To extensively explore the advantage of using nanobubbles in the ElMaghara coal flotation process, the effect of nanobubbles on both column and mechanical flotation was investigated under different operating parameters such as diesel oil collector dosage, MIBC frother concentration, superficial feed velocity, superficial air velocity, and superficial wash water velocity in column flotation; besides the slurry flow rate through nanobubbles generator into a 25-liter mechanical flotation cell. The representative coal flotation feed acquired from the El-Maghara deposit located in Sinai, Egypt with chemical characterization using proximate analysis containing 25.27% mineral matter forming ash during coal combustion and with particle size distribution measurement using laser particle size analyzer is 57 µm d90. Also, the flotation kinetic experiments were done to show the influence of nanobubbles on the flotation time required to obtain high-quality coal products with high combustible recovery. Nanobubbles enhanced the flotation performance and kinetics by up to 24% combustible recovery based on the operation parameters reducing flotation time from 4 to 2.25 min for 80% combustible recovery.

Millimeter wave (mmWave) massive MIMO systems will be used extensively in future communications systems to provide high data rates. For such systems, hybrid precoders are preferred to fully digital precoders in decreasing the cost and energy consumption. In this paper, we propose a partially connected hybrid precoding design on the orthogonal matching pursuit (OMP) algorithm. The proposed algorithm accounts for the limitations of analog beamforming circuitry and assumes channel state information (CSI) at both the base and mobile stations. Simulation results show that the proposed algorithm is superior to other solutions in the literature including the nearest Kronecker product (NKP) algorithm and successive interference cancellation (SIC) method. The proposed algorithm provides a higher data rate compared to other methods. In addition, the proposed design offers higher energy efficiency than the fully …

The topic of water management and distribution is addressed in this study with an IoTs (Internet of Things)-based solution. In typical systems, water distribution and consumption are not seen instantaneously, which slows the process of finding leaks desirable. An actual prototype that abstracts the Water Distribution Network (WDN) is created. To measure the desired physical quantities, namely, water flow rates, pH, and turbidity, sensors are mounted on the WDN. Hence, a network is installed to transmit the signal data to the Firebase platform. In this regard, a comprehensive IoT testbed design is developed to connect all the IoT devices. To grasp the leakage situation and control the water quality, actuators are fitted on the piping system in specified locations to cover the entire pipe network. Leakage and low water quality scenarios are conducted to test the capability of the developed prototype. The results show that …

In critical energy infrastructures, Insulated Gate Bipolar Transistors (IGBTs) serve as essential components but are prone to unexpected failures. Precise estimation of the Remaining Useful Lifetime (RUL) of IGBTs is imperative for implementing predictive maintenance and assuring system reliability. This paper presents an innovative GPU-based approach for real-time health monitoring and lifetime prediction of IGBTs. The study explores a range of machine learning algorithms to determine the most effective one for precise lifetime prediction. Contrary to prior studies that concentrated on singular sensor data to minimize complexity and resource expenditure, this research leverages the capabilities of modern, economical, and robust GPUs to facilitate a data-driven, multi-sensor monitoring framework. The application of this approach has the potential to substantially bolster the reliability of energy infrastructure, notably in hydrogen plant. The paper conducts an exhaustive analysis of both single-variable and multivariate machine learning models, including Random Forest (RF), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNNs), operating in real-time on edge GPUs. It also assesses the performance of two distinct GPU architectures - the NVIDIA Jetson Nano and Jetson Orin - in executing these machine learning algorithms.

In the context of critical energy infrastructures (e.g., hydrogen infrastructure) that extensively utilize power converters, the need for reliable and accurate monitoring is of paramount importance. Addressing this necessity, this paper presents a novel GPU-based multivariate approach to Insulated Gate Bipolar Transistor (IGBT) lifetime prediction. Despite the substantial technological advances in the field, accurately predicting the lifetime of IGBTs remains a significant challenge. Current methods often rely on single precursor variable models, which can lack the precision required in demanding power electronic applications. In contrast, this study utilizes multiple precursor variables (V CE(ON) and case temperature) to achieve more accurate results. Initial results using NASA's open-source dataset, and Gaussian Process Regression (GPR) reveal that our multivariate model outperforms its single-variable counterparts in prediction accuracy. Furthermore, the paper elaborates on the development of a small-scale experimental setup to enable real-time health monitoring of the IGBT. It uses an NVIDIA Jetson Nano GPU to handle real-time data from V CE(ON) and Case Temperature. A pre-trained random forest model based on V CE(ON) and Case Temperature profile data is deployed in NVIDIA Jetson Nano GPU for real-time prediction of the remaining useful life (RUL) of the IGBT, laying the groundwork for future implementation of the real-time multivariate model. The findings from this research highlight the significant potential for enhancing IGBT lifetime prediction accuracy through GPU-based multivariate models