7103 research outputs found
Sort by
A computational approach to the sizing of heat pump integrated thermal energy storage systems for wet central heating
Full text linkIntegrating heat pumps with a TES system provides a suitable and efficient solution for replacing a gas-fired boiler within a wet central heating system for residential buildings. This approach addresses the growing demand for energy-efficient and low-carbon technologies. The system enhances the efficacy of residential heating by shifting energy consumption to off-peak periods and improving heat distribution during peak demand. This study assesses the feasibility of a phase change-based TES system for load-shifting capabilities in residential heating. The study explores the application of CFD and building simulation models to evaluate the performance of a heat pump-integrated TES system in meeting the full space heating needs of a detached dwelling. The engineering modelling method was employed to estimate the next-day heat demand for a dwelling using the ESP-r tool. This approach facilitates comprehensive simulations that considers the building's physical and thermal characteristics, construction materials, occupancy patterns and weather data to predict the next-day space heat demand of the dwelling. This predictive capability provides a robust foundation for sizing a TES system. A phase change TES model was developed using the enthalpy formulation method, with the thermal store modelled in the Fluent CFD tool. This involved a single shelland-tube heat exchanger, comprising PCM in the annular-gap and HTF flowing through the tube. The TES model was validated by comparing its simulated fluid outlet temperature and temperature profiles at different positions with the results reported by Longeon et al. [186]. The results showed that the simulated data closely agreed with the experimental and numerical findings. This demonstrated that the CFD model can reliably predict the heat transfer characteristics and thermal behaviour of the TES system during the charging and discharging phases. The TES system sizing was demonstrated by utilising the dwelling’s peak day heat demand profile to determine the maximum storage capacity. which is then sufficient to meet any daily heat demand throughout the heating season. The thermal behaviour and characteristics of the TES system were evaluated by simulating the charging process of the thermal store using mass flow rates and temperature conditions representative of a typical ASHP. The results indicate that system efficiency depends on the type of PCM used, the heat pump’s mass flow rate, and the charging strategies employed. A linkage between the dwelling and TES system model was established using a rudimentary model to determine radiator return temperatures during TES utilisation (discharge). Simulating the discharge of the TES system using the radiator fluid outlet temperature at variable inlet mass flow rates provides the heat output, necessary to fulfil the space heating needs of the dwelling. The results obtained from discharging the TES system indicate a total heat output of 37.44 kWh, with peak and average discharge rates of 1.73 kW and 1.56 kW respectively used to match the detached dwelling’s peak day heat demand profile. A statistical analysis using the Pearson correlation coefficient shows a 94% match rate between the TES discharge rate and the dwelling heat demand profiles, indicating that they are in close agreement. The TES system discharge achieved a fluid output temperature of 60–40˚C. The fluid output temperature obtained, can effectively meets the space heating needs of the dwelling when larger radiators are used. However, the result of the analysis further indicates that this fluid output temperature is well-suited for underfloor heating systems or radiators designed for low-output temperature applications, particularly when advanced control systems are employed to ensure thermal comfort in buildings. The study contributes to knowledge by developing a detailed procedure for sizing a heat pump-integrated TES system for wet central heating. The developed methodology was proved by assessing the load-shifting capability of the TES system in meeting the continuous full day space heating needs of the detached dwelling. The study further contributes to knowledge by developing the control procedure for charging and discharging the TES system.Integrating heat pumps with a TES system provides a suitable and efficient solution for replacing a gas-fired boiler within a wet central heating system for residential buildings. This approach addresses the growing demand for energy-efficient and low-carbon technologies. The system enhances the efficacy of residential heating by shifting energy consumption to off-peak periods and improving heat distribution during peak demand. This study assesses the feasibility of a phase change-based TES system for load-shifting capabilities in residential heating. The study explores the application of CFD and building simulation models to evaluate the performance of a heat pump-integrated TES system in meeting the full space heating needs of a detached dwelling. The engineering modelling method was employed to estimate the next-day heat demand for a dwelling using the ESP-r tool. This approach facilitates comprehensive simulations that considers the building's physical and thermal characteristics, construction materials, occupancy patterns and weather data to predict the next-day space heat demand of the dwelling. This predictive capability provides a robust foundation for sizing a TES system. A phase change TES model was developed using the enthalpy formulation method, with the thermal store modelled in the Fluent CFD tool. This involved a single shelland-tube heat exchanger, comprising PCM in the annular-gap and HTF flowing through the tube. The TES model was validated by comparing its simulated fluid outlet temperature and temperature profiles at different positions with the results reported by Longeon et al. [186]. The results showed that the simulated data closely agreed with the experimental and numerical findings. This demonstrated that the CFD model can reliably predict the heat transfer characteristics and thermal behaviour of the TES system during the charging and discharging phases. The TES system sizing was demonstrated by utilising the dwelling’s peak day heat demand profile to determine the maximum storage capacity. which is then sufficient to meet any daily heat demand throughout the heating season. The thermal behaviour and characteristics of the TES system were evaluated by simulating the charging process of the thermal store using mass flow rates and temperature conditions representative of a typical ASHP. The results indicate that system efficiency depends on the type of PCM used, the heat pump’s mass flow rate, and the charging strategies employed. A linkage between the dwelling and TES system model was established using a rudimentary model to determine radiator return temperatures during TES utilisation (discharge). Simulating the discharge of the TES system using the radiator fluid outlet temperature at variable inlet mass flow rates provides the heat output, necessary to fulfil the space heating needs of the dwelling. The results obtained from discharging the TES system indicate a total heat output of 37.44 kWh, with peak and average discharge rates of 1.73 kW and 1.56 kW respectively used to match the detached dwelling’s peak day heat demand profile. A statistical analysis using the Pearson correlation coefficient shows a 94% match rate between the TES discharge rate and the dwelling heat demand profiles, indicating that they are in close agreement. The TES system discharge achieved a fluid output temperature of 60–40˚C. The fluid output temperature obtained, can effectively meets the space heating needs of the dwelling when larger radiators are used. However, the result of the analysis further indicates that this fluid output temperature is well-suited for underfloor heating systems or radiators designed for low-output temperature applications, particularly when advanced control systems are employed to ensure thermal comfort in buildings. The study contributes to knowledge by developing a detailed procedure for sizing a heat pump-integrated TES system for wet central heating. The developed methodology was proved by assessing the load-shifting capability of the TES system in meeting the continuous full day space heating needs of the detached dwelling. The study further contributes to knowledge by developing the control procedure for charging and discharging the TES system
The impact of single-point mutations and IgG subclass on the developability of high concentration monoclonal antibody formulations
Full text linkPreviously held under moratorium in Chemistry Department (GSK) from 22nd November 2024 to 27th May 2025.The developability of therapeutic monoclonal antibodies (mAbs) is a growing field of research poised to increase the probability of successful clinical translation for early-phase mAb candidates. Developability assessments typically entail high-throughput, low volume biophysical assays with parallel in silico sequence and structure based predictions to scope manufacturing, safety and efficacy risks. These include colloidal and conformational stability, and solution viscosity, which impact formulation shelf-life, immunogenicity, and manufacturability risks. High viscosity presents challenges with increased filtration pressure and reduced recovery during processing steps, with implications for vial filling during manufacture, and injection failure during administration. The latter is a growing concern with the move to patient self-administration using subcutaneous devices for improved patient autonomy and adherence. The dose volume limitations in autoinjector device design and high dosing requirements for mAb potency further complicates viscosity associated risks in mAb solutions. In this thesis, the impact of single-point mutations introduced in IgG1 variable regions, and different mAb subclasses on viscosity and other biophysical developability properties was investigated. Chapter 2 investigated the use of computational molecular descriptors derived from homology constructs to engineer mutants and the role of solvent accessible surface potential in promoting mAb self-interactions was assessed. Mutations with significant reductions in hydrophobicity resulted in lower solution viscosity, and a lack of correlation with in silico descriptors demonstrate the need for case-by-case evaluation of mAbs. Whilst many studies explore the design of mutants to enhance viscosity, few demonstrate the impacts of these mutations on manufacturing process and critical quality attributes. Chapter 3 explored the manufacturability of the single-point Fv mutants, assessing upstream and downstream process observations as well as phase behaviour and process-related impurities. Significant modifications on mAb expression, required chromatography conditions, phase stability and post-translational modifications were observed and were mutation site-specific. Chapter 4 presented an insight into the reduced developability of an IgG3 relative to IgG1, with comparisons to in silico descriptors. This chapter targeted the knowledge gap in understanding the biophysical behaviour of the IgG3 subclass which holds unique therapeutic potential. The results in this chapter also demonstrate the impact of the constant domain sequence and structure on interactions governing viscosity. Overall IgG3 showed reduced developability, with increased viscosity, compared to the Fv-matched IgG1 ortholog assessed. Finally, the use of viscosity models in fitting and predicting formulation behaviour was evaluated in chapter 5. This chapter expanded upon interpretation limits of viscosity with regards to model-fit equation used and the concentration-dependence of viscosity, highlighting changes in contributing underlying mechanisms with increased molecular crowding. While low-concentration hydrodynamic parameters provided insights into such mechanisms, they poorly correlated with ultra-high concentration viscosity. Furthermore, the lack of generalisability of predictive models explored in this chapter highlights the necessity for machine-learning modelling to incorporate larger, diverse datasets for robust and accurate viscosity predictions of high concentration mAb formulations.
Overall, this thesis has provided a framework for the combined computational and experimental assessment of the biophysical behaviour of mAbs in high concentration formulations. Mutants designed from targeting computed surface patches were ineffective in reducing viscosity in the dose relevant concentration regime, so future works include combining mutations, exploring mechanistic contributions to viscosity further and use of machine-learning models for directed mutagenesis.The developability of therapeutic monoclonal antibodies (mAbs) is a growing field of research poised to increase the probability of successful clinical translation for early-phase mAb candidates. Developability assessments typically entail high-throughput, low volume biophysical assays with parallel in silico sequence and structure based predictions to scope manufacturing, safety and efficacy risks. These include colloidal and conformational stability, and solution viscosity, which impact formulation shelf-life, immunogenicity, and manufacturability risks. High viscosity presents challenges with increased filtration pressure and reduced recovery during processing steps, with implications for vial filling during manufacture, and injection failure during administration. The latter is a growing concern with the move to patient self-administration using subcutaneous devices for improved patient autonomy and adherence. The dose volume limitations in autoinjector device design and high dosing requirements for mAb potency further complicates viscosity associated risks in mAb solutions. In this thesis, the impact of single-point mutations introduced in IgG1 variable regions, and different mAb subclasses on viscosity and other biophysical developability properties was investigated. Chapter 2 investigated the use of computational molecular descriptors derived from homology constructs to engineer mutants and the role of solvent accessible surface potential in promoting mAb self-interactions was assessed. Mutations with significant reductions in hydrophobicity resulted in lower solution viscosity, and a lack of correlation with in silico descriptors demonstrate the need for case-by-case evaluation of mAbs. Whilst many studies explore the design of mutants to enhance viscosity, few demonstrate the impacts of these mutations on manufacturing process and critical quality attributes. Chapter 3 explored the manufacturability of the single-point Fv mutants, assessing upstream and downstream process observations as well as phase behaviour and process-related impurities. Significant modifications on mAb expression, required chromatography conditions, phase stability and post-translational modifications were observed and were mutation site-specific. Chapter 4 presented an insight into the reduced developability of an IgG3 relative to IgG1, with comparisons to in silico descriptors. This chapter targeted the knowledge gap in understanding the biophysical behaviour of the IgG3 subclass which holds unique therapeutic potential. The results in this chapter also demonstrate the impact of the constant domain sequence and structure on interactions governing viscosity. Overall IgG3 showed reduced developability, with increased viscosity, compared to the Fv-matched IgG1 ortholog assessed. Finally, the use of viscosity models in fitting and predicting formulation behaviour was evaluated in chapter 5. This chapter expanded upon interpretation limits of viscosity with regards to model-fit equation used and the concentration-dependence of viscosity, highlighting changes in contributing underlying mechanisms with increased molecular crowding. While low-concentration hydrodynamic parameters provided insights into such mechanisms, they poorly correlated with ultra-high concentration viscosity. Furthermore, the lack of generalisability of predictive models explored in this chapter highlights the necessity for machine-learning modelling to incorporate larger, diverse datasets for robust and accurate viscosity predictions of high concentration mAb formulations.
Overall, this thesis has provided a framework for the combined computational and experimental assessment of the biophysical behaviour of mAbs in high concentration formulations. Mutants designed from targeting computed surface patches were ineffective in reducing viscosity in the dose relevant concentration regime, so future works include combining mutations, exploring mechanistic contributions to viscosity further and use of machine-learning models for directed mutagenesis
Numerical investigation of ROV deployment onboard a small offshore service vessel for offshore wind farm O&M
Full text linkAimed at significant cost reduction and reducing the overall greenhouse gas (GHG) emission during the operation, the deployment of remotely operated underwater vehicle (ROV) from a small offshore service vessel (OSV) based on single point mooring system (SPMS) method is recently adopted in offshore renewable energy sector. However, the tension spike in wire, also known as snap load, often occurs when the ROV passes through the wave zone in launching and lifting operation of deployment. This study developed a coupled numerical model of ROV deployment onboard a small OSV positioned by a SPMS for subsea inspection of a fixed or floating offshore wind turbine. The numerical model for predicting wire tension during launch and recovery of ROV is developed and validated by wave flume test of a 1:10 scaled model. The numerical simulations reveal that the ROV deployment at vessel stern along with an appropriate reduction of horizontal distance from the hull are reliable safety strategies for reducing wire tension. By adopting the new deployment strategy, the annual operational capacity can be expanded by approximately 6% when the safe operational limit of ROV under a significant wave height of 1.25 m. Based on the comprehensive numerical simulation, the newly developed safe operating envelope provides a practical recommendation for onboard ROV operation in the operations and maintenance (O&M) of offshore wind farms. As a typical practical offshore operation involving multiple floating body dynamics, the dynamic response characteristics of umbilical cable of ROV, connecting lines of SPMS, and mooring lines of floating offshore wind turbines (FOWT) are investigated under the environmental conditions of the northern North Sea. The coupled numerical model was first validated against the maximum and average tension measurements of connecting lines obtained from full-scale OSV operations at sea for subsea inspection of two different fixed wind turbines. Numerical simulations of coupled OSV-FOWT system indicated that the dynamic tension in umbilical cable and mooring lines, primarily determining the environmental limits of ROV deployment and safe operation, is influenced by wind-wave misalignment and the relative distance between OSV and FOWT. The relationships and safe operational ranges of umbilical cable, connecting lines, and mooring lines were examined in detail to provide further guidance for onboard ROV operations in offshore wind farm maintenance.Aimed at significant cost reduction and reducing the overall greenhouse gas (GHG) emission during the operation, the deployment of remotely operated underwater vehicle (ROV) from a small offshore service vessel (OSV) based on single point mooring system (SPMS) method is recently adopted in offshore renewable energy sector. However, the tension spike in wire, also known as snap load, often occurs when the ROV passes through the wave zone in launching and lifting operation of deployment. This study developed a coupled numerical model of ROV deployment onboard a small OSV positioned by a SPMS for subsea inspection of a fixed or floating offshore wind turbine. The numerical model for predicting wire tension during launch and recovery of ROV is developed and validated by wave flume test of a 1:10 scaled model. The numerical simulations reveal that the ROV deployment at vessel stern along with an appropriate reduction of horizontal distance from the hull are reliable safety strategies for reducing wire tension. By adopting the new deployment strategy, the annual operational capacity can be expanded by approximately 6% when the safe operational limit of ROV under a significant wave height of 1.25 m. Based on the comprehensive numerical simulation, the newly developed safe operating envelope provides a practical recommendation for onboard ROV operation in the operations and maintenance (O&M) of offshore wind farms. As a typical practical offshore operation involving multiple floating body dynamics, the dynamic response characteristics of umbilical cable of ROV, connecting lines of SPMS, and mooring lines of floating offshore wind turbines (FOWT) are investigated under the environmental conditions of the northern North Sea. The coupled numerical model was first validated against the maximum and average tension measurements of connecting lines obtained from full-scale OSV operations at sea for subsea inspection of two different fixed wind turbines. Numerical simulations of coupled OSV-FOWT system indicated that the dynamic tension in umbilical cable and mooring lines, primarily determining the environmental limits of ROV deployment and safe operation, is influenced by wind-wave misalignment and the relative distance between OSV and FOWT. The relationships and safe operational ranges of umbilical cable, connecting lines, and mooring lines were examined in detail to provide further guidance for onboard ROV operations in offshore wind farm maintenance
Image processing and machine learning applications in lung cancer treatment
Full text linkThe continued advancement of image processing and machine learning techniques opens up the opportunity for their application in the medical setting. The aim of the work in this thesis was to apply these techniques from this broad field to lung cancer treatment with the aim of providing tools that can improve patient outcomes. The topics covered were; pulmonary and esophageal toxicity following radiotherapy, registration of PET/CT imaging to pathology and the automatic segmentation of tumour regions in gross pathology images. For the prediction of pulmonary toxicity, predictive features were extracted from pre-treatment planning CT images using radiomic and deep learning based approaches. When combined with dose features, these models produced a large increase in predictive power compared to models using only dose and clinical features. For the ILD patients receiving SABR, predictive power was also shown on several metrics such as the FACT-L and EQ-5D-5L scales. For predicting esophageal toxicity, the data from the RTOG0617 clinical trial was used. Here the focus was on improving predictions from the dose maps. It was found that using 3D-CNNs, regression based training, including additional toxicities and ensembling models improved model performance. Tests were
also conducted to determine the robustness of boosted decision tree and artificial neural network based models for esophageal toxicity prediction by adding noise to the test data. The PET/CT to pathology registration task followed on from a previous project that built the framework for registering CT to pathology but failed to include PET due to respiratory motion blurring. This was added to by including respiratory gating and the OncoFreeze algorithm in the workflow to reduce the effects of respiratory motion. A PET to pathology registration was evaluated using thresholding based registration of the PET image. Additionally, a deep learning based method for the automatic segmentation of gross pathology images was produced. This included training and testing various UNet and DeeplabV3+ models with both Dice and cross entropy based loss functions. The best performing model was an ensemble of several models with morphological post processing steps.The continued advancement of image processing and machine learning techniques opens up the opportunity for their application in the medical setting. The aim of the work in this thesis was to apply these techniques from this broad field to lung cancer treatment with the aim of providing tools that can improve patient outcomes. The topics covered were; pulmonary and esophageal toxicity following radiotherapy, registration of PET/CT imaging to pathology and the automatic segmentation of tumour regions in gross pathology images. For the prediction of pulmonary toxicity, predictive features were extracted from pre-treatment planning CT images using radiomic and deep learning based approaches. When combined with dose features, these models produced a large increase in predictive power compared to models using only dose and clinical features. For the ILD patients receiving SABR, predictive power was also shown on several metrics such as the FACT-L and EQ-5D-5L scales. For predicting esophageal toxicity, the data from the RTOG0617 clinical trial was used. Here the focus was on improving predictions from the dose maps. It was found that using 3D-CNNs, regression based training, including additional toxicities and ensembling models improved model performance. Tests were
also conducted to determine the robustness of boosted decision tree and artificial neural network based models for esophageal toxicity prediction by adding noise to the test data. The PET/CT to pathology registration task followed on from a previous project that built the framework for registering CT to pathology but failed to include PET due to respiratory motion blurring. This was added to by including respiratory gating and the OncoFreeze algorithm in the workflow to reduce the effects of respiratory motion. A PET to pathology registration was evaluated using thresholding based registration of the PET image. Additionally, a deep learning based method for the automatic segmentation of gross pathology images was produced. This included training and testing various UNet and DeeplabV3+ models with both Dice and cross entropy based loss functions. The best performing model was an ensemble of several models with morphological post processing steps
Enhancing safety and human reliability through data-driven and NLP innovations
Full text linkThis thesis explores the development and application of innovative algorithmic, data-driven, machine learning, and natural language processing tools designed to enhance human reliability analysis in critical sectors such as nuclear power, aviation, and oil and gas. Motivated by identified challenges and opportunities in these industries, a suite of advanced tools was created to address key aspects of safety analysis and management. Presented in this work are six tools, the first- and second- generation Virtual Human Factors Classifiers, the Human-Centric Summarizer, the High-Potential Violation Trigger Identification tool, the Ambiguity Identifier and finally the Human Factors Causal Relationships tool. The Virtual Human Factors Classifiers were designed to automatically read analyze accident reports to classify the contributing factors. The primary motivation for this development was the expansion of a human reliability analysis database (MATA-D, Multiattribute Technological Accidents Dataset), to provide the additional data necessary to address the issue of missing information and reduce the uncertainty of human error probability models. The tools have also demonstrated additional applications such as aiding
assessors in their reviews of accidents and informing the procedure design process.
Complimentarily the Human-Centric Summarizer was developed to distill lengthy accident reports into high-quality concise summaries, that emphasize the human role in the incident. The summarizer serves a dual purpose. Firstly, it aids researchers and safety professionals in rapidly grasping each report, as well as any models based on the incident, without delving into the pages of detailed reports. Secondly, it assists in maintaining and updating the MATA-D. The summaries generated provide a quick reference to the key points of each incident, facilitating easier analysis and review of performance shaping factor classification. In high-risk industrial environments, the clarity and accuracy of standard operating procedures are critical for ensuring safety and regulatory compliance. The presence of ambiguities in standard operating procedures can lead to misunderstandings, errors, and
increased risks. While violations of procedural directives can significantly contribute to catastrophic outcomes. To address these issues, two additional tools are introduced that leverage both rule-based and machine learning methodologies in natural language processing to evaluate the quality of standard operating procedure documents. The High-Potential Violation Trigger Identification tool identifies directives within procedural guides that when violated pose a high-risk potential. And the Ambiguity Identifier has been designed to detect various types of ambiguities and misleading steps within procedure guides. By addressing these linguistic and procedural discrepancies, the tools aim to enhance the clarity and applicability of standard operating procedures, ultimately improving adherence and reducing risks in complex operational settings. The final tool presented in this work is the Human Factors Causal Relationships Tool. It leverages data collected through the MATA-D to identify causal relationships among performance shaping factors. This tool is designed to reduce reliance on expert judgment in the development of human error models, thereby helping to mitigate concerns related to subjectivity and bias.
Case studies are presented for each tool, demonstrating their real-world utility and effectiveness in critical industry contexts. This thesis highlights the potential of data-driven and natural language processing approaches to revolutionize human reliability analysis practices, ultimately enhancing safety across critical industries.This thesis explores the development and application of innovative algorithmic, data-driven, machine learning, and natural language processing tools designed to enhance human reliability analysis in critical sectors such as nuclear power, aviation, and oil and gas. Motivated by identified challenges and opportunities in these industries, a suite of advanced tools was created to address key aspects of safety analysis and management. Presented in this work are six tools, the first- and second- generation Virtual Human Factors Classifiers, the Human-Centric Summarizer, the High-Potential Violation Trigger Identification tool, the Ambiguity Identifier and finally the Human Factors Causal Relationships tool. The Virtual Human Factors Classifiers were designed to automatically read analyze accident reports to classify the contributing factors. The primary motivation for this development was the expansion of a human reliability analysis database (MATA-D, Multiattribute Technological Accidents Dataset), to provide the additional data necessary to address the issue of missing information and reduce the uncertainty of human error probability models. The tools have also demonstrated additional applications such as aiding
assessors in their reviews of accidents and informing the procedure design process.
Complimentarily the Human-Centric Summarizer was developed to distill lengthy accident reports into high-quality concise summaries, that emphasize the human role in the incident. The summarizer serves a dual purpose. Firstly, it aids researchers and safety professionals in rapidly grasping each report, as well as any models based on the incident, without delving into the pages of detailed reports. Secondly, it assists in maintaining and updating the MATA-D. The summaries generated provide a quick reference to the key points of each incident, facilitating easier analysis and review of performance shaping factor classification. In high-risk industrial environments, the clarity and accuracy of standard operating procedures are critical for ensuring safety and regulatory compliance. The presence of ambiguities in standard operating procedures can lead to misunderstandings, errors, and
increased risks. While violations of procedural directives can significantly contribute to catastrophic outcomes. To address these issues, two additional tools are introduced that leverage both rule-based and machine learning methodologies in natural language processing to evaluate the quality of standard operating procedure documents. The High-Potential Violation Trigger Identification tool identifies directives within procedural guides that when violated pose a high-risk potential. And the Ambiguity Identifier has been designed to detect various types of ambiguities and misleading steps within procedure guides. By addressing these linguistic and procedural discrepancies, the tools aim to enhance the clarity and applicability of standard operating procedures, ultimately improving adherence and reducing risks in complex operational settings. The final tool presented in this work is the Human Factors Causal Relationships Tool. It leverages data collected through the MATA-D to identify causal relationships among performance shaping factors. This tool is designed to reduce reliance on expert judgment in the development of human error models, thereby helping to mitigate concerns related to subjectivity and bias.
Case studies are presented for each tool, demonstrating their real-world utility and effectiveness in critical industry contexts. This thesis highlights the potential of data-driven and natural language processing approaches to revolutionize human reliability analysis practices, ultimately enhancing safety across critical industries
Probing the biology of zinc alpha2-glycoprotein
Full text linkZinc Alpha 2-Glycoprotein (AZGP1;ZAG) is a ~40-kDa single-chain polypeptide protein
thought to contribute to the regulation of weight and body fat through lipid and glucose
metabolism. In healthy individuals, ZAG exerts a homeostatic effect by inducing lipolysis of
adipose tissue to help reduce fat storage and overall weight. ZAG is upregulated in various
carcinomas; cancer patients with upregulated ZAG lose weight rapidly and there is a clear link
between ZAG and cancer cachexia. The crystal structure of ZAG revealed an MHC-Class Ilike protein fold which has been proposed to act as a potential lipid binding site that could be
important for ZAG’s function. The work in this thesis aimed to produce an in silico docking approach to identify ligand(s) which may bind this groove, and to understand the key interactions between them and ZAG. Using PLANTS software and the protein visualisation software UCSF Chimera, we identified candidate ligands for binding in ZAGs pocket that have equalled or outscored previous identified ligands which can now be incorporated into competition binding assays to test their affinity for ZAG. In parallel, we developed approaches to produce recombinant ZAG using bacterial and mammalian expression systems, confirmed their structure using fluorescence spectroscopy and outline initial work to show that the recombinant protein is functional and can promote lipolysis in fat cells. Because a key target of ZAG action is adipocytes, methods were developed to utilise Stimulated Raman Scattering (SRS) to provide a workflow that allows for a label-free, high throughput, single cell analysis to investigate heterogeneric metabolic activity of adipocytes. Specifically, the approach described allows measurement of lipid droplet numbers, size and quantifying glucose metabolism using glucose-d7. We have demonstrated the validity of our method in 3T3-L1 adipocytes incubated in culture for different periods as a model of adipocyte
hypertrophy. Lastly, we adapted a mitochondrial isolation assay to separate cytosolic and
peridroplet mitochondria from 3T3-L1 adipocytes. In sum, this study provided tools and insight to help progress the knowledge and understanding of ZAG biology, signalling mechanisms and for the investigation of both phenotypic and metabolic adipocyte heterogeneity.Zinc Alpha 2-Glycoprotein (AZGP1;ZAG) is a ~40-kDa single-chain polypeptide protein
thought to contribute to the regulation of weight and body fat through lipid and glucose
metabolism. In healthy individuals, ZAG exerts a homeostatic effect by inducing lipolysis of
adipose tissue to help reduce fat storage and overall weight. ZAG is upregulated in various
carcinomas; cancer patients with upregulated ZAG lose weight rapidly and there is a clear link
between ZAG and cancer cachexia. The crystal structure of ZAG revealed an MHC-Class Ilike protein fold which has been proposed to act as a potential lipid binding site that could be
important for ZAG’s function. The work in this thesis aimed to produce an in silico docking approach to identify ligand(s) which may bind this groove, and to understand the key interactions between them and ZAG. Using PLANTS software and the protein visualisation software UCSF Chimera, we identified candidate ligands for binding in ZAGs pocket that have equalled or outscored previous identified ligands which can now be incorporated into competition binding assays to test their affinity for ZAG. In parallel, we developed approaches to produce recombinant ZAG using bacterial and mammalian expression systems, confirmed their structure using fluorescence spectroscopy and outline initial work to show that the recombinant protein is functional and can promote lipolysis in fat cells. Because a key target of ZAG action is adipocytes, methods were developed to utilise Stimulated Raman Scattering (SRS) to provide a workflow that allows for a label-free, high throughput, single cell analysis to investigate heterogeneric metabolic activity of adipocytes. Specifically, the approach described allows measurement of lipid droplet numbers, size and quantifying glucose metabolism using glucose-d7. We have demonstrated the validity of our method in 3T3-L1 adipocytes incubated in culture for different periods as a model of adipocyte
hypertrophy. Lastly, we adapted a mitochondrial isolation assay to separate cytosolic and
peridroplet mitochondria from 3T3-L1 adipocytes. In sum, this study provided tools and insight to help progress the knowledge and understanding of ZAG biology, signalling mechanisms and for the investigation of both phenotypic and metabolic adipocyte heterogeneity
Thresholds for patterns in random compositions and random permutations
Full text linkWe explore how the asymptotic structure of a random permutation of [n] with m
inversions evolves, as m increases, establishing thresholds for the appearance and disappearance of any classical, consecutive or vincular pattern. Our investigation begins
with exploring how the asymptotic structure of a random n-term weak integer composition of m evolves, as m increases from zero. The primary focus of our investigation
into compositions is establishing thresholds for the appearance and disappearance of
substructures, particularly the appearance and disappearance of consecutive composition patterns. We are then able to transfer the established composition threshold
to establish the thresholds for classical, consecutive or vincular permutation patterns
occurring within our random permutation model.
This thesis is based on the papers [12] and [13].We explore how the asymptotic structure of a random permutation of [n] with m
inversions evolves, as m increases, establishing thresholds for the appearance and disappearance of any classical, consecutive or vincular pattern. Our investigation begins
with exploring how the asymptotic structure of a random n-term weak integer composition of m evolves, as m increases from zero. The primary focus of our investigation
into compositions is establishing thresholds for the appearance and disappearance of
substructures, particularly the appearance and disappearance of consecutive composition patterns. We are then able to transfer the established composition threshold
to establish the thresholds for classical, consecutive or vincular permutation patterns
occurring within our random permutation model.
This thesis is based on the papers [12] and [13]
Decoding negative treatment of self : comprehensive measurement and diverse presentations in socially anxious clients
Full text linkNavigating the landscape of self and emotion, and bridging the experience of
self in relation to others, emotion-focused therapy (EFT) is a humanistic-experiential
psychotherapy that has demonstrated efficacy in treating depression (Greenberg et al.,
1990; 1998) and social anxiety (SA; Elliott et al., 2013). At the heart of social anxiety lie
numerous conflicting self-identities, rooted in enduring feelings of inadequacy and
shame. Adopting a deleterious self-critical stance, the array and complexity of inimical
self-actions underscores the debilitating nature and therapeutic challenges of SA. While
existing literature on the self-relationship has examined the global self-concept and
constructs such as perfectionism and self-criticism, there remains a significant gap in
comprehensively understanding and effectively measuring negative treatment of self
(NTS).
Drawing on archival data from SA clients undergoing EFT, this three-part mixedmethod study aimed to achieve several objectives: (a) evaluating the reliability and
validity of the Self-Relationship Questionnaire (SRQ; Faur & Elliott, 2007); (b)
comprehensively mapping the manifestations of NTS within-therapy discourse; (c)
testing and validating the rational-empirical model of NTS proposed by Capaldi and
Elliott (2023); and (d) exploring the amelioration of NTS observed by the conclusion of
therapy.
The findings confirmed the SRQ as a reliable and valid instrument for assessing
the self-relationship. The analysis extended beyond mapping the nuances of NTS
therapy discourse, exploring its multifaceted dimensions, including self-dislike,
detrimental self-actions, and their emotional effects, providing comprehensive insights
into NTS. The empirical validation of the rational-empirical model of NTS was
supported and expanded upon. The observed decrease in NTS by therapy's end further
enhanced the model, highlighting significant improvements in client discourse about
the self-relationship.Navigating the landscape of self and emotion, and bridging the experience of
self in relation to others, emotion-focused therapy (EFT) is a humanistic-experiential
psychotherapy that has demonstrated efficacy in treating depression (Greenberg et al.,
1990; 1998) and social anxiety (SA; Elliott et al., 2013). At the heart of social anxiety lie
numerous conflicting self-identities, rooted in enduring feelings of inadequacy and
shame. Adopting a deleterious self-critical stance, the array and complexity of inimical
self-actions underscores the debilitating nature and therapeutic challenges of SA. While
existing literature on the self-relationship has examined the global self-concept and
constructs such as perfectionism and self-criticism, there remains a significant gap in
comprehensively understanding and effectively measuring negative treatment of self
(NTS).
Drawing on archival data from SA clients undergoing EFT, this three-part mixedmethod study aimed to achieve several objectives: (a) evaluating the reliability and
validity of the Self-Relationship Questionnaire (SRQ; Faur & Elliott, 2007); (b)
comprehensively mapping the manifestations of NTS within-therapy discourse; (c)
testing and validating the rational-empirical model of NTS proposed by Capaldi and
Elliott (2023); and (d) exploring the amelioration of NTS observed by the conclusion of
therapy.
The findings confirmed the SRQ as a reliable and valid instrument for assessing
the self-relationship. The analysis extended beyond mapping the nuances of NTS
therapy discourse, exploring its multifaceted dimensions, including self-dislike,
detrimental self-actions, and their emotional effects, providing comprehensive insights
into NTS. The empirical validation of the rational-empirical model of NTS was
supported and expanded upon. The observed decrease in NTS by therapy's end further
enhanced the model, highlighting significant improvements in client discourse about
the self-relationship
Assessment of membrane distillation modules for seawater desalination and oilfield-produced water treatment applications
Full text linkMembrane distillation (MD) is a technology that is emerging as a viable
alternative to traditional desalination techniques such as Reverse Osmosis, Multistage flash distillation, Multiple-effect distillation, Vapour-compression evaporation,
etc. The advantages of MD over conventional desalination technologies include
higher ionic rejection capacity, greater feasibility for high saline brine treatments,
ability to operate using low-grade heat energy, a single-stage process and remote
operation using renewable energy, among others.
In this dissertation, a parametric study was performed using experiments to
assess the feasibility of using direct contact membrane distillation (DCMD)
technology to desalinate saline water of different concentrations. The results showed
that the permeate flux increased to 37.1 L/m².h from 11.6 L/m².h when the
temperature was raised from 45 °C to 75 °C. Additionally, the permeate flux
decreased to 13.6 L/m2
.h from 27.3 L/m2
.h, and the reduction in flux was around
50% when the concentration of sodium chloride in the feed solution was increased
from 0% to 26%.
The experimental results obtained using oilfield-produced water were highly
encouraging. The permeate flux was 11.5 L/m².h and 12.5 L/m².h at 80 °C and 85
°C, respectively. The results indicate the enormous potential of DCMD to treat
hypersaline oilfield-produced water, with an overall rejection of salts above 99%. The
base-line technology is the DCMD technology.
This study also evaluated the viability of air-gap membrane distillation
(AGMD) and vacuum membrane distillation (VMD) for treating different types of
saline water (3.5%, 7%, 15% and 26% NaCl solutions), including Arabian Gulf
Seawater (AGS) and oilfield-produced water. AGMD experiments at different feed
temperatures found that increasing the test temperature from 70 °C to 85 °C
increased the permeate flux by 56.64%. In contrast, the VMD experiments showed
that increasing the feed temperature from 65 °C to 85 °C resulted in a 26.87%
increase in permeate flux. The results obtained from the experiments showed that
VMD performed better at higher feed concentrations, while AGMD was superior at
lower feed concentrations. The flow-rate experimental results showed that increasing
the flow rate from 1.3 to 2.0 litres per minute resulted in a 1.2-fold increase in
permeate flux for both configurations, with salt rejection close to 99.9% and unaffected by the feed flow rate. AGMD outperformed VMD at all flow rates, and the
increase in permeate flux with flow rate was similar for both configurations.
This study found lower gaps, i.e. air gap and vacuum space, were preferred in
AGMD and VMD configurations, respectively, as they showed good flux, potentially
due to the reduced effects of heat and mass-transfer mechanisms at smaller gaps.
The experimental results showed that AGMD and VMD processes were highly
efficient in treating oilfield-produced water and AGS, achieving high salt rejections as
high as 99.97%. The results showed that the tested membranes achieved salt
rejections as high as 99.97%, and the order of fluxes observed in the VMD
configuration was Polyvinylidene fluoride (PVDF) > Polypropylene (PP) >
Polytetrafluoroethylene (PTFE). In the AGMD configuration, the order of fluxes
observed was Polypropylene (PP) > Polyvinylidene fluoride (PVDF) >
Polytetrafluoroethylene (PTFE).
This study’s results will provide valuable insights into the potential applications
of AGMD and VMD processes in desalination, especially in regions where freshwater
resources are scarce or contaminated. The information gained from this study can
be used to optimise the performance of these processes, improve their costeffectiveness and energy efficiency, and enhance their viability as potential solutions
for addressing water scarcity and pollution issues. The study discussed in this
dissertation is the first to present laboratory-scale results of using AGMD and VMD
technologies to treat AGS and oilfield-produced water in Kuwait while considering
prevailing conditions. The findings of this study lay the groundwork for conducting
pilot-scale studies on Arabian Gulf Seawater and oilfield-produced water utilising
DCMD, AGMD and VMD technologies, not only in the Middle East region but
globally.Membrane distillation (MD) is a technology that is emerging as a viable
alternative to traditional desalination techniques such as Reverse Osmosis, Multistage flash distillation, Multiple-effect distillation, Vapour-compression evaporation,
etc. The advantages of MD over conventional desalination technologies include
higher ionic rejection capacity, greater feasibility for high saline brine treatments,
ability to operate using low-grade heat energy, a single-stage process and remote
operation using renewable energy, among others.
In this dissertation, a parametric study was performed using experiments to
assess the feasibility of using direct contact membrane distillation (DCMD)
technology to desalinate saline water of different concentrations. The results showed
that the permeate flux increased to 37.1 L/m².h from 11.6 L/m².h when the
temperature was raised from 45 °C to 75 °C. Additionally, the permeate flux
decreased to 13.6 L/m2
.h from 27.3 L/m2
.h, and the reduction in flux was around
50% when the concentration of sodium chloride in the feed solution was increased
from 0% to 26%.
The experimental results obtained using oilfield-produced water were highly
encouraging. The permeate flux was 11.5 L/m².h and 12.5 L/m².h at 80 °C and 85
°C, respectively. The results indicate the enormous potential of DCMD to treat
hypersaline oilfield-produced water, with an overall rejection of salts above 99%. The
base-line technology is the DCMD technology.
This study also evaluated the viability of air-gap membrane distillation
(AGMD) and vacuum membrane distillation (VMD) for treating different types of
saline water (3.5%, 7%, 15% and 26% NaCl solutions), including Arabian Gulf
Seawater (AGS) and oilfield-produced water. AGMD experiments at different feed
temperatures found that increasing the test temperature from 70 °C to 85 °C
increased the permeate flux by 56.64%. In contrast, the VMD experiments showed
that increasing the feed temperature from 65 °C to 85 °C resulted in a 26.87%
increase in permeate flux. The results obtained from the experiments showed that
VMD performed better at higher feed concentrations, while AGMD was superior at
lower feed concentrations. The flow-rate experimental results showed that increasing
the flow rate from 1.3 to 2.0 litres per minute resulted in a 1.2-fold increase in
permeate flux for both configurations, with salt rejection close to 99.9% and unaffected by the feed flow rate. AGMD outperformed VMD at all flow rates, and the
increase in permeate flux with flow rate was similar for both configurations.
This study found lower gaps, i.e. air gap and vacuum space, were preferred in
AGMD and VMD configurations, respectively, as they showed good flux, potentially
due to the reduced effects of heat and mass-transfer mechanisms at smaller gaps.
The experimental results showed that AGMD and VMD processes were highly
efficient in treating oilfield-produced water and AGS, achieving high salt rejections as
high as 99.97%. The results showed that the tested membranes achieved salt
rejections as high as 99.97%, and the order of fluxes observed in the VMD
configuration was Polyvinylidene fluoride (PVDF) > Polypropylene (PP) >
Polytetrafluoroethylene (PTFE). In the AGMD configuration, the order of fluxes
observed was Polypropylene (PP) > Polyvinylidene fluoride (PVDF) >
Polytetrafluoroethylene (PTFE).
This study’s results will provide valuable insights into the potential applications
of AGMD and VMD processes in desalination, especially in regions where freshwater
resources are scarce or contaminated. The information gained from this study can
be used to optimise the performance of these processes, improve their costeffectiveness and energy efficiency, and enhance their viability as potential solutions
for addressing water scarcity and pollution issues. The study discussed in this
dissertation is the first to present laboratory-scale results of using AGMD and VMD
technologies to treat AGS and oilfield-produced water in Kuwait while considering
prevailing conditions. The findings of this study lay the groundwork for conducting
pilot-scale studies on Arabian Gulf Seawater and oilfield-produced water utilising
DCMD, AGMD and VMD technologies, not only in the Middle East region but
globally
Access Repository Dashboard
Do you manage STAX (Strathclyde Repository)? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!