Optimization of operating conditions for steam turbine using an artificial neural network inverse. (22nd January 2015)

• Record Type: Journal Article
• Authors: Hamzaoui, Y.El.; Rodríguez, J.A.; Hernández, J.A.; Salazar, Victor
• Is Part Of: Applied thermal engineering. Volume 75(2015:Jan.)
• Page Start: 648
• Page End: 657
• Publication Date: 2015-01-22
• Subjects: Inverse neural network -- Optimal parameters -- Optimization -- Steam turbine failure -- Life cycle assessment in blades; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2014.09.065
• Languages: English
• ISSNs: 1359-4311

Abstract

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". 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.