Thermal Efficiency Supervision by NN Based Functional Approximation Techniques
Thermal efficiency monitoring allows us evaluating the performance of thermal engines which operates under the Rankine cycle. In this research work, massive application of backpropagation neural networks (BPNN) is used with the aim of evaluating the thermal efficiency of processes operating under the Rankine cycle with various working fluids. Knowing the thermal efficiency behavior allows us estimating the best working fluid as well as the optimal operating temperatures for which thermal efficiency is maximized. Achieving mentioned objectives requires a critic modeling task in which massive application of BPNNs are applied. The required information to train the BPNNs is achieved from the NIST database. With such monitoring method, the way to improving the efficiency results a simple reliable task.
KeywordsBackpropagation feedforward NNs Conjugate gradient Functional approximation Organic Rankine cycle Thermal efficiency
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- 1.Norgard, M., Ravn, O., Poulsen, N., Hansen, L.: Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)Google Scholar
- 8.NIST.: Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) Version 8.0, U.S. Department of Commerce, Maryland (2007)Google Scholar