Optimization of operating conditions for steam turbine using an artificial neural network inverse Y.El. Hamzaoui a , J.A. Rodríguez b, * , J.A. Hern  andez b , Victor Salazar b a Instituto de Ingeniería y Tecnología, Dpto. Ingeniería El ectrica y Computaci on, CU, Universidad Aut onoma de Ciudad Ju arez (UACJ), Av. Del Charro # 450 Norte, CP 32310, AP 1594-D Ciudad Ju arez, Chihuahua, Mexico b Centro de Investigaci on en Ingeniería y Ciencias Aplicadas (CIICAp-UAEM), Av. Universidad #1001, Col Chamilpa, CP 62209 Cuernavaca, Morelos, Mexico highlights  The failure assessment in blades is optimized using artificial neural network inverse (ANNi).  (ANNi) is a very effective modeling the useful life in blades of steam turbines.  Failure assessment in blades is optimized using artificial neural network inverse. article info Article history: Received 30 June 2014 Accepted 23 September 2014 Available online 2 October 2014 Keywords: Inverse neural network Optimal parameters Optimization Steam turbine failure Life cycle assessment in blades abstract The useful life (UL) of the failure assessment in blades of steam turbines is optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with a Nelder Mead optimization method to estimate the resonance stress when the UL of the blades is required. The proposed method ANNi is a new tool which inverts the artificial neural network (ANN). Firstly, It is necessary to build the artificial neural network (ANN) that simulates the output parameter (UL). ANN's model is constituted of feedforward network with one hidden layer to calculate the output of the process when input parameters are well known, then inverting ANN. The ANNi could be used as a tool to estimate the optimal unknown parameter required (resonance stress). Very low percentage of error and short computing time are precise and efficient, make this methodology (ANNi) attractive to be applied for control on line the UL of the system and constitutes a very promising framework for finding set of “good solutions”. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction Steam turbines have many applications in various industrial sectors. However, by common experience blade failures are the main origin of operational breakdowns in these machines, causing great economic lost in turbo machinery industry. The turbines are designed to work in stable operation condition [1e4]. Nevertheless, failure in blades has been present after a short time period of work. These failures commonly attributed to resonance stress of the blades at different stages to certain excitation frequencies. The expense of downtime and repair is about the millions of dollars [5]. The useful life (UL) is a very important variable for determining the performance of steam turbines [6]. Therefore, the critical compo- nents which determine the useful life of the turbine should be evaluated to determine the rehabilitation or replacement of them. The critical components are the blades of steam turbines [7]. Most of the existing analytical models used to predict the useful life of the failure assessment in blades of steam turbines are based on analysis using analysis of vibrations for the construction of the di- agram of Campbell, which shows the natural frequencies of the blades like a function of the speed of the rotor (RPM) [8]. These models do not provide reliable predictions for useful life (UL). This is caused by the complexity of solving the equations that involve the radiant energy balance, the spatial distribution of the absorbed radiation, mass transfer, and the mechanisms of steam turbines [5]. Moreover, in the light of the rapid development witnessed by the modern world in different fields of knowledge, science and technology, due to the increased speed of complexity of the system, in response to the issues requiring urgent attention of the people, in * Corresponding author. Tel.: þ52 7772677638. E-mail address: jarr@uaem.mx (J.A. Rodríguez). Contents lists available at ScienceDirect Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng http://dx.doi.org/10.1016/j.applthermaleng.2014.09.065 1359-4311/© 2014 Elsevier Ltd. All rights reserved. Applied Thermal Engineering 75 (2015) 648e657