Novel or unconventional technologies are critical to providing cost-competitive natural gas supplies to meet rising demands and provide more opportunities to develop low-quality gas fields with high contaminants, including high carbon dioxide (CO2) fields. High nitrogen concentrations that reduce the heating value of gaseous products are typically associated with high CO2 fields. Consequently, removing nitrogen is essential for meeting customers' requirements. The intensification approach with a rotating packed bed (RPB) demonstrated considerable potential to remove nitrogen from natural gas under cryogenic conditions. Moreover, the process significantly reduces the equipment size compared to the conventional distillation column, thus making it more economical. The prediction model developed in this study employed artificial neural networks (ANN) based on data from in-house experiments due to a lack of available data. The ANN model is preferred as it offers easy processing of large amounts of data, even for more complex processes, compared to developing the first principal mathematical model, which requires numerous assumptions and might be associated with lumped components in the kinetic model. Backpropagation algorithms for ANN Lavenberg-Marquardt (LM), scaled conjugate gradient (SCG), and Bayesian regularisation (BR) were also utilised. Resultantly, the LM produced the best model for predicting nitrogen removal from natural gas compared to other ANN models with a layer size of nine, with a 99.56% regression (R2) and 0.0128 mean standard error (MSE).
A rotating packed bed (RPB) is an innovative intensification technology that improves its separation capabilities in high-gravity conditions. This process increases efficiency with smaller equipment size and footprint than conventional packed columns. Although significant advancements have been made regarding RPBs, most studies only focused on single or dual rotor configurations in addressing dry pressure drop. Hence, multiple rotor systems in industrial settings can enhance economic efficiency by minimizing the necessity for numerous RPBs. This study investigated the pressure drops and holdup in a three-stage rotor-based RPB under actual process conditions using natural gas as the feed. A novel pressure drop correlation was introduced based on the nitrogen removal process from the natural gas in continuous RPB distillation operations. Consequently, the correlation between centrifugal acceleration, turbulent, and momentum effects demonstrated remarkable accuracy within ±15%. This outcome also highlighted the importance of meticulous design considerations in RPB-based applications due to the complex correlation between centrifugal forces, liquid holdup, and gas flow rates. The reflux feed ratio, liquid holdup, rotating speed, and F-factor effects were examined to comprehend the RPB distillation process. Overall, the correlations between the critical parameters offered crucial insights to prevent process upsets (such as flooding), contributing to advancing RPBs in practical industrial settings.
The identification and segmentation of inhomogeneous image regions is one of the most challenging issues nowadays. The surface vessels of the human heart are important for the surgeons to locate the region where to perform the surgery and to avoid surgical injuries. In addition, such identification, segmentation, and visualisation helps novice surgeons in the training phase of cardiac surgery.
Computerized tomographic angiography (3D data representing the coronary arteries) and X-ray angiography (2D X-ray image sequences providing information about coronary arteries and their stenosis) are standard and popular assessment tools utilized for medical diagnosis of coronary artery diseases. At present, the results of both modalities are individually analyzed by specialists and it is difficult for them to mentally connect the details of these two techniques. The aim of this work is to assist medical diagnosis by providing specialists with the relationship between computerized tomographic angiography and X-ray angiography.