Consumption of water is never constant throughout the day due to the daily routines of the consumer. This pattern of daily water consumption is called water demand profile. The initiative to create these profiles are to improve hydraulic performance and to build energy conservative strategies for designed networks in Dubai. Therefore, the aim is to develop and analyze a domestic consumption profile for selected developments with socio-demographic factors including weekday/weekend variation, population, income, fasting during the month of Ramadan, and the outbreak of Covid-19. Data from more than 7000 smart meters were collected while water meters of more than 350 residential flats were examined manually. Water demand profiles generated from the data showed weekdays have more predictable peaks (morning 6–8 am and evening 5–7 pm) than weekends. During Ramadan, peak hours shifted to 7–10 am followed by 3–4 pm during workdays while peaks for low income areas are higher due to stricter working routine. The Covid-19 crisis has led to significant rise in observed consumption, with over 30% increase during the month of Ramadan. The observed results, if compared with further end-use studies on more factors affecting demand profiles, can help in generating both cost and energy efficient networks.

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The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages.

The development of technology has made it easier for engineers to design and test models that allow simulation of real-time water distribution networks with greater accuracy. However, with so many nodes and links in a network, building a model still requires some simplification. One such simplification is the ‘conservative approach’, which applies the principle of ‘lumped demand’, taking the demand only from nodes at the ends of pipes. Herein, the full effect of lumped demand on key water parameters is analysed, on a large-scale network based on as-built networks of Al Furjan and Dubai Silicon Oasis, Dubai, UAE for different conditions. Epanet and WDNetXL software are used for the analysis, and results show the impact of different levels of skeletonisation on the head and velocity values for the two models. The analysis indicates that the head changes are high for a branched network under the extreme condition of firefighting. It includes the effect of skeletonising local tanks, with changes being higher when all tanks are empty. These findings provide a critical evaluation of the performance of this method for the Middle East region and it is concluded that the considerable velocity changes observed in the models could lead to overdesign.

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