Green hydrogen has become a central topic in discussions about the global energy transition, seen as a promising solution for decarbonizing economies and meeting climate goals. As part of the process of decarbonization, green hydrogen can replace fossil fuels currently in use, helping to reduce emissions in sectors vital to the global economy, such as industry and transport, as well as in the power and heat sectors. Whilst there is significant potential for green hydrogen, there are also challenges. The upfront costs for infrastructure and technology are high, and the availability and accessibility of the renewables needed for production varies by region. Green hydrogen production and storage technologies are continuously evolving and being promoted as the demand for hydrogen in many applications grows. Considering this, this paper presents the main methods for its production and storage, as well as its economic impact. Hence, the trend of governments and international organizations is to invest in research and development to make this technology more accessible and efficient, given the carbon reduction targets.

In this paper a discrete-time sliding mode control for the trajectory tracking problem of wheeled mobile robots is presented. The wheeled mobile robot (WMR) taken into account was PowerBot. PowerBot is a mobile platform with two differential driving wheels (2DW) and two balancing caster wheels. It is an automated guided vehicle specially designed and equipped for autonomous, intelligent delivery and handling of large payloads. PowerBot is a member of MobileRobots' Pioneer family of mobile robots, which are research and development platforms that share a common architecture, foundation software and employ intelligence-based client-server robotics controls. Due to its size and high load carrying capacity PowerBot is an ideal robot for both, indoor and outdoor transportation. The algorithm has been designed in discrete-time domain in order to avoid problems caused by the discretization of continuous-time controllers. The simulation results and real time results prove the effectiveness of the proposed controller.

Oceanic energy sources, notably offshore wind and wave power, present a significant opportunity to generate green hydrogen through water electrolysis. This approach allows for offshore hydrogen production, which can be efficiently transported through existing pipelines and stored in various forms, offering a versatile solution to tackle the intermittency of renewable energy sources and potentially revolutionize the entire electrical grid infrastructure. This research focusses on assessing the technical and economic feasibility of this method in six strategic coastal regions in Morocco: Laayoune, Agadir, Essaouira, Eljadida, Casablanca and Larache. Our proposed system integrates offshore wind turbines, oscillating water column wave energy converters, and PEM electrolyzers, to meet energy demands while aligning with global sustainability objectives. Significant electricity production estimates are observed across these regions, ranging from 14 MW to 20 MW. Additionally, encouraging annual estimates of hydrogen production, varying between 20 and 40 tonnes for specific locations, showcase the potential of this approach. The system’s performance demonstrates promising efficiency rates, ranging from 13% to 18%, while maintaining competitive production costs. These findings underscore the ability of oceanic energy-driven green hydrogen to diversify Morocco’s energy portfolio, bolster water resilience, and foster sustainable development. Ultimately, this research lays the groundwork for comprehensive energy policies and substantial infrastructure investments, positioning Morocco on a trajectory towards a decarbonized future powered by innovative and clean technologies.

The ultraviolet water disinfection process has numerous advantages in comparison with other water treatments. This study deals with this process’s optimal control and presents all its stages, from problem statement to analysis of results. We consider a typical disinfection water system and data presented in the literature. In this case, the control inputs are the water flow rate and the UV light intensity. A nonlinear dynamic model for the UV disinfection system is constructed and generates a method to calculate the bacterial reduction. An optimal control problem (OCP) is stated using the dynamic model and two optimal criteria: the disinfection and energy criterion. The optimal solutions are yielded by a metaheuristic algorithm based on hybrid topology particle swarm optimization. The algorithm determines the optimal flow rate and light intensity for an imposed bacterial reduction. The implemented program was carried out for a wide range of input values for comprehensive analysis. The analysis of the results establishes when an optimal solution exists and the extent to which the disinfection or energy criterion is fulfilled. The method to calculate the bacterial reduction, OCP statement, optimization algorithm, and results’ analysis may be considered findings of this work.

The idea for this work starts from the situation in which a metaheuristic-based algorithm has already been developed in order to solve an optimal control problem. This algorithm yields an offline

A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dynamic model are very well known and have been validated in previous papers. The control solution is a closed-loop structure whose controller generates predicted control sequences. An efficient way to make optimal predictions is to use a metaheuristic algorithm, the particle swarm optimization algorithm. Even if this metaheuristic is efficient in treating predictions with a very large prediction horizon, the main objective of this paper is to find a tool to reduce the controller’s computational complexity. We propose a soft sensor that gives information used to reduce the interval where the control input’s values are placed in each sampling period. The sensor is based on measurement of the biomass concentration and numerical integration of the process model. The returned information concerns the specific growth rate of microalgae and the biomass yield on light energy. Algorithms, which can be used in real-time implementation, are proposed for all modules involved in the simulation series. Details concerning the implementation of the closed loop, controller, and soft sensor are presented. The simulation results prove that the soft sensor leads to a significant decrease in computational complexity.