Atmospheric water generation using desiccant materials is a promising technology for producing clean drinking water in water-scarce regions. While experimental research on this topic has been extensive, modeling and simulation research are still in their nascent stages. The development of accurate models and simulations is crucial for predicting performance and refining system design. This paper presents analytical solutions for predicting and improving the behavior of water absorption and desorption by the calcium chloride (CaCl2) desiccant, which is commonly used in atmospheric water generation systems. The model considers several physical effects, such as mass transfer, and diffusion. The model considers a linear relationship between the collected water content and relative humidity. Based on this assumption the model has been solved analytically for different cases of boundary conditions including, Dirichlet boundary conditions and Dirichlet–Neumann boundary conditions. Several physical scenarios have been calculated and the results have been discussed.

This paper proposes a new multi-level entropy-based image thresholding method. The key principle of the proposed method depends on the minimum of the variance entropy. The method is fully automated at all stages of implementation. It produces competitive segmentation results as compared to the generalized Otsu’s method, which is one of the most powerful multi-level thresholding techniques that requires human intervention. In addition, the method significantly outperforms the generalized Kapur’s method, which is one of the benchmarking entropy-based thresholding techniques. The method is successfully applied to several scenarios of trial histograms and real images, and its performance is checked using a variety of classification measures and quality metrics.

In accelerated life testing analysis with nonthermal accelerating stress, the inverse power law (IPL) is often solely merged with a particular lifetime probability distribution with a shape parameter. Although many fundamental lifetime distributions, such as the normal distribution, are excellent fits to the experimental lifetime data, they have not been considered as they lack the shape parameter. As such, this paper, for the first time, demonstrates that the shape parameter can be replaced by the coefficient of variation, allowing the use of normal distributions in this context. The work further introduces the IPL-normal model in a rigorous mathematical setup that precisely leads to the least squares estimating equations and maximum likelihood estimates of the IPL-normal accelerating parameters and the general coefficient of variation. The proposed model uses accelerated experimental data to successfully predict the lifetime of organic light-emitting diodes (OLEDs) at use conditions. Based on these fundamentals, the predictions are benchmarked with prior works that were validated by market studies.

Harvesting atmospheric water by solar regenerated desiccants is a promising water source that is energy-efficient, environmentally clean, and viable. However, the generated amounts of water are still insignificant. Therefore, more intensive fundamental research must be undertaken involving experiments and modeling. This paper describes several experiments, which were conducted to predict and improve the behavior of water absorption/desorption by the Calcium Chloride (CaCl2) desiccant, where the uncertainty did not exceed ±3.5%. The absorption effect in a deep container was studied experimentally and then amplified by pumping air into the solution. The latter measured water absorption/desorption by a thin solution layer under variable ambient conditions. Pumping air inside deep liquid desiccant containers increased the water absorption rate to 3.75% per hour, yet when using a thin layer of the solution, it was found to have increased to 6.5% per hour under the same conditions. The maximum amount of absorbed water and water vapor partial pressure relation was investigated, and the mean absolute error between the proposed formula and measured water content was 6.9%. An empirical formula, a one-dimensional mathematical model, was then developed by coupling three differential equations and compared to experimental data. The mean absolute error of the model was found to be 3.13% and 7.32% for absorption and desorption, respectively. Governing mathematical conservation equations were subsequently formulated. The mathematical and empirical models were combined and solved numerically. Findings obtained from the simulation were compared to experimental data. Additionally, several scenarios were modeled and tested for Jeddah, Saudi Arabia, under various conditions.

Reservoir simulation is a time-consuming procedure that requires a deep understanding of complex fluid flow processes as well as the numerical solution of nonlinear partial differential equations. Machine learning algorithms have made significant progress in modeling flow problems in reservoir engineering. This study employs machine learning methods such as random forest, decision trees, gradient boosting regression, and artificial neural networks to forecast nanoparticle transport with the two-phase flow in porous media. Due to the shortage of data on nanoparticle transport in porous media, this work creates artificial datasets using a mathematical model. It predicts nanoparticle transport behavior using machine learning techniques, including gradient boosting regression, decision trees, random forests, and artificial neural networks. Utilizing the scikit-learn toolkit, strategies for data preprocessing, correlation, and feature importance are addressed. Furthermore, the GridSearchCV algorithm is used to optimize hyperparameter tuning. The mean absolute error, R-squared correlation, mean squared error, and root means square error are used to assess the models. The ANN model has the best performance in forecasting the transport of nanoparticles in porous media, according to the results.

Harvesting atmospheric water by solar regenerated desiccants is a promising water source that is energy-efficient, environmentally clean, and viable. However, the generated amounts of water are still insignificant. Therefore, more intensive fundamental research must be undertaken involving experiments and modeling. This paper describes several experiments, which were conducted to predict and improve the behavior of water absorption/desorption by the Calcium Chloride (CaCl2) desiccant, where the uncertainty did not exceed ±3.5%. The absorption effect in a deep container was studied experimentally and then amplified by pumping air into the solution. The latter measured water absorption/desorption by a thin solution layer under variable ambient conditions. Pumping air inside deep liquid desiccant containers increased the water absorption rate to 3.75% per hour, yet when using a thin layer of the solution, it was found to have increased to 6.5% per hour under the same conditions. The maximum amount of absorbed water and water vapor partial pressure relation was investigated, and the mean absolute error between the proposed formula and measured water content was 6.9%. An empirical formula, a one-dimensional mathematical model, was then developed by coupling three differential equations and compared to experimental data. The mean absolute error of the model was found to be 3.13% and 7.32% for absorption and desorption, respectively. Governing mathematical conservation equations were subsequently formulated. The mathematical and empirical models were combined and solved numerically. Findings obtained from the simulation were compared to experimental data. Additionally, several scenarios were modeled and tested for Jeddah, Saudi Arabia, under various conditions.

This paper presents a discussion on the sustainable development goals in Saudi Arabia objectives that will be executed by the year 2030 according to their vision 2030. There are 17 sustainable development goals set. These objectives will provide the citizen and the nation with the structure to be assigned to the activity and to make another general public appearing as a goal of development. This work has focus on the efforts being made by the Kingdom of SaudiArabia towards the implementation of the Sustainable Development Goals (SDGs), Goal #7 (Renewable Energy). This SGD is important as the old society move on to a new level of innovation and, at the same time, to develop SDGs that will help to have a workable effect that will motivate the shared activity of individuals, the planet and success. With the implementation of Goal 7 the renewable energy sector will enable the nation of Saudi Arabia to move towards sustainability and green energy consumption.

This paper examines how the GCC countries are dealing with COVID-19. This paper shows how this outbreak creates difficult conditions for the economy of the GCC countries. From February 2020, the number of confirmed cases continues to increase around the globe. This pandemic situation creates a very alarming situation for the economic cycle. GCC countries depend on hydrocarbon exports, and this outbreak not only reduces the epidemic but also reduces oil prices. In this situation, the GCC countries have a considerable challenge to maintain growth. As a matter of concern, this paper examines how the GDP of the GCC countries has decreased and how demands and supply shortages are occurring. The decrease in oil prices indicates that it takes time to cover these circumstances. Research also suggests that it seems difficult for oil prices to reach $45 per barrel. This pandemic is reducing travel, and this is the main cause of this shortfall. Thus, in this case, the GCC countries must manage the fiscal deficit with a fiscal stimulus. The government is already working on public financing and is providing a fiscal stimulus to the industry.

Nowadays, harvesting water from the atmosphere is becoming a new alternative for generating fresh water. To the author’s best knowledge, no mathematical model has been established to describe the process of harvesting water from the atmosphere using porous materials. This research seeks to develop a new mathematical model for water moisture absorption in porous materials to simulate and assess harvesting atmospheric water. The mathematical model consists of a set of governing partial differential equations, including mass conservation equation, momentum equation, associated parameterizations, and initial/boundary conditions. Moreover, the model represents a two-phase fluid flow that contains phase-change gas–liquid physics. A dataset has been collected from the literature containing five porous materials that have been experimentally used in water generation from the air. The five porous materials include copper chloride, copper sulfate, magnesium sulfate, manganese oxides, and crystallites of lithium bromide. A group of empirical models to relate the relative humidity and water content have been suggested and combined with the governing to close the mathematical system. The mathematical model has been solved numerically for different times, thicknesses, and other critical parameters. A comparison with experimental findings was made to demonstrate the validity of the simulation model. The results show that the proposed mathematical model precisely predicts the water content during the absorption process. In addition, the simulation results show that; during the absorption process, when the depth is smaller, the water content reaches a higher saturation point quickly and at a lower time, i.e., quick process. Finally, the highest average error of the harvesting atmospheric water model is around 1.9% compared to experimental data observed in manganese oxides

This paper introduces two more definitions of the conditional average entropy. Some properties of the three definitions are studied and some mistakes in the preceding literature are corrected.