The proton exchange membrane fuel cell (PEMFC) is the fastest growing fuel cell technology on the market. Due to their sustainable nature, PEMFCs are widely adopted as a renewable energy resource. Fabricating a PEMFC is a costly process; hence, mathematical modeling and simulations are necessary in order to fully optimize its performance. Alongside this, the feasibility of a waste heat recovery system based on the organic Rankine cycle is also studied and power generation for different operating conditions is presented. The fuel cell produces a power output of 1198 W at a current of 24A. It has 50% efficiency and hence produces an equal amount of waste heat. That waste heat is used to drive an organic Rankine cycle (ORC), which in turn produces an additional 428 W of power at 35% efficiency. The total extracted power hence stands at 1626 W. MATLAB/Simulink R2016a is used for modeling both the fuel cell and the organic Rankine cycle.

The accurate prediction of aerodynamic properties is an essential requirement for the design of applications that involve fluid flows, especially in the aerospace industry. The aerodynamic characteristics of fluid flows around a wing or an airfoil are usually forecasted using the numerical solution of the Reynolds-averaged Navier–Stokes equation. However, very heavy computational expenses and lengthy progression intervals are associated with this method. Advancements in computational power and efficiency throughout the present era have considerably reduced these costs; however, for many practical applications, performing numerical simulations is still a very computationally expensive and time-consuming task. The application of machine learning techniques has seen a sharp rise in various fields over recent years, including fluid dynamics, and they have proved their worth. In the present study, a famous machine learning model that is known as the back-propagation neural network was implemented for the prediction of the aerodynamic coefficients of airfoils. The most important aerodynamic properties of the coefficient of lift and the coefficient of drag were predicted by providing the model with the name, flow Reynolds number, Mach number and the angle of attack of the airfoils with respect to the incoming flows as input parameters. The dataset for the current study was obtained by performing CFD simulations using the RANS-based Spalart–Allmaras turbulence model on four different NACA series airfoils under varying aerodynamic conditions. The data that were obtained from the CFD simulations were divided into two subsets: 70% were used as training data and the remaining 30% were used as validation and testing data. The BPNN showed promising results for the prediction of the aerodynamic coefficients of airfoils under different conditions. An RMSE value of 3.57 $\times$ 10⁻⁷ was achieved for the best performance validation case with 28 epochs when there were 10 neurons in the hidden layer. The regression plot also depicted a close to perfect fit between the predicted and actual values for the regression curves.

The Saudi population is at high risk of multimorbidity. The risk of these morbidities can be reduced by identifying common modifiable behavioural risk factors. This study uses statistical and machine learning methods to predict factors for multimorbidity in the Saudi population. Data from 23,098 Saudi residents were extracted from the “Sharik” Health Indicators Surveillance System 2021. Participants were asked about their demographics and health indicators. Binary logistic models were used to determine predictors of multimorbidity. A backpropagation neural network model was further run using the predictors from the logistic regression model. Accuracy measures were checked using training, validation, and testing data. Females and smokers had the highest likelihood of experiencing multimorbidity. Age and fruit consumption also played a significant role in predicting multimorbidity. Regarding model accuracy, both logistic regression and backpropagation algorithms yielded comparable outcomes. The backpropagation method (accuracy 80.7%) was more accurate than the logistic regression model (77%). Machine learning algorithms can be used to predict multimorbidity among adults, particularly in the Middle East region. Different testing methods later validated the common predicting factors identified in this study. These factors are helpful and can be translated by policymakers to consider improvements in the public health domain.

Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process.

The issue of depleting fossil fuels has emphasized the use of renewable energy. Multigeneration systems fueled by renewables such as geothermal, biomass, solar, etc., have proven to be cutting-edge technologies for the production of different valuable by-products. This study proposes a multigeneration system using a geothermal source of energy. The main outputs include power, space heating, cooling, fresh and hot water, dry air, and hydrogen. The system includes a regenerative Rankine cycle, a double effect absorption cycle and a double flash desalination cycle. A significant amount of electrical power, hydrogen and fresh water is generated, which can be used for commercial or domestic purposes. The power output is 103 MW. The thermal efficiency is 24.42%, while energetic and exergetic efficiencies are 54.22% and 38.96%, respectively. The COP_en is found to be 1.836, and the COP_ex is found to be 1.678. The hydrogen and fresh water are produced at a rate of 0.1266 kg/s and 37.6 kg/s, respectively.

Tool health monitoring (THM) is in great focus nowadays from the perspective of predictive maintenance. It prevents the increased downtime due to breakdown maintenance, resulting in reduced production cost. The paper provides a novel approach to monitoring the tool health of a computer numeric control (CNC) machine for a turning process using airborne acoustic emission (AE) and convolutional neural networks (CNN). Three different work-pieces of aluminum, mild steel, and Teflon are used in experimentation to classify the health of carbide and high-speed steel (HSS) tools into three categories of new, average (used), and worn-out tool. Acoustic signals from the machining process are used to produce time–frequency spectrograms and then fed to a tri-layered CNN architecture that has been carefully crafted for high accuracies and faster trainings. Different sizes and numbers of convolutional filters, in different combinations, are used for multiple trainings to compare the classification accuracy. A CNN architecture with four filters, each of size 5 × 5, gives best results for all cases with a classification average accuracy of 99.2%. The proposed approach provides promising results for tool health monitoring of a turning process using airborne acoustic emission.