Multifunctionalization of fiber-reinforced composites, especially by adding energy storage capabilities, is a promising approach to realize lightweight structural energy storages for future transport vehicles. Compared to conventional energy storage systems, energy density can be increased by reducing parasitic masses of non-energy-storing components and by benefitting from the composite meso- and microarchitectures. In this paper, the most relevant existing approaches towards multifunctional energy storages are reviewed and subdivided into five groups by distinguishing their degree of integration and their scale of multifunctionalization. By introducing a modified range equation for battery-powered electric aircrafts, possible range extensions enabled by multifunctionalization are estimated. Furthermore, general and aerospace specific potentials of multifunctional energy storages are discussed. Representing an intermediate degree of structural integration, experimental results for a multifunctional energy-storing glass fiber-reinforced composite based on the ceramic electrolyte Li_1.4Al_0.4Ti_1.6(PO₄)₃ are presented. Cyclic voltammetry tests are used to characterize the double-layer behavior combined with galvanostatic charge–discharge measurements for capacitance calculation. The capacitance is observed to be unchanged after 1500 charge–discharge cycles revealing a promising potential for future applications. Furthermore, the mechanical properties are assessed by means of four-point bending and tensile tests. Additionally, the influence of mechanical loads on the electrical properties is also investigated, demonstrating the storage stability of the composites.

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre‐ and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering.