The aim of this study is to demonstrate the comparison of an artificial neural network (ANN) and an adaptive neuro fuzzy inference system (ANFIS) for the prediction of the coefficient of performance (COP) for a water purification process integrated in an absorption heat transformer system with energy recycling. ANN and ANFIS models take into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two presures and LiBr+H2O concentrations. Experimental results are performed to verify the results from the ANN and ANFIS approaches. For the network, a feedforward with one hidden layer, a Levenberg-Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validaton data set, simulations and experimental data test were in good agreement (R2>0.9980). However, the ANFIS model was developed using the same input variables. The statistical values are given in as tables. However, comparaison between two models shows that ANN provides better results than the ANFIS results. Finally this paper shows the appropriateness of ANN and ANFIS for the quantitative modeling with reasonable accuracy.
Modernization of technological equipment in the waste water purification process behind the coke oven using the organic Rankine cycle
