<p>In this thesis a mixture density network has been constructed to predict steel hardenability for a given alloy composition. Throughout the work hardenability is expressed in terms of jominy profiles according to the standard jominy test. A piecewise linear description of the jominy profile has been developed to solve the problem of missing data, model identification from data based on different units and measurement uncertainty. When the underlying physical processes are complex and not well understood, as the case with hardenability modelling, mixture density networks, which are an extension of neural networks, offer a strong non-linear modelling alternative. Mixture density networks model conditional probability densities, from which it is possible to determine any statistical property. Here the model output is presented in terms of expectation values along with confidence interval. This statistical output facilitates future extension of the model towards optimisation of alloy cost. A good agreement has been obtained between the experimental and the calculated data. In order to ensure the reliability of the model in service, novelty detection of the input data is performed.</p>

In this thesis a mixture density network has been constructed to predict steel hardenability for a given alloy composition. Throughout the work hardenability is expressed in terms of jominy profiles according to the standard jominy test. A piecewise linear description of the jominy profile has been developed to solve the problem of missing data, model identification from data based on different units and measurement uncertainty. When the underlying physical processes are complex and not well understood, as the case with hardenability modelling, mixture density networks, which are an extension of neural networks, offer a strong non-linear modelling alternative. Mixture density networks model conditional probability densities, from which it is possible to determine any statistical property. Here the model output is presented in terms of expectation values along with confidence interval. This statistical output facilitates future extension of the model towards optimisation of alloy cost. A good agreement has been obtained between the experimental and the calculated data. In order to ensure the reliability of the model in service, novelty detection of the input data is performed.

Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to apply probabilistic deep learning to power plant operations using historic plant data. The first study presented, entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, steam temperatures, flow rates and pressures are used to predict the HTCs. In the second case study, an encoder-decoder mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both case studies, hyperparameter searches are done to determine the best performing architectures for these models. Comparisons are drawn between the MDN model versus standard model architecture in both case studies. The HTC predictor model achieved 90% accuracy which equates to an average error of 4.89 W m2K across all heat exchangers. The resultant time-series ACC model achieved an average error of 3.14 kPa, which translate into a model accuracy of 82%.