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Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for various membrane separation techniques and the integration of AI concepts such as ML and ANNs to simulate membrane fouling, water flux and micropollutant behaviour, aiming to enhance wastewater treatment and optimise treatment and desalination processes. In conclusion, this review summarised the applications of AI in predicting the behaviour of membranes as well as their strengths, weaknesses and future directions of AI in membranes for water purification and desalination processes. [ABSTRACT FROM AUTHOR]

The environmental implications of various municipal solid waste (MSW) management systems in Riyadh, Kingdom of Saudi Arabia, were analysed considering the Riyadh Green Initiative through a comprehensive Life Cycle Assessment (LCA). This study evaluates five distinct scenarios: unsanitary landfilling (Sc0), incineration (Sc1), and a combination of technologies such as anaerobic digestion (AD) and a material recovery facility (MRF) in scenarios Sc2–Sc4. These scenarios were assessed using GaBi 9.2.1 software, employing the impact methodology outlined by CML (2001) to evaluate eight impact categories, including Global Warming and Acidification Potentials, among others. The findings indicate that scenarios incorporating the treatment of 100% of recyclable and organic waste through AD exhibited substantial environmental benefits. Additionally, Sc2 demonstrated the lowest environmental burdens across all impact categories. In contrast, Sc0 ranked the worst in all categories due to the absence of gas and leachate treatment. The results were shown to be reliable when compared qualitatively to previous studies. Furthermore, a sensitivity analysis was conducted to assess this study's system boundaries and the impact of the MRF rate. Overall, this research provides valuable insights for optimising MSW management practices in Riyadh, aiming to reduce their environmental impacts and align with the goals of the Riyadh Green Initiative. [ABSTRACT FROM AUTHOR]

H2 is considered a practical substitute for fossil fuels, especially for transportation by road and air, created either from fossil fuels or through the process of electrolysis of water. Research questions were included based on numerous research and the analysis of articles. The cost analysis of H2 processes, techno-economic hurdles in commercialization, and the economic comparison of various H2-production methods were the basis for the study of papers. The current research examines the different methods of thermochemical, biological, and electrochemical processes utilized in converting biomass into hydrogen. The benefits, constraints, and significant enhancements of every procedure are outlined. The examination assesses the cost of production, the level of technology readiness, and the potential for scalability. Thermochemical techniques, such as gasification and steam reforming, are effective at producing hydrogen. Steam gasification is perfect for moist and dry biomass in the absence of an oxidizing agent. Dark fermentation is more efficient for biological conversion because it requires less energy. Moreover, the electrochemical procedure is viable for biomass. Thermochemical treatment is significantly more advanced than biological or electrochemical treatment when it comes to scaling opportunities based on comparisons of current processes. The results of this research show that biomass–hydrogen processes have the potential for increasing H2 production, but further enhancements are needed to produce larger quantities for competitiveness. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models' prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R2, values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes. [ABSTRACT FROM AUTHOR]