Neural Network Inverse Modeling for Optimization

Abstract

In this chapter, artificial neural networks (ANNs) inverse model is applied for estimating the thermal performance () in parabolic trough concentrator (PTC). A recurrent neural network architecture is trained using the Kalman Filter learning from experimental database obtained from PTCs operations. Rim angle (φ r), inlet (T in), outlet (T out) fluid temperatures, ambient temperature (T a), water flow (F w), direct solar radiation (Gb) and the wind velocity (V w) were used as main input variables within the neural network model in order to estimate the thermal performance with an excellent agreement (R 2 =0.999) between the experimental and simulated values. The optimal operation conditions of parabolic trough concentrator are established using artificial neural network inverse modeling. The results, using experimental data, showed that the recurrent neural network (RNN) is an excellent tool for modeling and optimization of PTCs.