Neural networks for cost estimation of shell and tube heat exchangers

Abstract

Abstract-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.

An integrated artificial neural network algorithm for performance assessment and optimization of decision making units

“…They are made up of simple processing units which are linked by weighted connections to form structures that are able to learn relationships between sets of variables. Recently ANN is the most commonly used in different aspect of science (Behzad, Asghari, Eazi, & Palhang, 2009;Bildirici & Ersin, 2009;Chauhan, Ravi, & Chandra, 2009;Das, Turkoglu, & Sengur, 2009;Duran, Rodriguez, & Consalter, 2009;Gençoglu & Cebeci, 2009;Mostafa, 2009;Paliwal & Kumar, 2009;Selek, S ßahin, & Kahramanli, 2009;Sun, Liu, Tsai, & Hsieh, 2009;Wu & Chan, 2009;Yudong & Lenan, 2009).…”

Application of machine learning methods to cost estimation of product life cycle

“…An accurate cost estimation model for the entire life cycle of industrial products is therefore critical; this can facilitate good decision-making. The ability to offer price quotations quickly and to have an adjustable estimation model is also important in highly changeable competitive environments (Zhang and Fuh 1998, Wang 2007, Verlinden et al 2008.Although many studies (Bode 1997, Zhang and Fuh 1998, Wang 2007 indicate that costs in the early design and planning phases account for only a small proportion of the total cost, they affect approximately 80% of the total cost; therefore, these early phases have significant impact on the overall life cycle cost (Bode 1997, Zhang and Fuh 1998, Layer et al 2002, Duran et al 2009). …”

A new approach for product cost estimation using data envelopment analysis

“…For this illustration, we choose the back propagation ANN non-parametric cost estimation methods as ANN has been proven to be superior compared to parametric and other nonparametric methods (Cavalieri et al, 2004;Caputo and Pelagagge, 2008;Verlinden et al, 2008;Dura et al, 2009). The ANN used in this work has been detailed in the Appendix and was trained using the thirteen data points given in Table 1.…”

“…A case study of manufacturing cost estimation of machined components in an automotive industry using ANN is presented in Cavalieri et al (2004). Several papers report comparative studies between ANN and parametric regression methods (Cavalieri et al, 2004;Caputo & Pelagagge, 2008;Verlinden et al, 2008;Dura et al, 2009). These articles assert that ANNs show better cost estimation performance than regression analysis.…”