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    Adsorption Kinetics of Copolymers and Sulfonated Polymers for Enhanced Oil Recovery

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    Polymer flooding is one of the most mature enhanced oil recovery (EOR) techniques, where the injection water viscosity is increased through addition of a high molecular weight polymer. In field operations, one of the most critical parameters for successful polymer flooding is the polymer adsorption. During transport, the polymer will irreversibly adsorb onto the reservoir rock, with the exact extent of adsorption depending on reservoir/polymer properties, such as the polymer composition and molecular weight, mineralogy of the reservoir, brine composition, reservoir temperature etc. For each reservoir, there will be an upper limit of adsorption above which sufficient polymer will be removed from solution to make the process uneconomic. Copolymers of acrylamide (AM) and acrylic acid (AA) have been the most prominent chemicals to be applied, whereas sulfonated polymers containing acrylamide tertiary butyl sulfonic acid (ATBS) have been used for higher temperature and/or salinity conditions. In this work, it is demonstrated that there is a large kinetic component to the adsorption for a range of six polymer species on silica sand. The work was carried out in a field composition brine at a temperature of 31°C. The polymers consisted of AA-AM co-polymers (20-33 % AA) and AM-AA-ATBS ter-polymers (up to 15 mol% ATBS). While an adsorption of ~20 µg/g was measured after 24 hours, this increased continuously over 20-30 days for the AA-AM co-polymers. The same trend was observed for the AM-AA-ATBS terpolymers – with an adsorption at 24 hours of ~15 µg/g increasing over time to 36 ug/g. Two polymer species (33 % AA & 15 % ATBS) were then taken forward to dynamic core flood experiments where a novel shut-in procedure was used to highlight the kinetic behavior. The breakthrough profiles were matched via numerical simulation using a simple isotherm and kinetic constant. These results were then extrapolated to other conditions to highlight the potential for misinterpretation of traditional core flooding approaches. To the authors knowledge, the kinetic adsorption and its impact has not been very extensively discussed in the literature. The ability to accurately plan polymer flooding projects is essential to fully optimise recovery performance as efficiently as possible, minimise the environmental footprint and reliably predict polymer breakthrough for production chemistry requirements. Thus, a complete understanding of the polymer adsorption and adsorption kinetics is critical for the continued development of polymer EOR

    Practical considerations to optimize aquatic testing of particulate material, with focus on nanomaterials

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    Aquatic testing of particulate materials (PMs), e.g., nanomaterials (NMs) and microplastics (MPs), poses inherent challenges potentially hindering the application of existing test guidelines (TGs). Those TGs are primarily designed for hazard assessment of the dissolvable form of a material, whereas the guidance document on aquatic and sediment toxicological testing of NM (OECD Guidance Document 317) encourages the inclusion of potential colloidal fractions in the assessment. A prerequisite for the testing of PMs is the preparation of stable dispersions. However, testing difficulties may result from the fact that nano-scale PMs are inherently unstable when dispersed in test media, leading to the need for differentiation of potential chemical vs. physical effects caused by the tested material. Aquatic testing of unstable PMs will likely result in inconsistent and non-uniform uptake and exposure scenarios and thus effects observed in the respective test systems. Maintaining stable exposure conditions is often very challenging given the constantly changing size of the PM and its agglomerates, requiring observed endpoints to be based on measured concentrations and particle size distributions present in the water phase, while neglecting agglomerated and settled particulates. In this paper we describe the current state of PM-testing, demonstrate PM-specific challenges in aquatic testing (e.g., test duration, physical effects, instability, biodegradation, bioaccumulation) with a focus on NMs, considering a set of most relevant TGs, and provide proposed testing considerations to optimize aquatic testing of PMs

    Integration of BIPV Design and Energy Efficient Technologies for Low Energy Building in Meeting Net Zero Target

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    Based on the World Energy Transition Outlook, almost two thirds of the CO2 emissions released in the world are related to energy emissions with 36 Gigatonnes of energy related CO2 released in 2015. To reduce CO2 emissions by 70% before 2050, energy efficiency parameters and renewable energy systems will have to be incorporated together to achieve a more sustainable future. The synergy between these two systems could potentially decrease the total final energy consumption of the world by 25EJ, which is by 5% by the year 2030. One of those synergy methods would be the incorporation of energy efficient building designs and technologies with BIPV systems. Energy efficient building design is the process of upgrading or constructing buildings or elements that are able to maximize the consumption of energy supplied to them by reducing energy losses, through various innovative and effective systems and technologies. BIPV systems are PV products and components that are integrated into the buildings structure. In Malaysia, research and applications of BIPV are just emerging and is still in early development. In the final report submitted to the United Nations Development Program, Malaysia aimed to achieve an increase of 330% in installed BIPV capacity. This paper aims to simulate an energy efficient building through a specific 3D modelling software, Autodesk Revit, that incorporates BIPV design systems. The method used to create this model will be performed in four stages. First, the development of a comprehensive 3D model of a suitable building. Second, analysis of photovoltaic system of the model. Third, an intricate energy analysis of 3D building and BIPV system via Revit and Autodesk Insight. Lastly, evaluation of the energy consumption of the building and CO2 emissions. This research has managed to reduce the Final Energy Use Intensity of the building from 194 kwh/m2/year to 140 kwh/m2/year and the CO2 emissions from 258 metric tonnes CO2e to 186 metric tonnes CO2e a year. This research presents the integration of energy efficient designs and technologies, with BIPV systems toward low energy building target

    A Novel RIS-Aided Optimization Strategy for Semantic Communication System

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    Reconfigurable intelligent surfaces (RISs) are programmable metasurfaces capable of optimizing signal strength and reducing interference, serving as key components in maintaining the integrity of semantic information during transmission. This study explores the establishment of additional semantic transmission and reflection pathways by deploying RISs in different cells. An optimization strategy, maximizing mutual information (MI) for quality of experience (QoE)-aware modeling of the R-SC system (QR-SC), is proposed to enhance both semantic and communication performance. Additionally, a QoE-aware model is utilized for users to gauge semantic transmission performance. Experimental results indicate that QR-SC can elevate the performance of semantic communication while ensuring reliable transmission, highlighting the substantial potential of RIS in digital and energy simultaneous transmission.</p

    Exploring CO<sub>2</sub>-H<sub>2</sub>S Storage in Deep Saline Aquifers:A Case Study from an Offshore Gas Field in Malaysia. From Lab to Numerical Simulation

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    Most carbon dioxide (CO2) storage schemes rely on storing CO2 in its supercritical state, free from impurities (SNC-Lavalin Inc., 2004). The X field reservoir model, like many other existing reservoir models, assumes pure CO2 injection for its numerical simulation of CO2 storage. However, one of the gas sample analyses from the X field revealed the presence of trace amounts of hydrogen sulfide (H2S,) ranging from 500 to 1000 ppm. Given the limitations of the separation technology, there is a potential scenario where CO2 might be co-injected with H2S for storage. Understanding the impact of this H2S within the injected CO2 stream is crucial for ensuring the success of Carbon Capture and Storage (CCS) operations (Basava-Reddi et al., 2014; Wang et al., 2011). There is a possibility of CO2 being co-injected with this H2S for storage. The effect of the contaminant in the injected CO2 stream needs to be accessed to ensure the success of the CCS operation. The alterations in the base CO2 solubility can ultimately influence storage integrity and capacity (Ahmad et al., 2023). While abundant solubility data for CO2 in water or brine exist in the literature (A Chapoy et al., 2004; Valtz et al., 2004; Ahmadi &amp; Chapoy, 2018), limited data are available for this ternary CO2- H2S-Brine system. Therefore, the need to quantify the impact of H2S impurities on CO2 solubility is evident. In this context, extensive laboratory experiments were undertaken to address these uncertainties and further refine the X field dynamic model for enhanced accuracy.</p

    Impact of CO<sub>2</sub> solubility on design of single well tracer tests to evaluate residual saturation during carbon capture and storage

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    Single-well tracer technique have been well applied in many petroleum industry and environmental applications. However, these tests have not been well developed for CO2 geological storage purposes to evaluate residual CO2 saturation during the appraisal phase of site investigation, due to the challenges occurring from the complex phase behaviour. In this study, two single-well tracer tests are numerically modelled to quantify the residual gas saturation. Our study addresses the design of an alternative single well tracer test sequence, which involved a single pass of the tracer saturated water over the residually trapped zone, thereby reducing the amount of CO2 dissolution into the tracer solution. A one-dimensional numerical modelling of the tracer propagation and partitioning with homogenous properties was used for the calculations of the difference in tracer breakthrough times during water withdrawal from the tests. Model sensitivity variations were applied to analyse the impact of reservoir and treatment design parameters on the residual gas saturation. The residual gas saturations calculated reflect the input values, including the effect of hysteresis, to within 10% accuracy. It was found that changing the CO2 saturated water volume injected after CO2 made the CO2 front to travel to different distances from the well, and thus the tracer had different size of residually trapped zones to travel through when it is back produced and encounters different residual gas saturations, and therefore affected the residual gas saturation calculations. The modelling also shows that optimal injection of CO2-saturated water to prevent the dissolution of the residually trapped CO2 and establish the residually trapped zone was challenging to achieve, and therefore using the fluid withdrawal method was more robust to establish the residually trapped zone. This is because of the dependency of solubility on pressure. The numerical models may be used to design, optimise, and interpret the field tests.</p

    Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student Programmers

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    Generative AI has considerably altered traditional workplace practice across numerous industries. Ever since the emergence of large language models (LLMs), their potential to generate formative feedback for introductory programming courses has been extensively researched. However, most of these studies have focused on Python. In this work, we examine the bug-fixing and feedback-generation abilities of Code Llama and ChatGPT for Java programming assignments using our new Java benchmark called CodeWBugs. The results indicate that ChatGPT performs reasonably well, and was able to fix 94.33% of programs. By comparison, we observed high variability in the results from Code Llama. We further analyzed the impact of different types of prompts and observed that prompts that included task descriptions and test inputs yielded better results. In most cases, the LLMs precisely localized the bugs and also offered guidance on how to proceed. Nevertheless, we also noticed incorrect responses generated by the LLMs, emphasizing the need to validate responses before disseminating feedback to learners

    Multiscale Upscaling Study for CO<sub>2</sub> Storage in Carbonate Rocks Using Machine Learning and Multiscale Imaging

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    Microporosity is commonly assumed to be non-connected porosity and not commonly studied in geoengineering industry. However, the presence of micropores plays a key role in connecting macropores and it can contribute significantly to the overall flow performance. In this study, targeted CO2 storage carbonate fields in Southeast Asia have significant amounts of microporosity ranging from 10 to 60% of the total measured porosity. Microporosity can only be seen in high resolution images. To study the unresolved and the resolved microporosity, Middle Miocene carbonate samples from CO2 storage candidate fields were scanned using lower resolution micro-computed micro-tomography (micro-CT) and higher resolution synchrotron light source to understand the pore scale structure of the carbonate sample at different length scales. This paper proposes a proof-of-concept upscaling method that integrates multiscale 3D imaging techniques and trendline analysis to establish porosity-permeability relationships with microporosity insight. After image acquisition and processing, the images were divided into smaller sub-volumes. Pore-scale modelling was conducted to predict the permeability using Darcy-Brinkman-Stokes (DBS) model. Then, a nano-scale porosity-permeability transform is generated using natural log trendline fitting based on simulation results. The porosity-permeability transform is further extended to three cases to cover the low case, mid case, and high case of datapoint fittings and is further validated with laboratory measured data. The established porosity-permeability transforms in this study have been applied to compare with petrophysical derived porosity-permeability transforms with better performance (higher R2 value) for low permeability datapoint. The multiscale imaging upscaling workflow has integrated machine learning during image segmentation with pore-scale modelling and trendline fitting during the upscaling study. It emphasises the importance of seeing the unseen (unresolved microporous phase) to understand the internal texture and microstructure of a rock sample in order to understand the connectivity of the overall flow performance in a carbonate rock.</p

    Dataset for the Heron Core in "Heron: Modern Hardware Graph Reduction"

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    This dataset contains the full source code for the processor design presented in the paper "Heron: Modern Hardware Graph Reduction". It includes the compiler, emulator, hardware description, and FPGA designs for the Heron Core

    NiReMS: A regional model at household level combining spatial econometrics with dynamic microsimulation

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    The heterogeneous spatial and individual impacts of the Great Recession, Brexit and COVID-19 have generated an important challenge for macroeconomic and regional/spatial modellers to consider greater integration of their approaches. Focusing on agent heterogeneity at the ITL 1 level in the UK, we propose the National Institute Regional Modelling System (NiReMS) – a synthesis of dynamic microsimulation with a spatial regional macroeconometric model. The model gives regional macro projections while allowing for household level inference. To showcase the model, we explore the impact of discontinuing the uplift in Universal Credit (UC) before the end of the pandemic and show that it led to more households consuming less. Importantly, the proposed framework highlights the unequal distributional impact across regions of the UK

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