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Applications of reservoir simulation and machine learning in subsurface energy systems for decarbonization
Get PDFThe transition to a low-carbon future necessitates innovative approaches to carbon management and hydrogen storage, particularly in the context of enhanced oil recovery (EOR) from hydrocarbon reservoirs. This study employs advanced analytics and machine learning techniques to optimize carbon management strategies. One key focus of this research is to evaluate the effectiveness of flue gas and CO2 in Water Alternating Gas (WAG) injection within a homogeneous fractured carbonate reservoir characterized by low porosity and permeability. A computational model was developed to depict the flow regime in the reservoir and simulate reservoir fluid behavior using Eclipse (E300) software, various hybrid EOR methods were evaluated, revealing that natural production accounted for only 29% of the total output. The optimized Hybrid EOR method achieved an impressive oil recovery factor of approximately 85%, demonstrating the critical need for EOR techniques to enhance overall production. In parallel, to mitigate greenhouse gas emissions caused by reliance on hydrocarbon resources, the integration simulation study of CO2 storage with EOR in fractured carbonate reservoirs is implemented to meet this pressing requirement. Utilizing the Eclipse simulator, various gas injection scenarios were modeled to assess the effectiveness of CO2 and flue gas geo-sequestration and EOR. Key findings revealed that flue gas demonstrated superior storage capacity (150 MMSCF) compared to CO2 (85 MMSCF) and maintained better reservoir pressure, while CO2 injection resulted in a higher oil recovery factor of 52% versus 36% for flue gas. Sensitivity analyses indicated that increased reservoir porosity, permeability, and injection rates enhanced gas storage capacity, although CO2 showed a normal distribution trend in permeability. In addition, further reservoir simulation study explores the synergistic relationship between flue gas compositions, reservoir characteristics, and injection rates, highlighting that flue gases with higher concentrations of CO2 and O2 significantly improve recovery factors. Key findings indicate that reservoir temperature, porosity, and permeability are vital factors influencing oil recovery, with CO2 injection consistently yielding the highest recovery rates. Notably, the study established that flue gas injection demonstrated greater sensitivity to increased injection rates, with specific flue gas compositions enhancing recovery efficiency.
On another facet of advanced analytical techniques, a data-driven framework for site screening of offshore CO2 storage is introduced, which integrates diverse geospatial data with expert-weighted criteria to identify optimal locations for Carbon Capture, Utilization, and Storage (CCUS) projects. Machine learning algorithms, particularly Deep Neural Networks (DNN), were employed to enhance predictive accuracy in site selection, achieving an Average Absolute Percentage Difference (AAPD) of 1.486% and a Variance Accounted For (VAF) of 0.9937, thus bridging the gap between scientific inquiry and practical application. This approach not only enhances the precision of site selection but also exemplifies the transformative potential of machine learning in advancing carbon management strategies. The Deep Neural Network (DNN) algorithm proved to be the most effective tool for predicting site suitability, achieving accuracy rates exceeding 90% across various performance metrics.
In response to the escalating global energy demands and the transition to a low-carbon future, this study presents an advanced analytics framework aimed at hydrogen storage potential through machine learning methodologies. Hydrogen is emerging as a vital clean energy vector, and efficient underground storage in geological formations is essential for maintaining energy security. However, challenges remain in understanding the interactions between gas and rock. To tackle these challenges, a comprehensive dataset of 1,045 entries and over 5,200 data points was utilized to develop predictive models for contact angles in water-hydrogen-rock systems. Various machine learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Feedforward Deep Neural Network (FNN), and Recurrent Deep Neural Network (RNN) were evaluated, with the FNN achieving the highest predictive accuracy. This capability is crucial for assessing hydrogen flow through porous media during underground storage.
Overall, this thesis highlights the potential of advanced analytics and machine learning in refining carbon management practices and optimizing hydrogen storage capabilities. It provides essential insights for the energy sector\u27s sustainable transition, demonstrating that integrating CO2 storage with EOR is a viable strategy for reducing greenhouse gas emissions while enhancing oil recovery metrics. The findings underscore the importance of advanced analytics in addressing key challenges in the energy sector and promoting sustainable practices for a low-carbon future, paving the way for future research in these critical areas
From characterisation to strategy: A comprehensive review of fouling in dairy ultrafiltration and microfiltration
No full textMembrane-based separation technologies like ultrafiltration (UF) and microfiltration (MF) are widely used for concentrating and separating proteins and other milk components. Despite significant advancements, fouling remains a major challenge, increasing operational costs, processing time, and energy consumption. This review thoroughly examines fouling characterisation in milk UF and MF processes, focusing on the interplay between membrane properties, operating conditions and solution properties. The review highlights the critical need for effective fouling mitigation strategies. It provides an in-depth analysis of fouling properties, factors influencing filtration, and advanced techniques for characterisation. The discussion also addresses the optimisation of cleaning procedures, emphasising the importance of understanding the chemical and morphological characteristics of fouling to develop tailored cleaning protocols. Such customised approaches can result in reduced standard cleaning sequences, leading to the conservation of water and chemicals. The review suggests future research directions, emphasising the importance of collaboration between dairy processors and cleaning agent suppliers to enhance cleaning strategies. It highlights the necessity of employing multiple analytical techniques to comprehensively understand fouling, linking it with filtration performance data from real processes
A predictive model for the shear capacity of ultra-high-performance concrete deep beams reinforced with fibers using a hybrid ANN-ANFIS algorithm
Get PDFUltra-high-performance concrete (UHPC) has attracted considerable attention from both the construction industry and researchers due to its outstanding durability and exceptional mechanical properties, particularly its high compressive strength. Several factors influence the shear capacity of UHPC deep beams, including compressive strength, the shear span-to-depth ratio (λ), fiber content (FC), vertical web reinforcement (ρsv), horizontal web reinforcement (ρsh), and longitudinal web reinforcement (ρs). Considering these factors, this research proposes a novel hybrid algorithm that combines an adaptive neuro-fuzzy inference system (ANFIS) with an artificial neural network (ANN) to predict the shear capacity of UHPC deep beams. To achieve this, ANN and ANFIS algorithms were initially employed individually to predict the shear capacity of UHPC deep beams using available experimental data for training. Subsequently, a novel hybrid algorithm, integrating an ANN and ANFIS, was developed to enhance prediction accuracy by utilizing numerical data as input for training. To evaluate the accuracy of the algorithms, the performance metrics R2 and RMSE were selected. The research findings indicate that the accuracy of the ANN, ANFIS, and the hybrid ANN-ANFIS algorithm was observed as R2 = 0.95, R2 = 0.99, and R2 = 0.90, respectively. This suggests that despite not using experimental data as input for training, the ANN-ANFIS algorithm accurately predicted the shear capacity of UHPC deep beams, achieving an accuracy of up to 90.90% and 94.74% relative to the ANFIS and ANN algorithms trained on experimental results. Finally, the shear capacity of UHPC deep beams predicted using the ANN, ANFIS, and the hybrid ANN-ANFIS algorithm was compared with the values calculated based on ACI 318-19. Subsequently, a novel reliability factor was proposed, enabling the prediction of the shear capacity of UHPC deep beams reinforced with fibers with a 0.66 safety margin compared to the experimental results. This indicates that the proposed model can be effectively employed in real-world design applications
Co-designing health-related digital tools with children: A scoping review of current practice
Get PDFAn increased focus on research with rather than on or of participants provides challenges with the implementation of such research with children. This extends to participatory practices in which co-design is implemented towards developing a technology-based product or solving a problem particularly in the domain of health literacy. This systematic scoping review aimed to examine the practices of co-design with children to inform an interdisciplinary research team as they embarked on the development of a digital health literacy tool for young learners. While there were limited sources identified in the review (n = 11), it was ascertained the process of co-design is not clearly understood by all, and most research described implementation with older children or youth. A range of methods for co-design were identified, and the importance of an interdisciplinary approach was highlighted. Based on these findings, recommendations are made for successful co-design with young children towards digital products or solutions to problems that can be applied in health and other fields
Consumers\u27 perspectives on the design of a new digital frailty education course, ‘Focus on Frailty’: A qualitative co-design study
Get PDFIntroduction: Frailty-focused care in hospitals is hindered by systemic barriers, ageism and stereotypes about older adults and frailty. There is a need for frailty education to increase healthcare professionals\u27 and students\u27 understanding of frailty. Objective: As part of a larger study to co-design a new digital frailty education course, ‘Focus on Frailty’, this study aimed to explore consumers\u27 and caregivers\u27 perspectives on (i) how frailty and older adults should be represented in frailty education and (ii) what healthcare professionals should be taught about caring for older adults and people who are frail in hospitals. Design: This was a qualitative co-design study. Setting and Participants: Participants (n = 25) were older Australians, people living with frailty and family caregivers (collectively, ‘consumers’) who had interacted with the hospital system. This study was conducted in Australia via Zoom and telephone. Methods: Participants engaged in focus groups or individual interviews and completed a demographic questionnaire and a Research Engagement Feedback Survey. Qualitative data were inductively analysed using template analysis (codebook thematic analysis). Quantitative demographic data were analysed using descriptive statistics. Results: Seven themes were identified: (1) Consumers\u27 understanding of frailty as loss, deterioration and vulnerability; (2) Utilise a holistic approach to frailty care; (3) Dispel stereotypes; (4) Value consumers\u27 lived experience expertise; (5) Include diverse representation and educate for diversity; (6) Promote meaningful interactions; and (7) Practice care coordination. Discussion: Participants acknowledged the multifaceted nature of frailty, advocating for holistic frailty education that considers physical, social, emotional, cognitive, financial and spiritual aspects. They described the importance of representing real-world scenarios and stories, images and videos of real people that reflected the diversity of lived experience. Participants wanted ‘Focus on Frailty’ to include education on individualised care; looking beyond the acute situation; multidisciplinary care coordination that involved informal caregivers; overcoming stereotypes and ageism; and meaningfully interacting with older adults and people who are frail. Conclusions: Consumers wanted to be represented in frailty education in a way that elevates lived experience and celebrates diversity. They expressed that healthcare professionals should be taught to avoid stereotypes, coordinate multidisciplinary care and engage in meaningful interactions with patients. Consumer-focused recommendations for designing frailty education were generated. Patient or Public Contribution: E.M., a consumer partner, contributed to the study design, focus group/interview guide, ethics application and participant information and consent forms. E.M. attended some of the focus groups and contributed to the interpretation of study findings. She also contributed to manuscript revisions. Twenty-five consumers (family caregivers, older adults and people with lived experience of frailty) participated in focus groups and interviews. Participants shared their perspectives on frailty and contributed to the co-design of a new digital frailty education course for healthcare professionals and students
Development and feasibility of a driving training program for Autistic student drivers
Get PDFDriving licencing rates remain lower for autistic individuals capable of driving a motor vehicle, which can limit achieving independence in community mobility. However, there is limited autism-specific guidance in current driver training. The development and evaluation of the feasibility of an autism-specific Driving Training Program (DTP) intervention was conducted to improve the likelihood that autistic student drivers will safely and successfully learn to drive a motor vehicle and gain a driver’s licence. The DTP intervention was developed using a modified stepped approach for developing complex skills-based interventions. The Goals for Driving Education framework for explaining driving training behaviour modification formed the foundation of the intervention. A small-scale study was conducted using a single group pre-post-test design (n=5), followed by semi-structured interviews and a survey (n=12) to evaluate the feasibility of intervention components and participant acceptability. The driving performance of the autistic student drivers significantly improved, demonstrating the feasibility of the DTP intervention for training autistic student drivers to learn to drive. Participants also found the intervention acceptable, with program component refinement suggested. The DTP intervention is feasible for a larger randomised controlled trial after modifying highlighted program components
From waste to worth: Advances in energy recovery technologies for solid waste management
Get PDFAbstract: Clean, inexpensive, and renewable energy sources with zero adverse environmental impact are essential for long-term sustainability. Implementing waste-to-energy (WtE) technologies has been suggested to improve solid waste management and promote the development of clean and sustainable urban environments. This involves the retrieval of waste materials and their conversion to electricity. By 2050, the global rate of Municipal Solid Waste (MSW) production is anticipated to rise to 2.01 billion tonnes annually. This study evaluated various WtE technologies that have been developed to date. These technologies can be categorized into three groups: thermochemical methods (incineration, pyrolysis, and gasification), biochemical methods (anaerobic digestion and landfilling), and hybrid waste-to-energy systems. Additionally, the discussion touched upon various environmental aspects, highlighting the advantages of reducing COX, NOX, SOX, furans, and dioxin emissions. Furthermore, this study thoroughly describes the economic impact of various steps on a WtE plant. It also discusses policy and regulatory frameworks, namely availability, affordability, rights, social aspects, and environmental issues, that aim to incorporate principles of ethics, justice, planning, and decision-making when evaluating different aspects of energy systems
Networked multi-agent deep reinforcement learning framework for the provision of ancillary services in hybrid power plants
Get PDFInverter-based resources (IBRs) are becoming more prominent due to the increasing penetration of renewable energy sources that reduce power system inertia, compromising power system stability and grid support services. At present, optimal coordination among generation technologies remains a significant challenge for frequency control services. This paper presents a novel networked multi-agent deep reinforcement learning (N—MADRL) scheme for optimal dispatch and frequency control services. First, we develop a model-free environment consisting of a photovoltaic (PV) plant, a wind plant (WP), and an energy storage system (ESS) plant. The proposed framework uses a combination of multi-agent actor-critic (MAAC) and soft actor-critic (SAC) schemes for optimal dispatch of active power, mitigating frequency deviations, aiding reserve capacity management, and improving energy balancing. Second, frequency stability and optimal dispatch are formulated in the N—MADRL framework using the physical constraints under a dynamic simulation environment. Third, a decentralised coordinated control scheme is implemented in the HPP environment using communication-resilient scenarios to address system vulnerabilities. Finally, the practicality of the N—MADRL approach is demonstrated in a Grid2Op dynamic simulation environment for optimal dispatch, energy reserve management, and frequency control. Results demonstrated on the IEEE 14 bus network show that compared to PPO and DDPG, N—MADRL achieves 42.10% and 61.40% higher efficiency for optimal dispatch, along with improvements of 68.30% and 74.48% in mitigating frequency deviations, respectively. The proposed approach outperforms existing methods under partially, fully, and randomly connected scenarios by effectively handling uncertainties, system intermittency, and communication resiliency
Performance health for saxophonists: Saxophonists perspectives and experiences with playing-related health, wellbeing, injuries and education (cohort study)
Get PDFSaxophonists experience a high rate of performance-related injuries and wellbeing concerns, with limited research on player perspectives and lived experience to inform preventive or interventional methods. To address this gap, a cohort study was conducted with 14 saxophonists from around Australia, to gather perspectives and experiences on playing-related pain, injury, health, and wellbeing. A baseline survey and semi-structured interviews were used to gather a range of qualitative and quantitative data to provide a holistic overview of the cohort and their lived experience. Results indicated that the lifetime prevalence of a performance-related musculoskeletal disorder (PRMD) for saxophonists in this cohort was 64%, with a point prevalence of 15%. Performance-related pain or injury was prevalent in the following locations: hands, wrists, forearms, back, shoulders, and embouchure, as is consistent with comparable studies. Prominent themes were identified from qualitative data that covered participants’ lived experience and perspectives of injuries and pain, performance anxiety, and education. This included sub-themes of accessing recovery, physical conditioning, health literacy, and education for music teachers, occupational concerns, health education during tertiary study, and accessing healthcare. Future research that explores the efficacy of methods for health promotion, performance anxiety, injury prevention, and saxophone performance may be useful in addressing concerns and improving the health and wellbeing of saxophonists
Promises and perils of generative artificial intelligence: A narrative review informing its ethical and practical applications in clinical exercise physiology
Get PDFGenerative Artificial Intelligence (GenAI) is transforming various sectors, including healthcare, offering both promising opportunities and notable risks. The infancy and rapid development of GenAI raises questions regarding its effective, safe, and ethical use by health professionals, including clinical exercise physiologists. This narrative review aims to explore existing interdisciplinary literature and summarise the ethical and practical considerations of integrating GenAI into clinical exercise physiology practice. Specifically, it examines the ‘promises’ of improved exercise programming and healthcare delivery, as well as the ‘perils’ related to data privacy, person-centred care, and equitable access. Recommendations for the responsible integration of GenAI in clinical exercise physiology are described, in addition to recommendations for future research to address gaps in knowledge. Future directions, including the roles and responsibilities of specific stakeholder groups are discussed, highlighting the need for clear professional guidelines in facilitating safe and ethical deployment of GenAI into clinical exercise physiology practice. Synthesis of current literature serves as an essential step in guiding strategies to ensure the safe, ethical, and effective integration of GenAI in clinical exercise physiology, providing a foundation for future guidelines, training, and research to enhance service delivery while maintaining high standards of practice