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Article Citation - WoS: 33Citation - Scopus: 56Smart irrigation system for environmental sustainability in Africa: An Internet of Everything (IoE) approach(Amer inst Mathematical Sciences-aims, 2019) Adenugba, Favour; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Kazanavicius, EgidijusWater and food are two of the most important commodities in the world, which makes agriculture crucial to mankind as it utilizes water (irrigation) to provide us with food. Climate change and a rapid increase in population have put a lot of pressure on agriculture which has a snowball effect on the earth's water resource, which has been proven to be crucial for sustainable development. The need to do away with fossil fuel in powering irrigation systems cannot be over emphasized due to climate change. Smart Irrigation systems powered by renewable energy sources (RES) have been proven to substantially improve crop yield and the profitability of agriculture. Here we show how the control and monitoring of a solar powered smart irrigation system can be achieved using sensors and environmental data from an Internet of Everything (IoE). The collected data is used to predict environment conditions using the Radial Basis Function Network (RBFN). The predicted values of water level, weather forecast, humidity, temperature and irrigation data are used to control the irrigation system. A web platform was developed for monitoring and controlling the system remotely.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).
