Shchegolkov Alexander V, Shchegolkov Aleksei V, Zemtsova Natalia V, Stanishevskiy Yaroslav M, Vetcher Alexandre A
Institute of Technology of the Department of Technology and Methods of Nanoproducts Manufacturing, Tambov State Technical University, 392000 Tambov, Russia.
Department of Chemical Technology, Platov South-Russian State Polytechnic University (NPI), 132 Enlightenment Str., 346428 Novocherkassk, Rostov Region, Russia.
Polymers (Basel). 2022 Nov 18;14(22):4992. doi: 10.3390/polym14224992.
The turn to hydrogen as an energy source is a fundamentally important task facing the global energetics, aviation and automotive industries. This step would reduce the negative man-made impact on the environment on the one hand, and provide previously inaccessible power modes and increased resources for technical systems, predetermining the development of an absolutely new life cycle for important areas of technology, on the other. The most important aspect in this case is the development of next-generation technologies for hydrogen industry waste management that will definitely reduce the negative impact of technology on the environment. We consider the approaches and methods related to new technologies in the area of hydrogen storage (HS), which requires the use of specialized equipment equipped with efficient and controlled temperature control systems, as well as the involvement of innovative materials that allow HS in solid form. Technologies for controlling hydrogen production and storage systems are of great importance, and can be implemented using neural networks, making it possible to significantly improve all technological stages according to the criteria of energy efficiency reliability, safety, and eco-friendliness. The recent advantages in these directions are also reviewed.
转向氢能作为能源是全球能源、航空和汽车行业面临的一项根本性重要任务。这一步骤一方面将减少人为对环境的负面影响,另一方面为技术系统提供以前无法获得的动力模式和更多资源,从而为重要技术领域预先确定一个全新的生命周期发展方向。在这种情况下,最重要的方面是开发用于氢能产业废物管理的下一代技术,这肯定会减少技术对环境的负面影响。我们考虑与储氢(HS)领域新技术相关的方法,这需要使用配备高效且可控温度控制系统的专用设备,以及使用能实现固态储氢的创新材料。控制制氢和储氢系统的技术非常重要,可以通过神经网络来实现,从而能够根据能源效率、可靠性、安全性和生态友好性标准显著改善所有技术阶段。本文还综述了这些方向上的最新进展。