KEYWORDS: Neural networks, Mathematical modeling, Process modeling, Neurons, Artificial neural networks, Systems modeling, Intelligence systems, Signal processing, Oxygen, Data processing
The control of biological wastewater treatment can still be considered an open problem in the literature due to the difficulty of meeting environmental standards. The main objective of the biological treatment of wastewater is the removal of non-sedimentable organic solids, the stabilization of organic matter in sludge and the reduction of nutrients based on nitrogen and phosphorus. Knowing the complex structure of the biological purification process, the success of the command and control operation requires the division of the phenomena that occur according to the main effects and their nature. In this paper, an artificial neural network is used to optimize the biological purification process. The chosen network is feedforward and was driven by the backpropagation method using the supervised learning mechanism. The neuronal optimizer works at a higher hierarchical level than automatic regulation.
Tracking the maximum power point (MPP) represents the essence of the variable speed control of wind turbines since it determines their efficiency. The MPP is achieved by always adapting the wind turbine’s speed to the wind speed. This paper presents a study of methods to improve the control of wind turbines. One of these methods is the minimization of the time interval required for the wind turbine to achieve maximum power. Minimizing the time it takes to bring the speed/VUM, ω, from GE to the optimum value, ωOPTIM, is made at the maximum value for GE current. The study is based on the wind speed measurements collected from a wind farm located in Dobrogea, Romania. In this paper we have studied the methods of optimizing the control of wind turbines with an emphasis on minimizing the time required to bring wind turbines in the optimal area, from an energy point of view knowing the wind speed and, based on this, the optimal mechanical angular velocity, charges the electric generator, (at the rated current value), if 𝜔⋗ 〖ω〗 _OPTIM, or discharges, (zero power), if 𝜔⋖ 〖ω〗 _OPTIM. This ensures maximum wind energy capture. The study is based on the wind speed measurements collected from a wind farm located in Dobrogea, Romania.
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