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The Effect of Impurities on γ-Alumina Chlorination in a Fluidized Bed Reactor: A CPFD Study
Get PDFAlumina is one of the most widely used materials today, with a total annual production of millions of tonnes of highly pure alumina. A large portion of this is used to make metal aluminum. Apart from that, a growing amount of alumina is used in ceramics, refractories, catalysts, and various other products. In nature, alumina can be found in different phases. These phases can be transformed into each other in different temperatures. Among these, γ-alumina is used in the chlorination process in the aluminum production industry because of the higher reaction rates. Previously, the chlorination of pure γ-alumina has been considered in the CPFD simulations. Extending previous researches, the present study investigates the effect of seven percent α-alumina impurity on the overall chlorination reaction, bed hydrodynamics, and composition of the outflow of the reactor. Commercial CPFD software Barracuda® v20.1.0 is used for the simulations. The results are compared with the pure γ-alumina simulations, and the results show that the impurity has no considerable effect on the chlorine concentration at the outlet. However, the mass balance of the bed shows an unfavorable accumulation of α-alumina in the fluidized bed reactor
Methanol Synthesis from Syngas: a Process Simulation
Get PDFMethanol is one of the major candidates to take over the petroleum based liquid transportation fuel. Methanol synthesis from syngas is proposed in this paper. The Aspen Plus simulation software was used to simulate the conversion process from syngas into methanol. A CSTR reactor with defined reaction kinetics was taken at 40 bar and 270°C to simulate the methanol synthesis. Hydrogen recycles gave an increase of 50.4% in the production of methanol as compared to the results without a H₂ recycle stream. The conversion of CO, CO₂ and H₂ are 50.4%, 99.8% and 100% respectively for the case with the H₂ recycle. Considering an operation of 8600 hr/year, the annual mass production of methanol is equal to 96492 tonnes for a feed rate of 154972 t/year. A distillation column is used to separate the methanol from water. Simulations were performed to calculate the minimum number of stages for the different recovery ratios of methanol in distillate and the required molar reflux ratio versus the purity of methanol in the distillate. The column temperature and the composition profile were analyzed for the column. The model provides the insights of the methanol synthesis plants for a specific quality and the quantity of methanol production
Evaluating the acceptability and accuracy of Phasepy as a Phyton framework to calculate the interfacial properties and phase equilibrium
Get PDFPhasepy is a scientifically defined open-source package in python for computational thermodynamics. Phasepy indeed calculates the interfacial properties and fluid phases equilibrium using an equation of state. In addition, Phasepy enables the scientists to optimize the relevant parameters to the equilibrium of multicomponent vapor-liquid, liquid-liquid, or vapor-liquid-liquid mixtures. The Phasepy can model the equilibrium in the continuous approach (combining a cubic equation of state and a mixing rule) or the discontinuous approach (using a virial equation and an activity coefficient model). So, this study is to develop a code in a continuous approach using a combination of Soave-Redlich-Kwong (SRK) or Peng Robinson (PR) as the equation of state and quadratic mixing rule (QMR) and modified-Huron-Vidal mixing rule (MHV) as the mixing Rule. Although the algorithm of the developed model is new, it is tried to utilize the predefined function of Phasepy to calculate fluid phase equilibrium and interfacial properties. In fact, the five well-performed previous experimental studies are modeled using Phasepy, and in the following, the outputs of the developed models are compared with the relevant experimental results. The bubble point features, dew point features, liquid and gas composition, and density of multicomponent mixtures are considered parameters in this extended study to evaluate the accuracy of the Phasepy function based on experimental results
Numerical modelling of fin side heat transfer and pressure loss for compact heat recovery steam generators
Get PDFAn optimization tool for offshore bottoming cycle and heat recovery steam generator (HRSG) design has previously been developed. The tool is based on empirical correlations to obtain hydraulic and thermal quantities for the HRSG. However, as these correlations are based on experiments with typical onshore designs, they may not be valid for the compact designs encountered in offshore HRSGs.
In order to extend the validity range of the optimization tool, this work presents a numerical model able to predict heat transfer and pressure loss in finned tube bundles by means of Computational Fluid Dynamics (CFD), utilizing a periodic domain to reduce computational costs. Both steady-state and transient models were applied, using the Spalart-Allmaras turbulence model, and their performance compared. To validate the model, results were compared with available experimental data, and then the model’s performance was compared with a selected empirical correlation.
Three different fin-tube geometries were investigated (two serrated and solid) with varying tube layout angles. A parameterized grid generation tool was developed and used to generate grids for the selected geometries. The CFD results were found to be within 20 % of the experimental data, and were in most cases more accurate than the empirical correlation. The steady-state simulations did, however, not converge for the geometry with the largest layout angle. The steady-state framework should therefore be applied only to compact tube layouts. The transient simulations, though being computationally more intensive, are also able to model large layout angles
Modelling of liquid injection of ammonia in a direct injector using Reynolds-averaged Navier–Stokes simulation
Get PDFAmmonia as a fuel has gotten attention in the past years to enable decarbonization for internal combustion engines. There is a need to understand the behavior of the liquid fuel in direct injection engines, a crucial step of the engine cycle. Injection impacts the mixture formation in the cylinder, equivalence ratio, combustion and pollutants formation. Liquid ammonia is expected to behave significantly different than traditional fuels during the injection phase and hence requires to be investigated. Indeed, recent experimental research has highlighted the appearance of flash boiling during injection of ammonia spray under engine-relevant conditions. The high volatility is also expected to influence the cavitation behavior. Cavitation is the partial vaporization of the liquid typically caused by locally increased velocity resulting in a pressure drop, when the fluid enters an orifice with sharp edges. One parameter controlling cavitation is therefore the geometry, but cavitation is also influenced by the fuel’s property and the boundary conditions. This study presents 3-D RANS simulations performed with CONVERGE CFD of the internal flow of a Gasoline Direct Injector (GDI), operating with liquid ammonia. The transient simulations account for the injector needle movement. Simulations capture the presence of both vapor and liquid in the nozzle head. These results from the simulations will provide input data for separate spray simulations with the same engine geometry, as well as support the development of a 0-D model that will be important for design purposes. Preliminary results predict a liquid discharge coefficient of less than 0.1 at the outlet for injection in atmospheric conditions
Method for mean-line design and performance prediction of one-stage axial turbines
Get PDFToday, expanders for Organic Rankine Cycles (ORC) are either inefficient or expensive. One reason for this is that expanders of power conversion systems usually operate under different conditions over an annual perspective. Still, they are designed to perform best at a single operating point. In view of this limitation, it is suggested that the overall expander performance can be improved by taking into account off-design operation in the design process. As the first step in this direction, this paper presents a two-fold method for design optimization and performance analysis of one-stage axial turbines. The method utilizes the same mean-line model for performance analysis and design optimization, ensuring consistency between the two modes. In addition, the proposed method evaluates the turbine performance at three stations for each blade row: inlet, throat and exit, and employs a novel numerical treatment of flow choking that automatically determines which blade rows are choked as part of the solution. Furthermore, the method was validated against cold-air experimental data from three different one-stage axial turbines, at both on- and off-design conditions. The model predicts design point efficiencies between 1.1 and 4.5 percentage points off the experimental values. The model was also able to capture the trend of mass flow rate as a function of total-to-static pressure ratio and angular speed. However, an unphysical behavior was observed as the pressure ratio approaches the critical value, and further developments of the model are required. It is envisioned that the proposed method will serve as foundation for a robust design methodology that will enable higher expander performance over a range of operating conditions
Development of an Interactive Communication Model with Integrated Teach-Back – using a web-based IT solution to create synergy between research and practice.
Get PDFThis paper describes the development of an easily applicable web-based IT solution that enhances interactive communication (the interactive communication model) and ensures comprehension in nursing practice. The model seeks to identify knowledge and skills allowing tailored communication and seeks to ensure comprehension and recall between nurses and patients. Results from testing the model shows that it has the potential to enhance self-care in citizens receiving community nursing and creates a basis for a more holistic nursing approach
Automatic Report Generation for Medical Images
Get PDFIn this work, we propose an encoder-decoder-based automatic report generation system capable of generating radiology reports for chest x-rays. We tested five backbone Convolutional Neural Networks, namely VGG16, InceptionV3, Resnet50, MobileNet and NasNet mobile, to extract visual features and used Long Short-Term Long Memory (LSTM) to extract the text features from the reports. Both features are concatenated and given to a deep network for report generation. We performed experiments on publicly available Indiana University’s NLMCXR dataset. We evaluated our system against different backbones and evaluated accuracy and BLEU score. The result showed that our method achieved reliable and convincing results
Valuing Actions and Ranking Hockey Players With Machine Learning
Get PDFA fundamental goal of sports analytics is to rank player performance. A common approach is to assign a value to each player action and rank a player by their aggregate action value. A recent AI-based approach is to measure the value of a player’s action by how much it increases their team’s chance of success, that is, their team’s chance of scoring the next goal. This requires a model that outputs a success probability estimate, given a match context and an action. This talk describes machine learning techniques for building success probability models from data. The techniques range from easy-to-implement probabilistic classifiers to advanced reinforcement learning methods. The results of success probability models are illustrated with action values and player rankings for the National Hockey League
Keeping Count: Archiving Women’s Hockey Analytics for Accessibility
Get PDFWomen’s hockey analytics has historically lacked a centralized repository for research, data, and other projects, despite other areas of hockey analytics having such central resources. In this paper, we attempt to fill in this missing piece of women’s hockey analytics by holding an archiving event in which volunteers methodologically gathered as many details on past women’s hockey research and data as possible. Each piece of research and data was then turned into an entry on MetaHockey according to standardized instructions. This event resulted in almost one hundred new women’s hockey focused entries on MetaHockey, whose characteristics largely align with trends in men’s hockey analytics. Examining these entries also empirically reveals an exponentially increase trend in women’s hockey analytics entries year-over-year, demonstrating that both continuing to archive works and taking advantage of this new research in the private and public spheres is conducive to the growth of the field