Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System

Cement rotary kiln is the main part of the cement production process, which has always attracted many researchers’ attentions. However, this complex non-linear system has not been modeled efficiently, which can make an appropriate performance especially in the noisy condition. In this work, the Takagi-Sugeno neuro-fuzzy system (TSNFS) is used for identification of the cement rotary kiln, and the gradient descent (GD) algorithm is applied for tuning the parameters of antecedent and the consequent parts of fuzzy rules. In addition, the optimal inputs of the system are selected by genetic algorithm (GA) to achieve less complexity in the fuzzy system. The data related to the Saveh White Cement factory is used in the simulations. The Results obtained demonstrate that the proposed identifier has a better performance in comparison with the neural and fuzzy models presented earlier for the same data. Furthermore, in this work, TSNFS is evaluated in noisy condition, which had not been worked out before in related research works. The simulations show that this model has a proper performance in different noisy conditions.