Flow distribution estimation in parabolic-trough collector solar plants
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12053Keywords:
Estimation, Fault detection, Quality assurance and maintenance, Solar energy, AI methods for FDI, Neural networksAbstract
This work presents a methodology to estimate the flow distribution in parabolic-trough collector solar plants combining optimization techniques with recurrent neural networks to reduce their high computational cost. First, an algorithm estimates the loop temperatures and obtains the flow rates that minimize the estimation error. Next, neural networks are trained to reproduce the algorithm. The obtained flow rates are used as initial points to the optimization problem. This way, the search space is limited and the computation time is significantly reduced. The method is evaluated in sectors of 4, 20, and 50 loops, and three versions are compared: only optimization, optimization+neural network and only neural network. The results prove that the flow rate estimation improves with respect to assuming a uniform distribution. Moreover, the neural networks reduce significantly the computation time, which is specially relevant in large-scale sectors.
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Copyright (c) 2025 Sara Ruiz-Moreno, Antonio J. Gallego, Eduardo F. Camacho
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