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Article Citation - Scopus: 1Thermodynamic Modeling and Multi-Objective Optimization of a New System Presented for Reutilization of the Lost Heat in Combined-Cycle Power Plants(John Wiley and Sons Inc, 2023) Peng,W.; Karimi Sadaghiani,O.In combined-cycle power plants, a large amount of thermal energy is lost when the boiler and steam unit are out of order and the gas unit is operated in single mode. For the first time, this work suggests every combined-cycle power plants should be equipped with this kind of energy system to recover the waste heat by producing hydrogen and generating electricity. This system combines a Rankine cycle with a thermoelectric generator, a finned-tube heat exchanger, and a proton exchange membrane to produce hydrogen. Having been designed, the suggested energy system is assessed by energy, exergy, and exergo-economy laws. Furthermore, the impacts of some effective factors on the efficiency and the costs are precisely analyzed. Eventually, the presented system is optimized considering two main purposes of exergy efficiency and costs. The achieved results show that the proposed system can effectively link to the gas unit to restore and even save the lost thermal energy in the single-mode condition. The conducted optimization attenuates the objective parameter of exergy efficiency from 48.39% to 41.65% and diminishes the costs from 550.14 to 480.82 $ GJ−1. Eventually, the optimization causes (Formula presented.) to rise from 1.2 to 1.32 kg h−1. © 2023 The Authors. Energy Technology published by Wiley-VCH GmbH.Article Citation - WoS: 17Citation - Scopus: 23Regarding Solid Oxide Fuel Cells Simulation Through Artificial Intelligence: a Neural Networks Application(Mdpi, 2019) Baldinelli, Arianna; Barelli, Linda; Bidini, Gianni; Bonucci, Fabio; Iskenderoglu, Feride CansuBecause of their fuel flexibility, Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition. Yet, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable performance simulation tools are required. Given the high variability ascribed to the fuel in the wide range of SOFC applications and the high non-linearity of electrochemical systems, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is sound. In this paper, several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8YSZ/LSCF anode supported planar button cells. The best simulator obtained is a 3-hidden layer ANN (25/22/18 neurons per layer, hyperbolic tangent sigmoid as transfer function, obtained with a gradient descent with adaptive learning rate backpropagation). This shows high accuracy (RMS = 0.67% in the testing phase) and successful application in the forecast of SOFC polarization behaviour in two additional experiments (RMS in the order of 3% is scored, yet it is reduced to about 2% if only the typical operating current density range of real application is considered, from 300 to 500 mA.cm(-2)). Therefore, the neural tool is suitable for system simulation codes/software whether SOFC operating parameters agree with the input ranges (anode feeding composition 0-48%(vol) H-2, 0-38%(vol) CO, 0-45%(vol) CH4, 9-32%(vol) CO2, 0-54%(vol) N-2, specific equivalent hydrogen flow-rate per unit cell active area 10.8-23.6 mL.min(-1).cm(-2), current density 0-1300 mA.cm(-2) and temperature 700-800 degrees C).
