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Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.
Air- and water-stable phenyl complexes with nitridotechnetium(V) cores can be prepared by straightforward procedures. [TcNPh2(PPh3)2] is formed by the reaction of [TcNCl2(PPh3)2] with PhLi. The analogous N-heterocyclic carbene (NHC) compound [TcNPh2(HLPh)2], where HLPh is 1,3,4-triphenyl-1,2,4-triazol-5-ylidene, is available from (NBu4)[TcNCl4] and HLPh or its methoxo-protected form. The latter compound allows the comparison of different Tc–C bonds within one compound. Surprisingly, the Tc chemistry with such NHCs does not resemble that of corresponding Re complexes, where CH activation and orthometalation dominate.
The understanding that optimized components do not automatically lead to energy-efficient systems sets the attention from the single component on the entire technical system. At TU Darmstadt, a new field of research named Technical Operations Research (TOR) has its origin. It combines mathematical and technical know-how for the optimal design of technical systems. We illustrate our optimization approach in a case study for the design of a ventilation system with the ambition to minimize the energy consumption for a temporal distribution of diverse load demands. By combining scaling laws with our optimization methods we find the optimal combination of fans and show the advantage of the use of multiple fans.
Water distribution systems are an essential supply infrastructure for cities. Given that climatic and demographic influences will pose further challenges for these infrastructures in the future, the resilience of water supply systems, i.e. their ability to withstand and recover from disruptions, has recently become a subject of research. To assess the resilience of a WDS, different graph-theoretical approaches exist. Next to general metrics characterizing the network topology, also hydraulic and technical restrictions have to be taken into account. In this work, the resilience of an exemplary water distribution network of a major German city is assessed, and a Mixed-Integer Program is presented which allows to assess the impact of capacity adaptations on its resilience.
Assessment of RF Safety of Transmit Coils at 7 Tesla by Experimental and Numerical Procedures (490.)
(2012)
The Monte Carlo code FLUKA is used to simulate the production of a number of positron emitting radionuclides, ¹⁸F, ¹³N, ⁹⁴Tc, ⁴⁴Sc, ⁶⁸Ga, ⁸⁶Y, ⁸⁹Zr, ⁵²Mn, ⁶¹Cu and ⁵⁵Co, on a small medical cyclotron with a proton beam energy of 13 MeV. Experimental data collected at the TR13 cyclotron at TRIUMF agree within a factor of 0.6 ± 0.4 with the directly simulated data, except for the production of ⁵⁵Co, where the simulation underestimates the experiment by a factor of 3.4 ± 0.4. The experimental data also agree within a factor of 0.8 ± 0.6 with the convolution of simulated proton fluence and cross sections from literature. Overall, this confirms the applicability of FLUKA to simulate radionuclide production at 13 MeV proton beam energy.
In this study, an online multi-sensing platform was engineered to simultaneously evaluate various process parameters of food package sterilization using gaseous hydrogen peroxide (H₂O₂). The platform enabled the validation of critical aseptic parameters. In parallel, one series of microbiological count reduction tests was performed using highly resistant spores of B. atrophaeus DSM 675 to act as the reference method for sterility validation. By means of the multi-sensing platform together with microbiological tests, we examined sterilization process parameters to define the most effective conditions with regards to the highest spore kill rate necessary for aseptic packaging. As these parameters are mutually associated, a correlation between different factors was elaborated. The resulting correlation indicated the need for specific conditions regarding the applied H₂O₂ gas temperature, the gas flow and concentration, the relative humidity and the exposure time. Finally, the novel multi-sensing platform together with the mobile electronic readout setup allowed for the online and on-site monitoring of the sterilization process, selecting the best conditions for sterility and, at the same time, reducing the use of the time-consuming and costly microbiological tests that are currently used in the food package industry.