Modeling heat transfer properties in an ORC direct contact evaporator using RBF neural network combined with EMD

Without an intervening wall, the direct contact evaporator (DCE) has been already technically proven to improve the overall thermal efficiency of organic Rankine cycle (ORC) used to recover low-grade heat sources and transform them into power. In the estimation of volumetric heat transfer coefficient (VHTC) which is assumed to vary with flow rate, noises signals caused by various unstable factors (e.g., measurement errors) often corrupt the time series of VHTC. For forecasting the heat transfer performance of DCE in ORC more accurately, this paper proposes a novel approach (refers as EMD-RBF-NN), which combines multi-input radial basis function (RBF) neural network (NN) and empirical mode decomposition (EMD) method. Specifically, the original VHTC time series is firstly decomposed by EMD method that is fully data-driven. Then, the proposed method models the resultant decomposition series with flow rates of two fluids (dispersed and continuous phases) and VHTC by using RBF neural network. This simple technique was illustrated by using the ORC direct contact evaporator (ORC-DCE) and data processing system. Via using the experimental datasets of ORC-DCE, this paper demonstrates that the proposed EMD-RBF-NN model that associates flow rates of two phases with VHTC improves the forecasting accuracy of VHTC noticeably comparing with existing models.