Simulating the mechanical behavior of a rotary cement kiln using artificial neural networks

We present a new approach to the fast determination of structural deformations and stresses in the refractory-reinforced body of a cement rotary kiln. The proposed approach builds on a comprehensive neuro-finite element simulation of the kiln shell. Three-dimensional stresses and deformations in the rotating tubular shell are first determined for a finite number of input vectors using a validated finite element model of the kiln. The resulting data are then used to train a Multi-Layer Perceptron (MLP) Neural Network which would predict – accurately enough – values of stresses and deformations throughout the kiln body for any given input vector. The resulting neural simulator would serve as a replacement for the computationally expensive cost-function evaluators that are traditionally used in numerical optimization algorithms. To demonstrate the applicability of the proposed approach, we analyze a typical rotary kiln using the Neuro-FE method and compare the results with those obtained from traditional method.