Modeling of Solar Field in Direct Steam Generation Parabolic Trough Based on Heat Transfer Mechanism and Artificial Neural Network

Abstract

Accurate calculation of water/steam temperature and pressure in the solar field of direct steam generation (DSG) parabolic trough is essential to power dispatch and control. However, it is very difficult to achieve satisfied accuracy in limited time because of the existence of two-phase flow that has complex dynamic characteristics. In this work, a new hybrid model is proposed, which is based on the heat transfer mechanism enhanced by artificial neural network. This hybrid model has two characteristics as follows. (1) A heat-transfer and hydrodynamic coupling steady-state mechanism model as a prior model, which is used to quickly obtain the prior values including mechanism factors that are then used as input to an artificial neural network (ANN) model. (2) An ANN model consisting of two four-hidden-layer back propagation (BP) networks. The inlet pressure and outlet temperature of solar field are modelled in the first four-layer neural network, and then the outlet temperature of solar field is analyzed in the second four-layer neural network to improve the simulation accuracy. This model is verified by the data from DISS plant on 26 June 2001, and can be used for control studies and dispatching optimization. The calculation results show that the mean absolute percentage error (MAPE) of predicted inlet pressure and outlet temperature are 0.48% and 1.14%, respectively, which are better than the results of BP model, general regression neural network (GRNN) model, hybrid model with one BP network mentioned in this paper, and the dynamic simulation model mentioned in the literature.

Keywords

• Direct steam generation
• parabolic trough
• heat transfer mechanism
• deep neural network
• combining prediction model
• working fluid parameters