MODEL-BASED CONTROL WITH STOCHASTIC SIMULATORS: BUILDING PROCESS DESIGN AND CONTROL SOFTWARE FOR CATALYTICALLY ENHANCED MICROSYSTEMS

The production, characteristics, dynamics, and economics of microreactors were studied in this report. Overall it was found that the best microfabrication techniques for small scale processes were laser ablation, the LIGA process, soft lithography, and anisotropic wet chemical etching, roughly in ascending order of effectiveness. One of the few viable bonding techniques was found to be diffusion bonding followed by microlamination, whereas many coating methods -- such as solgel coating, modified anodic oxidation, and electrophoretic deposition -- were effective in μTAS integration. The high surface area to volume ratio of microreactors enables precise control of the temperature of the reactor along its axial dimension. Taking advantage of this feature in the design of microreactors leads to better control of complex reaction networks and generates more valuable effluent streams. A model predictive controller was implemented for the common, archetypical reaction network involving the hydrogenation and dehydrogenation of cyclohexene with various control objectives. It was found that the highest rate of production of benzene and cyclohexane occurred at 600 K while the most pure stream of benzene occurred at 200 K. Model predictive control was found to be highly resistant to the inherent stochasticity of small scale processes. The market for a software-based controller for microreactors was surveyed and found to still be in the early stages of development. A profitability analysis was conducted for a start-up company using microreactors to make cyclohexane. A price of $18,000 for the product was found to be a reasonable selling price yet allowed the start-up to remain profitable

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Journal of Telecommunications and Information Technology

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Development of hydrate inhibition monitoring and initial formation detection techniques

Prevention of gas hydrate blockages is a major challenge posed to the petroleum industry because uncontrolled formation of hydrate may result in plugging of transport pipelines, causing considerable production loss and personnel safety hazard. Injection of hydrate inhibitors is the most common option to prevent hydrate formation. In current industrial practice the dosage of hydrate inhibitor is estimated and injected upstream without much downstream measurements. Therefore, hydrate blockages are still encountered in the oil and gas industry due to lack of any hydrate monitoring measures against unexpected changes. In this thesis, novel techniques have been developed for monitoring hydrate inhibition and detecting early signs of hydrate formation based on downstream sample analysis and online measurements. The main achievements of this study can be categorised as follow: 1. Hydrate Inhibition Monitoring Techniques: Three techniques, i.e. conductivity-velocity (C-V) technique, water activity technique and water content technique, have been developed for determining optimising inhibitor injection rates 2. Initial Hydrate Formation Detection Techniques: The main objective of detecting early signs of hydrate formation is to give the operators adequate time to prevent hydrate formation and start remediation actions. Two techniques including the onset of hydrate formation and compositional change have been developed for detecting initial hydrate formation 3. Development of prototypes: Following the above fundamental studies, prototypes of the CV and water activity methods have been developed The development of hydrate inhibition monitoring and early hydrate formation detection techniques opens a novel flow assurance approach for the oil and gas industry. The developed hydrate monitoring techniques like the C-V technique, water activity and content techniques can be used to optimise hydrate inhibitor injection. In the near future, further development of the investigated early hydrate formation detection techniques like gas compositional change technique could provide an effective measure to minimise the risk of hydrate blockage