Theses - Chemical Engineering and Biotechnology


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  • ItemOpen Access
    A dynamic knowledge graph approach to self-driving chemical laboratories
    Bai, Jiaru; Bai, Jiaru [0000-0002-1246-1993]
    The contemporary design of self-driving laboratories faces difficulties in scalability and interoperability when it comes to the vision of a globally connected research network. This is due to heterogeneous data formats and resources as an obstacle to holistic integration. This thesis investigates a potential solution to the interoperability problem in chemical experiments by utilising a dynamic knowledge graph to unify the representation of data, software, hardware, and workflow. The developed approach is applied to a few selected case studies. To realise a self-driving chemical laboratory, the design-make-test-analyse cycle is first reformulated as the process of propagating information through a dynamic knowledge graph by means of a chain of actions. Our approach utilises ontologies to capture the data and material flows involved in the experimentation, and employs autonomous agents as executable knowledge components to carry out both computational and physical tasks. The iterative workflow is automatically managed by a derived information framework with data provenance semantically preserved following the FAIR principles – Findable, Accessible, Interoperable and Reusable. The derived information framework is also applied to an automated flood impact assessment in the smart cities domain to demonstrate its generalisability. On the computational front, the dynamic knowledge graph approach is applied to automate the calibration of a kinetic reaction mechanism, which demonstrated a reduction of calibration time from months when done manually to days while an increase in accuracy measured as a 79% decrease in objective function value. In the wet lab, we demonstrate the practical application by linking two robots in Cambridge and Singapore to achieve a collaborative closed-loop optimisation for an aldol condensation reaction in real time. The two robots effectively produced a Pareto front for the cost-yield optimisation problem over the course of three days of operation. The dynamic knowledge graph approach is also applied to optimise two Suzuki coupling reactions for efficient synthesis of challenging molecules, obtaining 82 mg of the final product from a 40 mL scale-up that would be otherwise difficult to access without extensive process redesign and manual synthesis efforts.
  • ItemOpen Access
    Developing bioelectronic sensors for 3D in vitro models at the air liquid interface: Innovative technology for biomedical and respiratory research.
    Barron, Sarah; Barron, Sarah [0000-0002-9922-2388]
    Respiratory diseases and airborne infections are significant global health issues and a major cause of morbidity and mortality worldwide. According to the World Health Organization (WHO), respiratory diseases rank as the third leading cause of death globally, with over 10 million deaths per year attributed to respiratory illnesses. Chronic respiratory conditions are characterized by aberrant immune and epithelial barrier function, leading to respiratory inflammation and tissue damage. Despite substantial progress in identifying therapeutic targets and drug candidates, the efficacy of novel treatments in clinical trials remains low. One of the reasons for this, is that the current gold standard models of human disease used for drug screening rely on two-dimensional (2D) cell culture and/or animal testing. However, neither of these models accurately reflects the native microenvironment of human tissues. Recent advancements in bioengineering have resulted in the development of *in vitro* tissue models that more closely mimic the native tissue microenvironment. These frameworks include the development of extracellular matrix (ECM) materials, microfluidics, and three dimensional (3D) organoids. However, the clinical translatability of these models is limited by the lack of sensor technologies capable of monitoring them non-invasively and in real time. Gold standard analysis techniques rely on sample labelling or destructive end-point analysis, while electronic methods require large, rigid electrode materials that are ill adapted to soft 3D microarchitecture. Additionally, these electronics require submersion in an electrolyte for operation, which is inadequate for native air-liquid interfaced tissue such as the lungs. In this context, the goal of this thesis project was to address these challenges from multiple angles and in a multidisciplinary manner. The first goal was biology-focused and aimed at transitioning from 2D monocultures to a multicellular 3D model. The development of the biological respiratory model was based on a co-culture of primary human bronchial epithelial cells and lung fibroblasts in collagen coated cell culture inserts. This model highlighted the impact that multicellular cross-talk can have on the development/differentiation of *in vitro* models and their differing response to drug stimulation. The second goal was materials-focused and aimed at creating a biomimetic ECM environment of the lung. A composite electroactive scaffold was developed using an organic conducting polymer blended with human Fibrinogen protein, which replicated the mechanical and morphological properties of the human airway. The platform was capable of monitoring barrier formation, in real-time over 32 days, and, permits continuous, simultaneous electrical readouts. The third goal was technology-focused and resulted in the design and fabrication of a novel microelectronic device that was capable of monitoring epithelial barrier function and perturbations of primary *in vitro* models at the air liquid interface (ALI). By developing integrated bioelectronic sensors, this work demonstrates the ability to monitor advanced 3D tissue-like systems non-invasively and in real time. Additionally, and for the first time, patient-specific electrical signatures of respiratory epithelial dysfunction were measured at ALI. This technology will also have the potential for use in other air interfaced model systems, including the gut, skin, eye and brain, for diagnostic, drug screening and personalised medicine applications. Overall, it is hoped the versatile biotechnology platforms developed here will contribute to the field of biomedical and respiratory research.
  • ItemOpen Access
    Renewables Integration Challenges of the Energy Transition
    Atherton, John
    Throughout the world a large-scale energy transition is unfolding. This fundamentally includes a vast expansion of variable renewable energy (VRE) generation. In Britain this primarily consists of onshore and offshore wind, as well as solar. The non-dispatchability of these technologies presents a unique range of challenges for the energy network. This thesis investigates the key difficulties of renewable energy integration in Britain, as well as some potential solutions. Renewables integration is investigated using a multidomain methodology to process and model live data. Using a knowledge graph supported infrastructure, the British energy network is modelled. This consists of a representation of Britain’s transmission network, power plants, and energy storage capabilities (both existing, and potential future expansions). These research investigations are expanded upon with respect to energy policy, and energy market modelling. A north/south divide is identified as a fundamental transmission limitation using both economic dispatch and curtailment analysis methodologies. From a network perspective, this inflexibility is particularly significant with respect to wind power from Scotland (both onshore and offshore), resulting in increased rates of curtailment. From a market perspective, a lower percentage rate of dispatchable energy penetration coincides with increased market volatility to the point where even coal has been strategically dispatched at higher prices, at a rate not seen in recent years prior. Co-located energy storage systems (ESSs) were modelled with the ability to both arbitrage price, and charge using otherwise curtailed energy to simulate their potential as a solution to both these challenges. ESSs co-located with Scottish wind farms were found to reduce curtailment volumes significantly compared to southern placements, while the latter achieved faster payback due to a greater of unconstrained opportunities to arbitrage prices. All ESSs, however, achieved payback due to significant energy price volatility experienced in recent years. Given these ongoing challenges to the electrical grid and markets, the role and placement of storage will remain topics of key significance, upon which these findings shed novel insight.
  • ItemOpen Access
    Development and characterisation of a human in vitro model of the Neurovascular Unit using a biomimetic tissue engineered system
    Barberio, Chiara
    The neurovascular unit (NVU) is a complex structure comprising endothelial cells, pericytes, astrocytes, neurons, and extracellular matrix (ECM) elements. It plays an essential role in regulating cerebral blood flow and maintaining the integrity of the blood-brain barrier (BBB). Over the years, several tissue engineering approaches modelling the NVU *in vitro* have gained popularity as they offer a platform to investigate the cellular cross-talk, unravel disease mechanisms as well as brain targeted therapeutics effects. While *in vivo* models have been invaluable in advancing our understanding of the brain functions, they also have significant limitations when it comes to tissue engineering purposes. Besides being expensive to establish and maintain, *in vivo* models are often species-specific and may not accurately reflect human pathophysiology. Additionally, ethical concerns around the use of animals in research can limit the types of studies that can be performed. These limitations highlight the need for alternative *in vitro* models that can more accurately mimic the NVU structure and functions in health and disease. Two-dimensional (2D) cell culture models, such as Transwell systems, have been widely used for *in vitro* NVU modelling allowing the investigation of cell-cell interactions and cellular influence on the BBB properties and integrity. However, one setback of such 2D models is that they do not fully recapitulate the complex 3D microenvironment of the NVU, which can negatively impact cell behaviour and signalling pathways. Additionally, in 2D models, shear stress and other mechanical forces experienced by endothelial cells are not representative of physiological conditions *in vivo*. Organ-on-chips technology, on the other hand, has emerged as a promising and more robust alternative platform to study the dynamic interactions between neuronal and vascular components under controlled conditions. Nevertheless, some challenges, such as difficulty in accurately replicating the complex 3D architecture and physiology of the native NVU still remain. Tissue engineering integrating bioelectronics has emerged as a promising strategy for *in vitro* modelling and monitoring of complex biological systems such as the NVU. This approach entails combining tissue-engineered equivalents (e.g., scaffolds, hydrogels) with integrated electronic sensors and devices, enabling real-time monitoring of cellular and molecular processes *in situ*. In addition, the seamless incorporation of bioelectronics within tissue-engineered systems enables the real-time and continuous recording of experimental conditions, hence avoiding end-point assay experiments. Furthermore, tissue engineering integrating bioelectronics has the remarkable potential to revolutionize disease modelling and drug screening by allowing the development of high-throughput platforms for screening drug candidates and personalized medicine. While there are still challenges to overcome, such as the biocompatibility of the electronic components, device design optimisation and the need for better integration methods, this approach holds great promise for advancing our understanding of complex biological systems and improving the design/development of therapeutics. The objective of this dissertation was to create a novel bioelectronic model of the neurovascular unit (NVU) that could continuously monitor the blood-brain barrier (BBB) *in vitro*. To achieve this goal, the research combined fundamental principles from 3D cell biology, material science, and tissue engineering. The first approach was to establish a 2D Transwell system as a reductionist and informative support for multiple NVU model configurations. The 2D system was used to determine the optimal cell culture conditions for endothelial cells, astrocytes, and neuronal cells. The resulting NVU *in vitro* platform was then characterised using optical and electrical methods to evaluate BBB integrity under different conditions. To improve and advance such biological model, the next step was to create a 3D biomimetic scaffold that resembled the brain extracellular matrix (ECM). The scaffold was designed to be a more physiologically relevant substrate than the 2D Transwell systems. A composite electroactive scaffold integrating ECM elements was generated to test its suitability to support neuronal cell culture and differentiation *in situ*. The biomimetic 3D scaffold was then used to host the multicellular NVU model, which demonstrated the formation of an apical endothelial barrier. Finally, the 3D NVU system was successfully incorporated into an in-house engineered bioelectronic platform called "e-Transmembrane." This platform enabled in-line, non-invasive, and dynamic monitoring of the NVU cell types and BBB development. The integration of the 3D NVU system into the e-Transmembrane platform represented a significant leap towards a more sophisticated and realistic model of the NVU and its BBB. In conclusion, this study described above has significant potential for advancing our understanding of the blood-brain barrier (BBB) and the neurovascular unit (NVU) *in vitro*. Furthermore, such bioelectronic NVU model could represent a reliable test bed for drug discovery and screening by providing a more reliable and predictive platform for testing the permeability of drugs across the BBB.
  • ItemOpen Access
    Functionalization of phage endolysins to treat infections of spore-forming Clostridia bacteria in the era of antibiotic resistance
    Mills, Gabrielle
    Certain *Clostridia* species are notable pathogens, not only because they form highly resistant endospores but because they produce toxins and exhibit antibiotic resistance. *Clostridioides difficile* is a nosocomial pathogen responsible for thousands of deaths every year, whereas *Clostridium perfringens* is a common cause of food poisoning and a notable veterinary pathogen. Research on *Clostridia* spores and phage therapy has been limited to a selection of species, partially because of their anaerobic nature. While recent work has endeavoured to bridge this gap, there is still a pressing need to understand the resistance mechanisms of *Clostridia* spore-formers—especially *Clostridioides difficile*—and develop improved treatments for these difficult pathogens. The aim of this thesis was to augment current knowledge of *Clostridia* and their phage-based therapies, specifically through analysis of *Clostridium perfringens* SM101 as well as *Clostridioides difficile* strains 630, R20291, and SH1. The first portion of the thesis specifically looks at the *Clostridia* spore coat: the composition through proteomics and genetic engineering and the structure via phase contrast and TEM imaging. Over 3,000 coat and exosporium proteins were identified in *Clostridia* species, and TEM showed that *Clostridioides difficile* 630 and *Clostridium perfringens* SM101 both possess an exosporium like that of *Clostridium sporogenes*. Then, phage were induced and isolated to assess their specificity and efficacy against *Clostridia*, yet endolysin domains were the ultimate focus for host range analyses and subsequent experimentation. Finally, the feasibility of combining endolysin binding domains and antibacterial proteins was investigated for improvements in lytic and binding activity. To do so, a novel methodology was formulated to synthesize these proteins in susceptible *Escherichia coli*. Fusions of hen-egg-white- lysozyme and a phage endolysin domain of *Clostridium perfringens* show enhanced lytic activity against *Clostridia* bacteria, including outgrown vegetative cells.
  • ItemOpen Access
    Rheology of bubbly liquids containing a non-Newtonian continuous phase
    Gibson, Douglas
    Bubbly liquids are two-phase materials where gas is dispersed in a liquid with bubble volume fractions ranging between 0 and 0.5. Bubbly liquids are found in many real-world contexts and their bulk rheological behaviour is determined by the continuous phase rheology, the bubble phase properties and the interaction between the two phases. The majority of research concerning bubbly liquids has focussed on materials with a Newtonian continuous phase. However, many of the bubbly liquids found in industrial and consumer settings have a viscoplastic continuous phase. This project has investigated the rheology of bubbly liquids with a viscoplastic continuous phase, namely a commercial polymer solution called ClearGlideTM that belongs to a family of yield stress fluids known as Carbopols. Bubbly liquids with air volume fractions in the range 𝜙a = 0 − 0.5 were produced by whisking the continuous phase in a planetary mixer. Aeration profiles describing how 𝜙a evolved were found to have the same general shape regardless of continuous phase dilution or mixer speed. When plotted using normalised aeration parameters the majority of data sets followed a common curve which was quantified using an exponential relationship. The bubbly liquids had polydisperse log-normal bubble size distributions. The variation in bubble size with continuous phase dilution and mixing time was considered with respect to bubble break-up. Original models accounting for the process geometry were proposed and found to provide a good description of the aeration process. These models provided insight into the mechanisms taking place during whisking, specifically entrainment and disengagement of bubbles. Rheological behaviour was investigated using both shear and oscillatory rheometry. Unaerated ClearGlideTM exhibited typical yield stress fluid behaviour comprising three distinct regions: solid-like linear elastic deformation at low stress, followed by a yielding transition, and subsequent liquid-like shear-thinning at large stress that was described using the Herschel-Bulkley model. Suitable rheological parameters were identified to characterise each region. Bubbly ClearGlideTM exhibited similar behaviour to that of its continuous phase. Aeration generally resulted in a reduction in the rheological parameters with increasing air volume fraction. Correlations quantifying the impact of air volume fraction were determined. The combined effect of the continuous and bubble phase material properties was considered and found to be complex, with the continuous phase having a greater impact on the bulk rheology.
  • ItemEmbargo
    Inverse Methods for Fast Field Cycling and NMR Correlation Experiments
    Beckmann, Julian
    This thesis focuses on the development of inverse methods for nuclear magnetic res- onance (NMR) correlation or exchange experiments and new fast field cycling (FFC) modelling approaches. Moreover, the theoretical framework to consider fast field cycling as an inverse problem is provided and the possibility to employ established regularization methods for the inversion of NMR dispersion profiles is investigated. It is shown that the relaxation behaviour of systems with sufficiently understood molecular dynamics such as ionic liquids can be accurately described by simplifying relaxation models. This is further supported by findings from molecular dynamics simulations and physicochemical measurements. Further, the concept of generalized cross validation was applied to modified to- tal generalized variation (MTGV) regularization and it was shown that deep learning can be employed for inversion of NMR signals from exchange or correlation ex- periments. In addition, the inversion performance of deep learning, Tikhonov and MTGV regularization was compared on a vast data set of simulated NMR signals providing strong evidence of deep learning achieving the best reconstruction results in a clear majority of instances. Finally, fast field cycling was treated as an inverse problem and MTGV regularization was employed to reconstruct correlation time distributions from NMR dispersion profiles. From this, detailed insights into the molecular dynamics of several cata- lyst samples were obtained previously not accessible with established modelling approaches.
  • ItemEmbargo
    Packed bed reactors at the pellet scale: magnetic resonance studies of hydrodynamics, mass transport and reaction
    Elgersma, Scott
    Packed bed reactors are widely used in the chemical and petrochemical industries to facilitate heterogeneous catalytic reactions. Despite the widespread use of packed bed reactors in industry, the flow, transport, and reaction phenomena occurring at the scale of individual catalyst pellets is poorly understood due to a lack of experimental techniques capable of non-invasively probing these phenomena at the pellet scale. This thesis presents the development and implementation of magnetic resonance (MR) techniques to investigate the hydrodynamics, mass transport and reaction occurring at the pellet scale within packed bed reactors. The aim of this work is to gain physical insight into the processes occurring at the pellet scale, which can subsequently be applied to optimize the catalyst and reactor technology used in industry. A novel MR method is developed and implemented to quantify the liquid-solid mass transfer coefficient in packed beds. The method, which utilizes MR relaxation exchange and a magnetization transport model, enables the true mass transfer process at the pellet scale to be probed without the confounding effect of molecular dispersion. The MR method is applied to measure the mass transfer coefficient for single phase liquid flow and two-phase gas-liquid flow in beds of SiO2 and TiO2 pellets. The limiting mass transfer coefficient at zero flow is directly measured for the first time, helping to resolve a debate in literature regarding its value. An MR velocity imaging methodology is developed to acquire 3D images of the time-averaged velocity and turbulent kinetic energy for turbulent flows in packed beds of commercially relevant α-Al2O3 pellets. The method is subsequently used to investigate the effect of pellet shape on hydrodynamics, revealing that the hydrodynamics at both the bed and pellet scale are influenced by pellet shape. Further, MR velocity imaging is applied to investigate the effect of the tube-to-pellet diameter ratio on the hydrodynamics. For the beds studied, the pellet scale hydrodynamics are found to be independent of the tube-to-pellet diameter ratio, but some differences in the near wall hydrodynamics are observed. Finally, MR based chemical shift imaging (CSI) is implemented to map the intra-pellet chemical composition at *operando* conditions during the hydrogenation of styrene to ethylbenzene over a Pd/Al2O3 catalyst. Inhomogeneous partial wetting of the catalyst, representative of that known to occur in commercial reactors, is found to cause substantial heterogeneity in chemical composition across a single catalyst pellet. Commonly used 1D reaction-diffusion models are inadequate to describe the observed reaction heterogeneity. The pellet-scale composition maps provide novel insight regarding the coupling between transport and reaction at the pellet scale and have important implications for pellet scale catalyst models.
  • ItemOpen Access
    Monitoring Biological Events on Native Cell Membranes with Organic Bioelectronics
    Lu, Zixuan
    Traditional drug screening technologies rely heavily on animal testing, which is not only expensive and time-consuming, but also raises many ethical issues. Also, most drug candidates fail during the different phases of drug testing. Therefore, developing a low-cost, rapid, and animal-free system to allow mass-screening of a drugs’ specificity and efficacy is the promising way to increase testing efficiency for novel drugs. Since more than half of currently approved drugs target cell membranes, studying drug interactions with cell membranes is one of the key factors in understanding drug cytotoxicity and efficacy. The recently developed membrane-on-a-chip system, involving cell-derived supported lipid bilayers (SLBs) integrated with bioelectronic devices, is able to rapidly sense drug responses from cell membrane. This system eliminates the complexities and challenges of traditional testing systems, such as sterile cell-culture environments or physiological monitoring of animals. Also, quantification of drug responses with this system can be easily achieved by membrane quality analysis (i.e. electrochemical impedance spectroscopy (EIS)). Therefore, the membrane-on-a-chip system is a promising technology to advance next-generation drug discovery. Colleagues from our group previously demonstrated the detection of drug response with overexpressed transmembrane proteins with this systems and quantified the response with a model circuit. The key progress of this dissertation is sensing bioevents associated with naturally-expressed transmembrane proteins. This dissertation also discusses on multiple aspects of the membrane-on-a-chip system, such as strategies for device design and fabrication, electrochemical characteristics of SLBs, noise reduction for accurate measurements, and applications for biomedical research and drug screening. First, the two issues (1. gold-ring marks; 2. delamination of sacrificial layer induced by bubble formation) caused by photolithography during microfabrication are optimised and discussed for achieving high productivity and stable performance of these organic devices. Then, the experimental data was combined with numerical simulations to explore the relationship between changes in SLB quality and impedance output, delving into a deeper understanding of the impedance profiles of devices with and without SLBs, as well as extracted parameters such as membrane resistance (Rm). This approach was employed to investigate the relationship between microelectrode area and sensor sensitivity with changes in SLB state, towards rational device design. We highlight the trend of electrode size (polymer volume) required for sensing bilayer presence as well as the dependence of electrode sensitivity on SLB capacitance and resistance. Lastly, I illustrate how the flexible approach of including electrode and transistor measurements to amalgamate characteristic impedance spectra of transistors overcoming the problem of low-frequency noise and errors seen with traditional EIS. With a deeper understanding of the membrane-on-a-chip system, we further apply this technology to: detect SARS-CoV-2 viral entry and screen antibody efficacy to inhibit viral invasion, which is discussed in chapter 4; Study drug-induced blockage of voltage-gated calcium (CaV) ion channels on SLBs derived from neuroblastoma and primary cortical cells, which is discussed in chapter 5. In chapter 4, I demonstrate characteristic electrochemical impedance spectra of the early and late pathways of SARS-CoV-2 entry on SLBs derived from two cell lines: human lung epithelial Calu-3 cells and human embryonic kidney cells which overexpress the ACE2 receptor (HEK293-ACE2), respectively. The sensitivity of virus detection is further assessed by testing a range of viral particle concentrations; the detectable concentration reaching as low as ~102 virus pseudo particles (VPPs) per ml. We successfully apply this platform not only as a sensitive viral particle detector, but also as a drug-screening platform for screening antibodies that specifically target either ACE2 proteins on the host membrane or SARS-CoV-2 Spike proteins. For the neuronal membrane application demonstrated in chapter 5, I first performed differentiation on SH-SY5Y neuroblastoma cells, and then form native SLBs from the SH-SY5Y cells before and after differentiation on microelectrode arrays. An upregulated dose response of blocking of naturally expressed CaV ion-channels after SH-SY5Y differentiation was successfully detected via EIS. Further, the same setup was adapted to the native SLBs derived from rat primary cortical cells for the first time. The response of CaV ion-channel blocking on rat cortical neuron SLBs is comparable to the response from SH-SY5Y SLBs with and without differentiation. This dissertation demonstrates that this membrane-on-a-chip system is a rapid, ultra-sensitive, cell-free, and high-throughput platform to sense biological events, including specific interactions with endogenously expressed levels of membrane proteins. This could be an important contribution to the development of next-generation diagnostics and antibody/drug screening technologies for healthcare and biomedical research.
  • ItemOpen Access
    Transfer Learning for Accelerated Process Development
    Felton, Kobi; Felton, Kobi [0000-0002-3616-4766]
    One of the greatest challenges in process development is the limited amount of data that can be collected. Techniques that can draw insights from this limited data have the possibility to accelerate process development. This thesis presents a collection of studies on using transfer learning to accelerate various aspects of process development. Part I focuses on reaction optimization, where I propose a benchmarking framework for comparing machine learning strategies for reaction optimization and demonstrate the benefits of using multi-task learning to accelerate chemical reaction optimization. In Part II, I explore the use of reinforcement learning and multi-fidelity Bayesian optimization for accelerating feedback controller tuning, specifically for distillation control systems. Finally, in Part III, I take two perspectives on using machine learning for predictive thermodynamics, a key aspect of process modelling. I introduce *DeepGamma* for predicting activity coefficients and ML-SAFT for predicting PCP-SAFT parameters, showing steps towards improving thermodynamic predictions using transfer learning. Together, all of these studies demonstrate the potential of transfer learning to accelerate process development, providing valuable insights for future research and practical applications.
  • ItemEmbargo
    Optimal control reformulation for the solution of decision-making problems in chemical engineering
    Mappas, Vasileios
    This thesis focuses on the challenges that arise from the solution of decision-making problems in terms of convergence, efficiency and robustness. State-of-the-art solvers could fail to find the optimal solution or solve small instances of these problems. The original contribution of the current research consists of the development of a new methodology, based on optimal control theory, to solve different types of decision-making problems. The goal behind developing optimal control reformulations is to effectively solve these problems, while overcoming the drawbacks of the widely used commercial solvers, and three different types of problems are examined. First, the proposed solution scheme is used for finding the best pairings between control and manipulated variables and utilising the optimal tuning parameters, simultaneously, while satisfying path and end-point constraints. The proposed methodology results in the same pairings and gives better control actions compared to well-established methods. Furthermore, a novel multistage feedback controller is presented, which is proven to be able to handle disturbances with and without uncertainty and switching between operating points. Second, the Reverse Osmosis (RO) cleaning maintenance scheduling problem is examined. It is shown that the proposed framework can solve successfully this type of problem, even for large-scale configurations, long time horizons and arbitrary realistic model complexity of the underlying dynamic model of the RO process and produce an automated solution for the membrane cleaning scheduling, obviating the need for any form of combinatorial optimisation. Two-Point Boundary Value Problems (TPBVPs) are examined and solved utilising a double shooting approach, where the initial TPBVP is reformulated into a set of Ordinary Differential Equations (ODEs) and is solved as a least-squares optimisation problem in order to obtain the solution trajectory. Furthermore, a TPBVP formulation is extended and applied to solve dynamic optimisation problems (OCP), based on Pontryagin's Minimum Principle. Three different algorithms are used to solve the OCPs: the forward, backward and double shooting methods. In the case of OCPs, the proposed solution scheme obviates the need for discretising the control actions through the time horizon. The proposed algorithm successfully solved the TPBVPs and found the optimal solution of the OCP, showing a robust performance.
  • ItemEmbargo
    Data-driven Dynamic Control Scheme for Antibody Producing CHO Cell Cultures in Fed-batch
    Gangadharan, Nishanthi
    Effective process control is a basic requirement for biopharmaceutical manufacturing to achieve high efficiency and enhanced quality control. High non-linearity and uncertainties associated with bioprocesses challenge the ability of traditional controllers to deliver satisfactory performance, thereby creating a need for advanced model based control strategies for efficient bioprocess control. Unlike the chemical or traditional pharmaceutical sector, bioprocess control is a complex process that involves controlling the behaviour of billions of cells in a bioreactor that evolves with non-linear dynamics over time. Variability in cell culture behaviour arising from heterogeneous culture conditions, product types, and cell types make developing a generalised control action challenging. Historic bioprocess data can provide valuable insights into the underlying dynamics of cell culture. Process Analytical Technology (PAT) initiative has highlighted the importance of identifying critical process parameters (CPP) of a bioprocess that influence critical quality attributes (CQA), to achieve seamless integration of analytical data with real-time monitoring and control for enhanced process understanding and to overcome manufacturing challenges. This thesis explores the different stages of development of a novel data-driven dynamic control scheme for bioprocesses in the context of antibody producing CHO cell cultures in fed-batch bioreactors. Multivariate monitoring techniques in biopharmaceuticals has resulted in the generation of large amounts of data comprising real-time measurements of critical quality and performance attributes, and if exploited efficiently can provide opportunity for developing superior control action. This work employs a novel methodology for condensing online data into an offline data matrix to achieve a comprehensive view of the critical process parameter landscape. The methodology was found superior to traditionally employed averaging and helped increase the number of variables available for representing the design space of the process. The augmented data set was then used to extract novel parameter relationships by applying concepts from network theory. The ability of these newly identified parameter relationship in describing the process efficiently was tested by constructing symbolic regression models. The performance of newly identified variables in predicting process behaviour was found superior to variable relationships found in literature. Following this, new models were generated using support vector machines (SVM) to predict the performance of these cultures at a future time point. These models helped decide the expected trajectory of a culture, based on process knowledge derived from historical bioprocess data. Customised control strategies were developed, to acknowledge the process dynamics of different days of the culture, by employing an optimisation algorithm along with the different models developed throughout the course of this study. The control scheme dynamically recalculates the expected trajectory and proposes customised reactive control action when encountered with a deviation from the expected trajectory. The proposed control scheme was able to recommend sensible control actions during the different test cases designed for control. This closed-loop model-based multi-attribute control scheme, that employs concepts from data science, network theory and control theory, ensure that the cultures remain on a pre-defined well established trajectory thereby minimising variability.
  • ItemOpen Access
    Deep learning for image processing in optical super-resolution microscopy
    Christensen, Charles N; Christensen, Charles [0000-0002-5355-1063]
    Optical microscopy is fundamentally governed by a trade-off between image quality, imaging speed and duration. The quality can be considered a function of the signal-to-noise ratio, contrast and image resolution, which are all limited by the amount of light that can be acquired within a set exposure time. Many applications in live-cell imaging have specific requirements for illumination power and exposure time, thus necessitating a compromise with quality. In recent years, this fundamental limitation in optical microscopy has been shifted with the aid of deep learning methods. In this thesis, I propose methods that improve robustness to noise in image processing while making greater use of the available signal in the data. Applications include denoising for improved electron tomography when using cryogenic electron microscopy; image segmentation facilitating quantitative analysis of dynamics in endoplasmic reticulum (ERnet); and versatile reconstruction of super-resolved images from raw data acquired with structured illumination microscopy (ML-SIM). The deep learning methods that are presented are compared to classical image processing alternatives and tested on real experimental data acquired by collaborators in different departments of the university. The overall finding of the thesis is that deep learning techniques offer a highly effective approach to many problems in bioimaging. With ERnet, it is possible to obtain a segmentation method that is reliable, fast and functional across different experiments without the need for retraining guided by further manual annotations. As for ML-SIM, I show that the reconstruction of structured illumination microscopy data can be treated as the inverse problem of a forward modelling process. This relies on an approximative image formation model that takes uncertainties and noise into account. By training a deep neural network to invert the forward modelled SIM data, a highly generalised reconstruction model can be obtained, which can handle SIM data from multiple microscopes while providing a high reconstruction quality. The thesis is concluded with a reflective section on where the field is headed and which future applications may be enabled by the advancement of deep learning techniques.
  • ItemEmbargo
    Ruthenium and Cobalt Catalysts for Hydrogen Production from Ammonia
    El-Kadi, Joseph
    In the global energy transition, green ammonia can serve as a carbon-free energy vector and is suited to store renewable energy long-term in its chemical bonds. Ammonia can then be partially cracked for direct use as a fuel or fully cracked for use in a hydrogen fuel cell. In both cases, catalytic ammonia cracking is paramount to release hydrogen on demand and avoid NOx formation. Currently, the benchmark catalyst for ammonia cracking is ruthenium supported on carbon nanotubes (CNT). Future catalyst development requires a departure from trial-and-error catalyst discovery. This thesis presents a new method for the synthesis of catalysts with precise nanoparticle sizes to unlock fundamental reaction knowledge, redefining the way we design and optimise heterogeneous catalysts. Ruthenium nanoparticles (Ru NPs) are synthesised in the absence of capping ligands in a bespoke continuous microreactor, enabling the control of average nanoparticle size with narrow size distributions. A method is developed to immobilise the Ru NPs onto various supports while maintaining size via electrostatic stabilisation, bridging the gap between colloidal science and heterogeneous catalysis. This reveals for the first time unique size- and support-activity relationships. For a Ru NP size of ~2.5-3 nm, CeO2 and ZrO2 are identified as superior supports compared to the benchmark CNT, attributed to the electron-donating properties of CeO2 and the ability of CeO2 and ZrO2 to spillover hydrogen into their bulk lattices. The morphology of CeO2 is seen to affect catalytic activity, with nanoparticles and nanocubes outperforming nanorods. The correlation between Ru NP size and activity is demonstrated experimentally, in agreement with theoretical studies that predict a maximum density of ‘B5’ active sites for ~2-3 nm hemispherical Ru. Cobalt (Co) catalysts are also investigated to replace Ru with a more abundant metal for the large-scale implementation of ammonia as an energy carrier. Catalytic activity improves by a factor of 2.8 via the continuous synthesis and immobilisation of colloidal 11 nm Co NPs on alumina, compared to ~60 nm Co from traditional impregnation. Strategies from Fischer-Tropsch synthesis on the promotion and reduction of cobalt catalysts are applied for the first time to ammonia cracking, demonstrating the transfer of knowledge between these catalysis fields. This reveals that a balance is required to achieve complete cobalt oxide reduction while maintaining the more active hexagonal close-packed phase of Co at elevated ammonia cracking temperatures. This shows the importance of considering both NP size and crystal structure during catalyst development and reaction conditions. Overall, this thesis demonstrates that the development of catalysts using the continuous synthesis of colloidal active sites provides an accelerated way of decoupling size, promotion, and support effects, contributing guidelines to the future design of catalysts for structurally sensitive reactions.
  • ItemEmbargo
    Development of Densified Metal-Organic Framework Monoliths for Gas Adsorption and Separation Applications
    Yang, Yue
    Metal-organic frameworks (MOFs) have garnered significant interest as promising functional materials with superior gas adsorption and separation performance due to their highly tuneable nature, which enables control over structures and physicochemical properties such as BET area, pore volume, and surface chemistry. However, despite their potential, the practical deployment of these materials in real-world applications has been hindered by their powdery morphology. In recent years, the Adsorption & Advanced Materials Laboratory (AAML) at the University of Cambridge has made significant progress in this regard by developing MOF monoliths (monoMOFs) that possess high bulk densities and tuneable porosity. These breakthroughs have enabled record-breaking performance in volumetric CH4 and H2 storage applications. The fundamental aim of this thesis is to expand the realm of synthesis techniques utilised for the formation of monolithic MOFs, while concurrently exploring the possible applications of the resultant MOF monoliths. Moreover, this thesis endeavours to investigate the association between synthesis processes, properties, and functionalities of MOF monoliths with respect to gas adsorption and separation applications. To achieve this goal, the self-shaping approach for MOF monolith formation was initially applied to a variety of materials, including homochiral CMOM-3S, mesoporous ZIF-90, and Hf-UiO-66 family MOFs. The synthesis protocols employed in this study demonstrate that highly dense MOF monoliths can be produced using large nanoparticles and that MOF gels are not the sole pathway to monolith formation. Rather, slurries and colloidal suspensions have also been shown to be effective in producing dense MOF monoliths. Following a comprehensive study of MOF monolith synthesis, the knowledge acquired was applied to develop efficient monolithic MOFs for targeted applications of interest. Specifically, using a solvent-determined synthesis approach, UTSA-16-Zn, a promising MOF for carbon capture and separation, has been synthesised as dense monoliths for the first time. The synthesis conditions were systematically optimised to attain optimal porosity properties and bulk densities. The resulting UTSA-16-Zn monoliths exhibit outstanding performance in carbon capture and separation applications, demonstrating high volumetric adsorption capacities and selectivity. Additionally, Al-soc-MOF, a benchmark MOF for CH4 storage and separation, was also investigated. A modulation approach was employed to shape it into monoliths with varying porosity and bulk densities. The impact of modulators on the performance of Al-soc-MOF monoliths was evaluated, revealing exceptional C3/C1 separation and CH4 storage performance. Overall, this thesis provides invaluable insights into the synthesis, shaping, and application of MOFs monoliths. The findings reported herein shed light on the unique properties and potential of MOF monoliths as a possible solution for tackling pressing environmental challenges.
  • ItemOpen Access
    Mathematical Optimisation Advances in Process Systems Engineering
    Espaas, Thomas
    Process systems engineering is a sub-field of chemical engineering that encompasses numerous applications. It also encompasses all levels of the problem solving process, from deep conceptual understanding of the applications, through the formulation of accurate and appropriate mathematical models, to the efficient and reliable computational solution and simulation of these problems. The process systems engineer benefits from knowledge of all these areas and also an appreciation for the interaction between them. The first contribution of this thesis to the area of process system engineering is in the form of a thorough study of higher-order interior point methods. Firstly, a number of theoretical developments are presented. These developments include formal definitions of trajectories relevant to an interior point framework, the derivation of higher-order derivatives of these trajectories for anything from the linear programming case to the general convex nonlinear programming case, and examples of how these can be employed in a full algorithmic framework. Computational results of employing such frameworks highlight how these higher-order methods have a strong potential to decrease the iteration count of an interior point method. More importantly, the higher-order methods also show potential in decreasing the CPU time on certain problem types. The performance of these methods seems to positively correlate with the density of the Hessian of the objective function and of the Jacobian of the constraints, and negatively correlate with the number of general nonlinear functions, at least in the limit where this number grows very large. Theoretical results also prove that the worst-case complexity of a higher-order trajectory-following method for linear programming does not need to compromise the best known O(√n log(1 / ε)) iteration complexity of standard interior point methods. This is important in guaranteeing the performance of the new method proposed in this work when applied to increasingly large problems. Unfortunately, many practical problems are not privileged with convexity, and to deal with these, special global optimisation methods are required. An important type of nonconvex problems is the bilinear programming problem. Bilinear problems are first and foremost interesting because of their many practical applications, including pooling problems. Over a couple of chapters, this thesis contributes to the solution of nonconvex pooling problems. A core building block of many global optimisation methods is the construction and solution of convex relaxations of the nonconvex problem. Studies are made into the piecewise-linear relaxation of a reformulation of the bilinear programming problem. Additional focus is devoted to studying the migration of the solutions of these relaxations as the relaxation is squeezed through an increased number of segments in the piecewise approximation. The studies are made on a set of pooling problems from the literature. The solutions of relaxed problems do not need to be feasible with respect to the original problem. Therefore, another core building block of global optimisation methods is the identification of good feasible solutions to the original problem. Through a discretisation paradigm, three related discretisation formulations are developed for bilinear programming problems. Algorithms based on these discretisation formulations are validated by solving a number of bilinear pooling problems from the literature. Introduction of model-intrinsic binary variables into continuous optimisation problems often incurs a significant complexity increase, but as the discretisation algorithms already solve the originally nonlinear programming problems as a series of mixed-integer linear programming problems, the inclusion of such model-intrinsic binary variables is straightforward. The formulations are modified to extend beyond bilinear programming problems to be able to address general nonlinearity. This is accomplished through a combination of dimensional lifting of the original problem and relaxations of the discretisation constraints. The approach is validated on two small-scale (mixed-integer) nonlinear programming problems. The final contribution of the thesis is to the field of optimisation under uncertainty, and in particular, to the pooling problem under uncertainty. This thesis gives a thorough conceptual discussion on the role of uncertainty in the pooling problem and on the use of proxy models for solving the original application. This is in itself a major contribution because the literature appears to blindly assign and solve proxy models in place of the original problem, with no validation of the solution using the true model of the problem. The consequences of this are highlighted quantitatively by applying robust optimisation and a scenario approach to a small number of different pooling problems. The true nature of the uncertainty is assumed to be normally distributed. A model is also developed to accurately represent the true stochastic problem, and the problem is solved through a spatial branch and bound method. As such, the thesis contributes directly to all three levels of the process systems engineering hierarchy. Finally, this thesis strives to leverage the novel research ideas presented herein to unlock new opportunities and highlight promising future research directions within process systems engineering.
  • ItemEmbargo
    Transparent neural interfaces for simultaneous electrophysiology and advanced brain imaging
    Middya, Sagnik
    Imaging and electrophysiology are the most fundamental tools in neuroscience research. On the one hand, optical imaging can target specific molecules with high spatial resolution *in vitro* and *in vivo*. Spectroscopic techniques like magnetic resonance imaging (MRI) can access deep regions of the brain over a large area and is the state-of-the-art in clinical brain imaging. On the other hand, microelectrode arrays (MEAs) and neural probes are indispensable in deciphering the electrical activity of neurons. Unfortunately, simultaneous imaging and electrophysiology is challenging with conventional metal electrodes which are non-transparent. In MRI, the issue is compounded by the heating effect in metals and loss of signal due to their significantly different magnetic susceptibility compared to biological tissue. Conducting polymer electrodes are prospective alternatives since their compositions are closer to biological tissues. Poly(3,4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS), one of the most widely used conducting polymers, exhibits volumetric capacitance effect which reduces electrode impedance and significantly improves the signal-to-noise ratio of neural interfaces. This work presents PEDOT:PSS-based optically transparent and MRI compatible MEAs. To begin with, a scalable and repeatable patterning technique of PEDOT:PSS on glass was developed which manifested in *in vitro* MEAs. The PEDOT:PSS electrodes exhibited superior electrochemical properties than other alternative transparent conductors. The transparent MEAs enabled simultaneous electrical recordings and Ca2+ imaging from neurons and allowed super-resolved imaging of diffraction limited cellular structures in addition to state-of-the art fluorescence imaging. Subsequently, the MEAs were implemented for studying the spread of tau pathology in neurodegenerative diseases and its effects on overall neuronal activity. They were integrated into a microfluidic neuronal culture platform to selectively examine the activity-dependent uptake of tau protein at the pre-synapse. Next, the *in vitro* glass-based transparent MEAs were translated into ultra-thin flexible MEAs for *in vivo* applications. As micro-electrocorticography (µECoG) arrays, the flexible MEAs enabled MRI imaging with minimal artifacts and showed promise for simultaneous functional MRI and electrophysiology. The structure of the flexible MEAs were also favourable for long-term organoid cultures and a modified design enabled continuous electrophysiology for weeks. It is expected that the versatile, transparent PEDOT:PSS MEAs would add new capabilities to neuroscience research by enabling complementary electrophysiology and multi-modal imaging.
  • ItemEmbargo
    Indocyanine Green J-aggregate Nanoparticles for Detection of Senescent Cells
    Baker, Andrew
    Cellular senescence is a response to unrepairable damage and stress characterized by the implementation of a stable cell cycle arrest and an intense pro-inflammatory secretory phenotype (SASP). Upon persistent damage or during aging, senescent cells accumulate in tissues and organs, partially due to an inefficient clearance by the immune system. The presence of senescent cells has been actively implicated in multiple pathological manifestations and chronic disorders including, fibrosis, neurological disorders, diabetes, sarcopenia, inflammatory syndromes and cancer. Evidence during the last decade convincingly demonstrates that the genetic and/or pharmacological removal of senescent cells (senotherapy), in preclinical models, ameliorates a number of aging-associated diseases. In addition, the removal of dysfunctional senescent cells in mice significantly extends not only their health span but also their lifespan (by about 30%). As a consequence of this preclinical success, the field of senotherapies has expanded and the most promising senolytic drugs have been prioritized to early phase clinical trials. Despite treatments showing promising translational results, there is still a lack of tools and techniques to detect, identify and longitudinally monitor senescent cells in *in vivo* settings, thereby emerging as an unmet need to assess the senescent burden pre- and post-senotherapy. Here, within this thesis, we have designed, synthesized and validated the first of its kind J-aggregate nanoparticle, exhibiting fluorescent and photoacoustic imaging properties for longitudinal *in vivo* bioimaging of senescence. First, in this thesis the synthesis of J-aggregates of indocyanine green was explored with the aim to develop a high contrast photoacoustic probe. Reported methods of J-aggregate productions resulted in poorly characterized systems, and the addition of centrifugation steps during the purification resulted in pure J-aggregates, which we named NanoJaggs. These structures are made of a dimer of the clinically approved dye indocyanine green, they were found to be stable in range of conditions, easily sterilized, and could be stored for prolonged periods of time, which makes them well-suited for biomedical applications. The specific chemical composition of NanoJaggs was studied using UV-Vis, cryoTEM, LC-MS and 1H NMR, resulting in a fully characterized system, exhibiting a strongly enhanced photoacoustic signal (Chapter 4). Following the synthesis optimization and characterization, the potential of NanoJaggs to be used as senescent cell probes was explored *in vitro*. They were found to selectively accumulate in senescent cancer cells and fibroblasts from multiple tissues of origin, and inducers of senescence. NanoJaggs were found to be comparable to established senescence detection methods, such as the lysosomal senescence-associated b-galactosidase (SA-β-gal) activity and to colocalize with the lysosome. As senescent cells possess a higher lysosomal mass and amount, we hypothesized that this was the reason for the selectivity of the NanoJaggs (Chapter 5). The evaluation of the targeting and uptake mechanism using endocytosis inhibitors showed that clathrin-mediated endocytosis as well as macropinocytosis play a role in NanoJagg uptake in senescent cells pointing towards an active mechanism of uptake in addition to lysosomal targeting (Chapter 6). Finally, *in vivo* experiments both exploiting the fluorescence and photoacoustic properties of NanoJaggs were performed. To do so, we employed a mouse model of chemotherapy-induced senescence in tumor xenografts. In both cases a significant accumulation of NanoJaggs in senescent lesions was found, confirming the potential of NanoJaggs to be used as *in vivo* probes to determine the burden of senescent cells in tumor models. Importantly, in this chapter the photoacoustic contrast of NanoJaggs was evaluated to detect cellular senescence and used to map the tumor microenvironment (Chapter 7). Together, these findings demonstrate the significant potential of NanoJaggs for detection of cellular senescence *in vitro*, *ex vivo* and *in vivo* in cancer models. The ability of NanoJaggs to act as a contrast agent for photoacoustic tomography, as well as its simple synthesis and FDA approved building blocks make the NanoJaggs appealing for future clinical applications, and a promising modality for senescence detection and longitudinal bioimaging.
  • ItemOpen Access
    The Shear and Extensional Rheology of Polymer Dispersions
    Umashanker, Janaki
    For the oil and gas extraction process, the understanding of well construction fluids (WCFs) were found to be fundamental towards the success and safety of the operation. This is due to the multiple functions the fluid provides such as maintaining the well bore formation pressure, lubricating the drill bit and transporting any drill cuttings to the surface. The well construction process methodology had been adapted towards carbon storage which promotes the research towards the understanding of well construction fluid rheology. WCFs generally present thixotropic or yield stress rheological behaviours. Additives are often used to promote the rheological properties. This causes the WCFs to have multiple phases present and much more complex interactions of polymers, emulsions, and particles. With the complex formulations, the process involves the WCFs to be pumped alongside a highly complex drill bit geometry. The shear rates required to pump from the surface of the well bore to the drill bit can range from 5 to 105 s-1. When WCFs are pumped to the drill bit, the fluids are contracted and expanded into the well bore. This causes the fluid to undergo both shear and extensional strains. With the process involving the complex formulations and flow regimes, it is ideal to understand the rheology in the shear and extensional regime over a range of shear rates. It is also essential to understand the interactions within the fluid and its impact on the rheology of the fluid. The topic of research for this PhD is to understand the rheological behaviours of WCFs. To understand the rheology of these complex fluids, the goal was to develop a simulation that can accurately calculate the rheological behaviour for WCFs. Simulations are an important and useful tool to obtain a ballpark range of any parameters under investigation. The methodology was defined by examining three types of fluids: Newtonian, viscoelastic and finally complex, multiphase fluids. This led to used silicone oil, xanthan gum aqueous solutions and WCFs respectively. Each fluid would then undergo three stages: experimental, rheological classification and simulation. For the experimental stage, the rheological data was obtained using the rotational and capillary rheometer. This is to ensure that a range of shear rates can be explored for rheological behaviours. A filament thinning rheometer was used to examine the fluid behaviour in the extensional regime. Using the experimental data, a numerical model is selected in the rheological classification stage. Numerical models are compared with the experimental data for the best fit with the fluid. The selected numerical model is used for simulation, the final stage, which uses the geometry selected in the experimental stage. The simulation stage compares qualitative and quantitative data with the experiments. This is the first step of understanding the applicability of numerical models to simulations and, if any, limitations arise from experiments or simulations. For the comparison of experiments and simulation data, contraction tests were used. These tests were chosen as it was a method to recreate similar conditions of WCFs contracting/expanding by the drill bit. Silicone oil, a viscosity standard fluid, had been used to test the methodology and to compare with literature. The experimental results had shown at a critical shear rate, there is a shear thinning trend. An investigation was made to identify the reasoning for the shear thinning trend and it was due to the high shear rates from the capillary rheometer. This had also been captured using the software Ansys PolyFlow with the Carreau-Yasuda model. For the contraction flow tests, the Carreau-Yasuda model was found to be a good fit for the OpenFOAM simulated pressure drop and streamlines with experimental data. Xanthan gum aqueous solutions were observed to act as a simpler viscoelastic fluid than the WCFs. There is a strong dependence on concentration with the viscoelasticity of the fluid. The Giesekus multi-mode model had been used to characterise the fluid. MatLabwas used to obtain a single set of parameters to capture the behaviour in both shear and extensional flow. The unified set of parameters were used on OpenFOAM using the contraction flow die geometry dimensions. The simulations had successfully obtained quantitative and qualitative data. However, discrepancy between the experimental and simulation pressure drops were found with a large vortex being visualised in the simulated streamlines at low piston speeds. Parallel superposition tests had shown the elasticity of xanthan gum decreasing when a controlled shear rate is applied to the fluid. From this, the relaxation time range was found and applied to the simulations. The simulation pressure drop was largely affected by the change in relaxation time and shown a closer value to the experimental data. The simulated streamlines, with the relaxation time limit, had shown a very small and absence of vortex at lower piston speeds. This matched well with the experimental streamline images using the capillary rheometer. For the spacer fluid used in the well construction process: DUO-VIS, the fluid is a grade of xanthan gum and was expected to produce similar rheological data. Yet, it was found DUO VIS presented a larger elastic modulus and much more entangled regime compared to the conventional xanthan gum. This dominance of elasticity caused difficulty in fitting the Giesekus model and thus, requires a more specific model for highly elastic polymer solutions such as the Phan-Thien-Tanner model. KCl Polymer was also analysed, a water based WCF, that presented a viscoelastic rheological behaviour. The Giesekus model was found to be a good fit with the experimental data and was used for simulations of the contraction geometry. Similar to xanthan gum, there was a large discrepancy between the simulated and experimental pressure drop data. The streamlines shown on OpenFOAM had also shown large vortex formations which were not visible in the experimental images. Using the parallel superposition tests, the elasticity is shown to decrease with increasing shear rates applied to KCl Polymer. Using the relaxation time limit, the simulations showed a much closer representation of streamlines to experimental and quantitively lower pressure drops. Despite this good match, it is noted that a multiphase model may be required for KCl Polymer when reaching high Reynolds numbers and Weissenberg numbers.