Neural networks for cost estimation of shell and tube heat exchangers

Orlando Duran, Nibaldo Rodriguez, Luiz Airton Consalter

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


The objective of this paper is to develop and test a model of cost estimating for the shell and tube heat exchangers in the early design phase via the application of artificial neural networks (ANN). An ANN model can help the designers to make decisions at the early phases of the design process. With an ANN model, it is possible to obtain a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of a shell and tube heat exchangers.

Original languageEnglish
Pages (from-to)7435-7440
Number of pages6
JournalExpert Systems with Applications
Issue number4
StatePublished - May 2009


  • Cost estimation
  • Heat exchangers
  • Neural networks
  • Shell
  • Tube


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