The Stereoselective synthesis of polyene natural products

A strategy for the stereocontrolled synthesis of polyene units was developed which centred around the chemistry of the vinyl boronate ester 4,4,6-trimethyl-2-vinyl-l,3,2- dioxaborinane 123. Reaction conditions have been developed to allow the Heck coupling of 123 with a range of aryl and alkenyl electrophiles. The reaction is promoted by cationic palladium species which can be generated through the addition of metal salts to the reaction mixture. Conversely conditions have also been developed which allows 123 to react exclusively at the boron functionality along the Suzuki-Miyaura pathway, the syntheses of a range of styrene and diene systems being demonstrated. Vinyl boronate 123 demonstrates complete chemoselectivity which is controlled by the reaction conditions employed. The alkenyl boronate esters, products of the Heck coupling of 123, can be converted to alkenyl iodides to produce the E- or Z-isomer with extremely high geometrical purity. This is done through an iododeboronation reaction involving ICI and NaOMe where the order of reagent addition determines the stereochemical outcome. Presented within is a detailed insight into the mechanistic intricacies of the transformations and the use of alternative and novel reagents such as pyridine-ICl for stereoselective iodo- and chlorodeboronation reactions is also demonstrated. This strategy was successfully applied to the syntheses of 1,6-diphenyl-1,3,5- . hexatrienes of varying alkene geometries 205-207, which were prepared from just iodobenzene and vinyl boronate 123 using those three key reactions. The use of this strategy also went some way to preparing the tetraene-containing natural product ixoric acid 124, although a total synthesis was not achieved during these studies. Research towards the first total synthesis of the natural product viridenomycin 125 was also conducted, especially towards the cyclopentenol core 246. An advanced intermediate cyclopentenone 248, was prepared from readily available starting materials along a succinct synthetic pathway to provide 248 in a good yield whilst expressing high diastereo- and enantioselectivity. Thus, a route was demonstrated which appears superior to those already existing in the literature

An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment

This paper is concerned with the implementation and field-testing of an edge device for real-time condition monitoring and fault detection for large-scale rotating equipment in the UK water industry. The edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters to help detect equipment wear before damage occurs. The paper describes the implementation of the edge system on a prototype microcontroller-based embedded platform, which supports the Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core, allowing further detailed analytics for predictive maintenance to take place. The paper first describes validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials on large-scale pumping equipment in the field. The paper concludes that the proposed system potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring and predictive maintenance applications in the water industry

Fuzzy systems in real-time condition monitoring and fault diagnosis, with a diesel engine case study

Diesel engines have become a common source of power, both for vehicles and for static equipment because they are fuel efficient, robust and reliable. It is important that diesel engines run in their correct condition and properly controlled in order to maintain efficiency, low emissions levels and high reliability.;The following thesis aims to assess the application of fuzzy systems in real-time condition monitoring and fault diagnosis. A 65kW diesel powered generator set has been purchased 'off the shelf' as an example of a typical application which may benefit from the development of CMFD techniques. As a test case, the diesel engine is appropriate as its sub-systems are complex, non-linear and subject to noise and uncertainty.;A diagnostic structure comprising fuzzy systems in three distinct roles has been proposed. Fuzzy reference models, incorporating heuristics and approximate non-linear mathematical relationships, are used for the generation of residuals by comparison with signals from a small number of low cost transducers. The residuals are classified and the diagnosis is inferred from the pattern of residual classes using a fuzzy rule-base. The diagnostic results obtained for three diesel engine sub-systems, show this to be a powerful technique for CMFD system design which may generalised, both for other types of plant and other forms of reference model.;This fuzzy model-based approach to fault diagnosis is shown to have benefits over other techniques by way of its transparency, ease of development, performance under variable engine load conditions, high level output and the lack of any requirement for fault data in the development process.;The robustness of the fuzzy reference models to certain fault conditions remains a key issue. The fuzzy models were generally effective at generating residuals where deviations from the normal condition are small. For larger deviations, robustness of models is not guaranteed or expected. A number of techniques were successfully deployed to reduce the number of misclassifications caused by this lack of robustness

Development of an intelligent system for detection of exhaust gas and vibration anomalies in gas turbines

An unplanned outage can be costly for a utility, and gas turbines are expensive pieces of equipment to repair or replace. It is therefore vital that anomalous behaviour is flagged before damage can occur that may cause a prolonged outage. An anomaly detection system is proposed for gas turbines to monitor the related parameters and raise alarms when anomalies are identified. The proposed system incorporates machine learning algorithms based on artificial neural networks (ANN). By using ANNs trained on normal plant behaviour, it is possible to identify anomalous behaviour by the high residuals between actual and predicted outputs. Within this paper, the data mining methodology is described and the process followed before arriving at the successful approach is documented. Results from testing the approach on an industrial case study are presented and, based on these results, areas for further development are identified. It is intended to deploy the system along with several other algorithms as part of a multi-agent system for plant-wide condition monitoring. This paper will focus on the design and testing of the developed anomaly detection system