using spiral resonators. The design is orientation insensitive, relatively inexpensive, and suitable for extreme well conditions. It eliminates the need for mixing oil and water, providing accurate measurements and promising a practical solution for multiphase flow monitoring.
• Attentive Graph-based Relational Encoder (GRE) for Antonyms vs Synonyms Distinction

  Alshmrani, Maha Muhammed (2023-08-09) [Poster]

  Antonyms vs synonyms distinction is a core challenge in lexicon-semantic analysis and automated lexical resource construction. These pairs share a similar distributional context which makes it harder to distinguish them from each other. Leading research in this regard at007 tempts to capture the properties of the relation pairs, e.g., symmetry, transitivity, etc. How009 ever, the inability of existing research to appropriately model the relation-specific properties limits their end performance. In this paper, we propose GRE, i.e., Graph-based Relational Encoder that aims to capture and model the relation-specific properties of the antonyms and synonyms pairs in order to perform the classifi016 cation task in a performance-enhanced manner. Experimental evaluation using the benchmark datasets shows that GRE outperforms the existing research by a relative score of up to 1.8% in F1-measure.
• Custom Data Set Recognition for Industrial Automation

  Kamal, Jana (2023-08-09) [Poster]

  This project is a combination of a Universal Robot manipulator controlled using ROS and MoveIt, a Zivid2 camera is attached to the manipulator to capture an image of what is infront of it to perform mug classification using AI by employing YOLOv5 with a custom dataset created using Roboflow. The system is able to identify whether a mug is intact or broken. This integration of robotics and AI allows for efficient and automated mug inspection and decision-making capabilities.
• Object recognition using tactile sensor

  Azab, Meral (2023-08-09) [Poster]

  Tactile sensors have emerged as a promising technology for object identification, offering a distinct approach compared to traditional visual-based recognition systems. By relying on touch to detect and analyze physical properties, tactile sensors provide a more accurate and intuitive means of recognizing objects. This study aims to showcase the advantages of utilizing tactile sensors for object recognition. Through the development of a model, we demonstrate the effectiveness of tactile sensors in accurately recognizing objects. Our model achieved a high level of accuracy on the test set and successfully predicted the class label of sample images. Furthermore, this study highlights the potential of DIGIT tactile sensor 2D images for enhancing object recognition. Future explorations in this area aim to unlock this potential, paving the way for advancements in tactile-based perception systems.
• Price Formation Using Mean Field Games

  Aljadhai, Khaled (2023-08-09) [Poster]

  Here, we consider price-formation model using mean-field game (MFG) framework. The model considers a large number of small players engage in trading commodity such as electricity. We present semi-explicit solution for the linear-quadratic model with time-varying preference included as a penalty. Then, we explore qualitative examples of the model.
• Probing self-assembled monolayers on Au(111) substrates at low temperature

  musallam, arwa (2023-08-09) [Poster]

  To investigate the behaivor of SAMs on Au(111) at 150K using scanning tunneling microscopy.
• Bayesian Deep Neural Networks

  AlMalawi, Maysoon (2023-08-09) [Poster]

  To investigate and develop methods for the fitting of Bayesian neural networks.
• Characterization of Solid Microneedles Coated with Hydrogel for Interstitial Fluid Extraction

  bajunaid, Roba (2023-08-09) [Poster]

  Microneedle (MN) applications in healthcare vary widely, from transdermal drug and vaccine delivery to extraction of skin interstitial fluid (ISF). MNs are non-invasive due to their small size and design. They consist of tiny needle tips that penetrate the outermost layer of the skin without causing significant pain or damage. They vary in geometry (pyramidic, conical) typically having a height range of 50-900?m, and can be categorized into solid, hollow, and porous MNs. Herein, we developed a 3x3 solid MN array coated with a layer of hydrogel matrix for extraction of ISF due to their biocompatibility, water absorption and swelling behavioral properties. We experimented with different ratios of alginate to cross-linker to determine the optimal hydrogel that has the highest absorption volume and tested the mechanical strength of the MNs to ensure their capability of penetrating through skin. Results show that a ratio of 5:1 has high absorption properties, and a solid MN has an average fracture force of 0.1967 N.
• CE program and CNS group

  Ruiz Martinez, Javier (2023-08-02) [Poster]
• Feasibility of Physics-Informed Neural Networks for Single-phase Flow in Pipes

  Al-Ahmed, Haidar (2023-08-01) [Poster]

  The use of Physics-informed neural networks (PINNs) has become a popular approach for solving differential equations that describe physical phenomena, particularly in the field of petroleum engineering. The main goal of this research is to apply PINNs to effectively solve the complex problem of single-phase flow in pipes. The problem that we are trying to solve concerns with plotting the velocity profile of an incompressible, isothermal, steady-state single-phase flow in a vertical wellbore.
• Photoluminescence Quenching in MoS2/Y6 Heterostructures

  Albeladi, Dhay (2023-08-01) [Poster]
• Easy-to-recycle one component composite of high performing Ultra-High Molecular Weight Polyethylene tapes

  Yar, Fayruz (2023-08-01) [Poster]

  Ultra-High Molecular Weight Polyethylene (UHMWPE) possess excellent mechanical properties. Due to the absence of functional groups, UHMWPE is unable to bond with any material. Here, we test the possibility of using lower molecular weight polyethylene (i.e. HDPE) to make one-component multi-layered composite with the UHMWPE tapes. Variable HDPE content in the UHMWPE is tested under different conditions (overlap area, HDPE content, temperature, Pressure) and the bonding is quantified by measuring the tensile strength of the laminated tapes.
• Addressing the Challenge of Automatic Differentiation for non-Differentiable Functions

  Alabdrabulrasul, Ali (2023-08-01) [Poster]

  My research aims to tackle the limitations of automatic differentiation (AD) within Physics Informed Neural Networks (PINNs), specifically focusing on the difficulties with non differentiable functions in petroleum engineering applications, such as fluid f low in porous media. By addressing these critical constraints, the applicability and accuracy of AD can be significantly improved, leading to enhanced problem solving capabilities and a deeper understanding of complex phenomena. Overcoming the challenges o f AD in managing non differentiable functions not only opens up new opportunities for its application in petroleum engineering but also extends its potential in other fields facing similar issues.
• 3D Printing for Accessible and Cost-Effective Microchannel Fabrication

  Alhazmi, Razi (2023-08-01) [Poster]

  Microchannels play a crucial role in biotechnology, chemistry, and materials science. However, the current fabrication process may not be feasible due to the high fabrication cost and limitations. Therefore, to meet the growing demand, we are proposing a new method that will ease the fabrication of microchannels with highly precise reproducibility and a smaller diameter size of less than 200 micrometers.
• Pioneering Aerial Highways for Unmanned Aerial Vehicles with high safety standards

  Alalwan, Siba (2023-08-01) [Poster]

  This work presents an overview of the development of efficient aerial highways for Unmanned Aerial Vehicles (UAVs). Our study attempts to preemptively solve the problem of increasing air traffic by designing a highway system similar to highways for ground transportation. Aerial vehicles could run into safety issues such as mid-air collisions, engine failure, and losing communication between drones and their operators. Our proposed highway design will mitigate the damages caused by these issues or avoid them altogether. We formulated the highway design into an optimization problem that optimizes the placement of the highways to maximize proximity to the delivery routes while minimizing the total distance traveled. Our results show that highways organize traffic, which reduces the probability of mid-air collisions and helps strategically place fewer communication and control centers along the corridors.
• Exploiting the application of ChatGPT on seismic wave analysis: A case study in self-supervised denoising

  Alqahtani, Fatimah (2023-08-01) [Poster]

  This poster highlights the potential of ChatGPT in generating code and its application in seismology. Specifically, we explore the use of the Eikonal equation for seismic wave travel time regulation, which is crucial for source localization, imaging, and inversion. To test ChatGPT's efficiency, we began with one wavelet and demonstrated its ability to implement self-supervised noise suppression. Noise is a significant part of seismic data, and geophysicists aim to obtain clean data for accurate analysis and interpretation. We showcase the transformative capabilities of AI tools like ChatGPT in generating code for various tasks. We also discuss different types of noise, such as White Gaussian Noise, Time Correlated Noise, and Trace-Wise Noise, and how understanding their differences can improve seismic data quality and increase accuracy in analysis and interpretation.
• Parametric study of the detonation cell width for H2:O2 by statistical approach on the soot-foil technique.

  Akram Aldeen, Sulaiman (2023-08-01) [Poster]

  Detonation is the supersonic mode of propagation of combustion waves. It can be described as a supersonic front sustained by an exothermic reaction zone. However, the structure of the front is not planar and exhibits both three-dimensional and transient features. This translates into the so-called cellular structure of detonation and is visualized by inserting a sooted plated on the side walls of the tube. In this case, a fish-scale pattern is observed and can be studied to further understand the properties of the detonation. In particular, a correlation between cell mean width and the chemical properties of the detonation (induction length) was demonstrated. However, most of the soot-foil studies report very limited information on the soot foil giving only an estimated value of the cell mean width. In this work, we aim at characterizing the statistical distribution of cells in the soot foil by providing the mean cell width, the standard deviation, and the size of the distribution. Several parameters while be varied to highlight the advantages of this approach.

View more