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The use of hydrogen as an alternative to fossil fuels for vehicle propulsion is already a reality. However, due to its physical characteristics, storage is still a challenge. There is an innovative way, presented in this study, to store hydrogen in conventional vehicles propelled by spark-ignition reciprocating engines and fuel cells, using hydrogen as fuel; the storage of hydrogen will be at high pressure within small spheres randomly packed in a tank, like the conventional tank of fuel used nowadays in current vehicles. Therefore, the main purpose of the present study is to assess the performance of this storage system and compare it to others already applied by car manufacturers in their cars. In order to evaluate the performance of this storage system, some parameters were taken into account: The energy stored by volume and stored by weight, hydrogen leakage, and compliance with current standards. This system is safer than conventional storage systems since hydrogen is stored inside small spheres containing small amounts of hydrogen. Besides, its gravimetric energy density (GED) is threefold and the volumetric energy density (VED) is about half when compared with homologous values for conventional systems, and both exceed the targets set by the U.S. Department of Energy. Regarding the leakage of hydrogen, it complies with the European Standards, provided a suitable choice of materials and dimensions is made.

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The rise of new digital technologies and their applications in several areas pushes the process industry to update its methodologies with more intensive use of mathematical models—commonly denoted as digital twins—and artificial intelligence (AI) approaches to continuously enhance operational efficiency. In this context, Real-time Optimization (RTO) is a strategy that is able to maximize an economic function while respecting the existing constraints, which enables keeping the operation at its optimum point even though the plant is subjected to nonlinear behavior and frequent disturbances. However, the investment related to the project of commercial RTOs may make its application infeasible for small-scale facilities. In this work, an in-house, small-scale RTO is presented and its successful application in a real industrial case—a Natural Gas Processing Unit—is shown. Besides that, a new method for enhancing the efficiency of using sequential-modular simulator inside an optimization framework and a new method to account for the economic return of optimization-based tools are proposed and described. The application of RTO in the industrial case showed an enhancement in the stability of the main variables and an increase in profit of 0.64% when compared to the operation of the regulatory control layer alone.