Microstructure and Texture Evolution of Cryo Rolled and Annealed Ductile TaNbHfZrTi Refractory High Entropy Alloy

The microstructure and texture evolution of cryo rolled and annealed ductile TaHfNbZrTi refractory high entropy alloy was investigated. To obtain that, the alloy is severely cryo rolled and subsequently annealed for the recrystallization process. The cryo rolled – 90% shows the presence of very fine grains and microstructural heterogeneity. The cryo rolled samples are annealed at a temperature ranging from 800°C to 1400°C, the partial recrystallization is observed at 800°C annealed condition, and at higher annealing temperatures the complete recrystallization process is noticed. The development of ND fiber texture is observed after the annealing.

Impact of Fluid Flow Patterns on Metastable Zone Width of Borax in Dual Radial Impeller Crystallizer at Different Impeller Spacings

Conducting crystallization in an agitated vessel requires a proper selection of mixing parameters that would result in a production of crystals of specific properties. In dual impeller systems, which are characterized by a more complex hydrodynamics due to the possible fluid flow interactions, revealing a clear link between mixing parameters and crystallization kinetics is still an open issue. The aim of this work is to establish this connection by investigating how fluid flow patterns, generated by two impellers mounted on the same shaft, reflect on metastable zone width of borax decahydrate, one of the most important parameters of the crystallization process. Investigation was carried out in a 15-dm3 bench scale batch cooling crystallizer with an aspect ratio (H/T) equal to 1.3. For this reason, two radial straight blade turbines (4-SBT) were used for agitation. Experiments were conducted at different impeller spacings at the state of complete suspension. During the process of an unseeded batch cooling crystallization, solution temperature and supersaturation were continuously monitored what enabled a determination of the metastable zone width. Hydrodynamic conditions in the vessel achieved at different impeller spacings investigated were analyzed in detail. This was done firstly by measuring the mixing time required to attain the desired level of homogeneity. Secondly, fluid flow patterns generated in a described dual impeller system were both photographed and simulated by VisiMix Turbulent software. Also, a comparison of these two visualization methods was performed. Experimentally obtained results showed that metastable zone width is definitely affected by the hydrodynamics in the crystallizer. This means that this crystallization parameter can be controlled not only by adjusting the saturation temperature or cooling rate, as is usually done, but also by choosing a suitable impeller spacing that will result in a formation of crystals of wanted size distribution.

Preparation of Protective Coating Film on Metal Alloy

A novel chromium-free protective coating films based on a zeolite coating was growing onto a FeCrAlloy metal using in – situ hydrothermal method. The zeolite film was obtained using in-situ crystallization process that is capable of coating large surfaces with complex shape and in confined spaces has been developed. The zeolite coating offers an advantage of a high mechanical stability and thermal stability. The physicochemical properties were investigated using X-ray diffraction (XRD), Electron Microscopy (SEM), Energy Dispersive X–ray Analysis (EDX) and Thermogravimetric Analysis (TGA). The transition from oxide-on-alloy wires to hydrothermally synthesised uniformly zeolite coated surfaces was followed using SEM and XRD. In addition, the robustness of the prepared coating was confirmed by subjecting these to thermal cycling (ambient to 550oC).

Control Improvement of a C Sugar Cane Crystallization Using an Auto-Tuning PID Controller Based on Linearization of a Neural Network

The industrial process of the sugar cane crystallization produces a residual that still contains a lot of soluble sucrose and the objective of the factory is to improve its extraction. Therefore, there are substantial losses justifying the search for the optimization of the process. Crystallization process studied on the industrial site is based on the “three massecuites process". The third step of this process constitutes the final stage of exhaustion of the sucrose dissolved in the mother liquor. During the process of the third step of crystallization (Ccrystallization), the phase that is studied and whose control is to be improved, is the growing phase (crystal growth phase). The study of this process on the industrial site is a problem in its own. A control scheme is proposed to improve the standard PID control law used in the factory. An auto-tuning PID controller based on instantaneous linearization of a neural network is then proposed.

Neural Network-Based Control Strategies Applied to a Fed-Batch Crystallization Process

This paper is focused on issues of process modeling and two model based control strategies of a fed-batch sugar crystallization process applying the concept of artificial neural networks (ANNs). The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. Two control alternatives are considered – model predictive control (MPC) and feedback linearizing control (FLC). Adequate ANN process models are first built as part of the controller structures. MPC algorithm outperforms the FLC approach with respect to satisfactory reference tracking and smooth control action. However, the MPC is computationally much more involved since it requires an online numerical optimization, while for the FLC an analytical control solution was determined.

Application of Feed Forward Neural Networks in Modeling and Control of a Fed-Batch Crystallization Process

This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.