<p>Examensarbetet behandlar en modellering av hyttgasnätet vid SSAB Oxelösund AB. Det objektorienterade modelleringsspråket Modelica används. Modellen valideras statiskt och dynamiskt för olika driftsfall med gott resultat. </p><p>Hyttgasen används delvis för generering av elektricitet i kraftverket. Simuleringar av modellen visar att hyttgasnätets konstruktion begränsar inflödet av hyttgas till kraftverket. Modellen används för att studera förändringar av hyttgasnätet som kan öka inflödet till kraftverket. I examensarbetet föreslås byte av de befintliga brännarna i kraftverket, införande av två extra brännare eller införande av en tryckstegringsfläkt. Samtliga förändringar möjliggör en ökad elproduktion i kraftverket, detta leder till ökad vinst för SSAB. </p><p>Olika driftsstörningar i hyttgasnätet simuleras för att ge förslag på åtgärder som kan minska störningen. Ett snabbstopp av kraftverket ger en tryckstegring i nätet. Störningen kan minskas genom att facklorna regleras av trycket i gasnätet, tryckregleringen kombineras med dagens reglering mot gasklockans nivå. När ett snabbstopp i blåsmaskinen studeras kan en modifiering av regulatorn till reglerspjället göras. Förändringen medför att gasklockans nivå stabiliseras. </p><p>För att minska förlusterna av gas bör facklornas reglering ändras. Facklornas reglering kan förbättras genom att fördelningen av den hyttgas som ska förbrännas i facklorna ändras. Om förbättringen införs i hyttgasnätet minskas förlusterna av hyttgas och elproduktionen i kraftverket kan ökas.</p>

Examensarbetet behandlar en modellering av hyttgasnätet vid SSAB Oxelösund AB. Det objektorienterade modelleringsspråket Modelica används. Modellen valideras statiskt och dynamiskt för olika driftsfall med gott resultat. Hyttgasen används delvis för generering av elektricitet i kraftverket. Simuleringar av modellen visar att hyttgasnätets konstruktion begränsar inflödet av hyttgas till kraftverket. Modellen används för att studera förändringar av hyttgasnätet som kan öka inflödet till kraftverket. I examensarbetet föreslås byte av de befintliga brännarna i kraftverket, införande av två extra brännare eller införande av en tryckstegringsfläkt. Samtliga förändringar möjliggör en ökad elproduktion i kraftverket, detta leder till ökad vinst för SSAB. Olika driftsstörningar i hyttgasnätet simuleras för att ge förslag på åtgärder som kan minska störningen. Ett snabbstopp av kraftverket ger en tryckstegring i nätet. Störningen kan minskas genom att facklorna regleras av trycket i gasnätet, tryckregleringen kombineras med dagens reglering mot gasklockans nivå. När ett snabbstopp i blåsmaskinen studeras kan en modifiering av regulatorn till reglerspjället göras. Förändringen medför att gasklockans nivå stabiliseras. För att minska förlusterna av gas bör facklornas reglering ändras. Facklornas reglering kan förbättras genom att fördelningen av den hyttgas som ska förbrännas i facklorna ändras. Om förbättringen införs i hyttgasnätet minskas förlusterna av hyttgas och elproduktionen i kraftverket kan ökas.

Le soudage FSW (Friction Stir Welding) est un procédé de soudage en phase solide pressenti pour des applications de transport en générale aérospatial et naval. Malgré le nombre considérable d’études qui ont été réalisées depuis son avènement en 1991, le contrôle du procédé n’est pas encore effectif.Ce travail a consisté en une partie expérimentale visant à la génération, par un outil trigone, de joints soudés dont la microstructure a été corrélée à l’écoulement de matière pendant le procédé. La connaissance de cet écoulement de matière a permis dans la deuxième partie d’enrichir le modèle thermofluide développé en périodique pour prédire la microstructure des joints de soudure FSW, notamment les "onion rings". Finalement, l’occurrence des "onion rings" a été corrélée à la vitesse de déformation maximale atteinte par les particules de la zone soudée, prédite par le modèle. Par ailleurs, un travail d’affinement du champ de vitesse en voisinage du pion est réalisé en modélisant l’outil trigone. Ce qui permet en plus de l’interaction (entrainement) outil/matière par frottement, d’intégrer une interaction par obstacle. Cette approche devrait permettre, en perspectives de ce travail, une meilleur description thermomécanique locale et par voie de conséquence microstructurale. / Friction Stir Welding is a solid state joining process developed for transport applications as aerospace and naval. Since its introduction, a large number of investigations have been carried out but the process is not fully controlled. This work including experimental section in which welds have been generated by trigonal tool. The microstructure of these welds has been correlated with the material flow during the process. By understanding the material flow, the transient thermofluid model developed in the second section has been significantly enriched. This modeled has been developed for predicting the microstructure of the weld, especially, the "onion rings". Finally, the occurrence of "onion rings" has been correlated with the maximal strain rate reached by any particle in the weld seam, simulated by the model. However, the velocity has been refined at the vicinity of the tool through the trigonal pin modelling. This was helpful to move the material not only by friction but also by obstacle at the interaction tool/material. The above approach should enable, in this work layout, a better local thermomechanical description and consequently microstructural.

This research investigates the use of an alternate means of modelling the performance of a train of feed water heaters in a steam cycle power plant, using machine learning. The goal of this study was to use a simple artificial neural network (ANN) to predict the behaviour of the plant system, specifically the inlet bled steam (BS) mass flow rate and the outlet water temperature of each feedwater heater. The output of the model was validated through the use of a thermofluid engineering model built for the same plant. Another goal was to assess the ability of both the thermofluid model and ANN model to predict plant behaviour under out of normal operating circumstances. The thermofluid engineering model was built on FLOWNEX® SE using existing custom components for the various heat exchangers. The model was then tuned to current plant conditions by catering for plant degradation and maintenance effects. The artificial neural network was of a multi-layer perceptron (MLP) type, using the rectified linear unit (ReLU) activation function, mean squared error (MSE) loss function and adaptive moments (Adam) optimiser. It was constructed using Python programming language. The ANN model was trained using the same data as the FLOWNEX® SE model. Multiple architectures were tested resulting in the optimum model having two layers, 200 nodes or neurons in each layer with a batch size of 500, running over 100 epochs. This configuration attained a training accuracy of 0.9975 and validation accuracy of 0.9975. When used on a test set and to predict plant performance, it achieved a MSE of 0.23 and 0.45 respectively. Under normal operating conditions (six cases tested) the ANN model performed better than the FLOWNEX® SE model when compared to actual plant behaviour. Under out of normal conditions (four cases tested), the FLOWNEX SE® model performed better than the ANN. It is evident that the ANN model was unable to capture the “physics” of a heat exchanger or the feed heating process as a result of its poor performance in the out of normal scenarios. Further tuning by way of alternate activation functions and regularisation techniques had little effect on the ANN model performance. The ANN model was able to accurately predict an out of normal case only when it was trained to do so. This was achieved by augmenting the original training data with the inputs and results from the FLOWNEX SE® model for the same case. The conclusion drawn from this study is that this type of simple ANN model is able to predict plant performance so long as it is trained for it. The validity of the prediction is highly dependent on the integrity of the training data. Operating outside the range which the model was trained for will result in inaccurate predictions. It is recommended that out of normal scenarios commonly experienced by the plant be synthesised by engineering modelling tools like FLOWNEX® SE to augment the historic plant data. This provides a wider spectrum of training data enabling more generalised and accurate predictions from the ANN model.

Air-cooled condensers (ACCs) are a type of dry-cooling technology that has seen an increase in implementation globally, particularly in the power generation industry, due to its low water consumption. Unfortunately, ACC performance is susceptible to changing ambient conditions, such as dry bulb temperatures, wind direction, and wind speeds. This can result in performance reduction under adverse ambient conditions, which leads to increased turbine back pressures and in turn, a decrease in generated electricity. Therefore, this creates a demand to monitor and predict ACC performance under changing ambient conditions. This study focuses on modelling a utility-scale ACC system at steady-state conditions applying a 1-D network modelling approach and using a component-level discretization approach. This approach allowed for each cell to be modelled individually, accounting for steam duct supply behaviour, and for off-design conditions to be investigated. The developed methodology was based on existing empirical correlations for condenser cells and adapted to model double-row dephlegmators. A utility-scale 64-cell ACC system based in South Africa was selected for this study. The thermofluid network model was validated using site data with agreement in results within 1%; however, due to a lack of site data, the model was not validated for off-design conditions. The thermofluid network model was also compared to the existing lumped approach and differences were observed due to the steam ducting distribution. The effect of increasing ambient air temperature from 25 35  −  C C was investigated, with a heat rejection rate decrease of 10.9 MW and a backpressure increase of 7.79 kPa across the temperature range. Condensers' heat rejection rate decreased with higher air temperatures, while dephlegmators' heat rejection rate increased due to the increased outlet vapour pressure and flow rates from condensers. Off-design conditions were simulated, including hot air recirculation and wind effects. For wind effects, the developed model predicted a decrease in heat rejection rate of 1.7 MW for higher wind speeds, while the lumped approach predicted an increase of 4.9 . MW For practicality, a data-driven surrogate model was developed through machine learning techniques using data generated by the thermofluid network model. The surrogate model predicted systemlevel ACC performance indicators such as turbine backpressure and total heat rejection rate. Multilayer perceptron neural networks were developed in the form of a regression network and binary classifier network. For the test sets, the regression network had an average relative error of 0.3%, while the binary classifier had a 99.85% classification accuracy. The surrogate model was validated to site data over a 3 week operating period, with 93.5% of backpressure predictions within 6% of site data backpressures. The surrogate model was deployed through a web-application prototype which included a forecasting tool to predict ACC performance based on a weather forecast.