Energetic thermo-physical analysis of MLP-RBF feed-forward neural network compared with RLS Fuzzy to predict CuO/liquid paraffin mixture properties

Abstract

Dynamic viscosity of novel generated Copper Oxide (CuO)/Liquid Paraffin nanofluids is obtained experimentally for various temperatures and concentrations. To optimize the empirical process and for cost-efficiency, Feed-Forward Neural Networks (FFNNs) were modeled and compared with Recursive Least Squares (RLS) Fuzzy model. To prepare CuO/ liquid paraffin nanofluids, CuO nanoparticles are dispersed within paraffin. Then an input-target dataset containing 30 input-target pairs is available for [Formula: see text], and [Formula: see text]. Based on the empirical results, two types of FFNNs are examined and compared with RLSF model to predict CuO/liquid paraffin nanofluids. To evaluate the best optimization methods of nanofluid viscosity, Multi-Layer Feed forward (MLF), Radial Basis Function (RBF), and RLSF are compared and discussed. The MLF network provides a global approximation while the RBF acts more locally, further, RLSF provides a better fit. On the contrary, the RBF network has better properties from the generalization and noise rejection points of view. Also, RBF networks can be applied in an online manner. Further, three curves of RLS Fuzzy model by Parabola2D, ExtremeCum, and Poly2D models were fitted on the empirical data and compared. The ExtremeCum model showed the least margin of error and can be employed to predict the data.

Keywords

• Viscosity
• copper oxide
• liquid paraffin
• artificial intelligence
• RLS Fuzzy
• machine learning