Application of new chemical test reactions to study mass transfer from shrinking droplets and micromixing in the rotor-stator mixer

A pair of fast competitive reactions, neutralization and 2,2-dimetoxypropane (DMP) hydrolysis, has been applied do study mass transfer and micromixing in a T 50 Ultra-Turrax® - IKA rotor-stator device. In experiments the dispersed organic phase containing p-Toluenesulfonic acid (pTsOH) dissolved in diisopropyl ether, whereas the continuous phase was represented by the aqueous solution of sodium hydroxide, 2,2-dimetoxypropane (DMP) and ethanol. During mixing a fast mass transfer of a solute (pTsOH) from organic phase droplets, which were shrinking due to fast dissolution of the organic solvent, was followed by micromixing and chemical reactions in the continuous phase. Measured hydrolysis yields were applied to express effects of mixing on the course of chemical reactions. Modeling was based on application of models describing drop breakup, mass transfer in the liquid-liquid system and micromixing. Combined effects of mass transfer and drop breakage on drop population were expressed using the population balance equations. The model has been used to interpret experimental results, in particular to identify the efficiency of mixing

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

To date, significant progress has been made in the field of railway anomaly detection using technologies such as real-time data analytics, the Internet of Things, and machine learning. As technology continues to evolve, the ability to detect and respond to anomalies in railway systems is once again in the spotlight. However, railway anomaly detection faces challenges related to the vast infrastructure, dynamic conditions, aging infrastructure, and adverse environmental conditions on the one hand, and the scale, complexity, and critical safety implications of railway systems on the other. Our study is underpinned by the three objectives. Specifically, we aim to identify time series anomaly detection methods applied to railway sensor device data, recognize the advantages and disadvantages of these methods, and evaluate their effectiveness. To address the research objectives, the first part of the study involved a systematic literature review and a series of controlled experiments. In the case of the former, we adopted well-established guidelines to structure and visualize the review. In the second part, we investigated the effectiveness of selected machine learning methods. To evaluate the predictive performance of each method, a five-fold cross-validation approach was applied to ensure the highest accuracy and generality. Based on the calculated accuracy, the results show that the top three methods are CatBoost (96%), Random Forest (91%), and XGBoost (90%), whereas the lowest accuracy is observed for One-Class Support Vector Machines (48%), Local Outlier Factor (53%), and Isolation Forest (55%). As the industry moves toward a zero-defect paradigm on a global scale, ongoing research efforts are focused on improving existing methods and developing new ones that contribute to the safety and quality of rail transportation. In this sense, there are at least four avenues for future research worth considering: testing richer data sets, hyperparameter optimization, and implementing other methods not included in the current study