Share

IIR document

Optimal solar COP prediction of a solar-assisted adsorption refrigeration system working with activated carbon/methanol as working pairs using direct and inverse artificial neural network.

Author(s) : LAIDI M., HANINI S.

Type of article: Article, IJR article

Summary

The aim of this work is to develop an ANN model to predict the solar COP (COPs) of a solar intermittent refrigeration system for ice production working with Activated carbon (AC)/methanol pair. A feedforward (FFBP) with one hidden layer, a Levenberg–Marquardt learning (LM) algorithm, hyperbolic tangent sigmoid transfer function and linear transfer function for the hidden and output layer respectively, were used. The best fitting training data was obtained with the architecture of (8 inputs, 8 hidden and 1 output neurons), Results of the ANN showed an excellent agreement R2 > 0.9985 between simulated and those obtained from literature with maximum root mean square error and RMSE = 0.0453%. A sensitivity analysis was also conducted using the inverse artificial neural network method to study the effect of all the inputs on the COPs. Results from the ANNi showed a good agreement in the case of the mass of activated (error less than 0.08%).

Available documents

Format PDF

Pages: 247-257

Available

  • Public price

    20 €

  • Member price*

    Free

* Best rate depending on membership category (see the detailed benefits of individual and corporate memberships).

Details

  • Original title: Optimal solar COP prediction of a solar-assisted adsorption refrigeration system working with activated carbon/methanol as working pairs using direct and inverse artificial neural network.
  • Record ID : 30006490
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 36 - n. 1
  • Publication date: 2013/01

Links


See other articles in this issue (24)
See the source

Indexing