Prediction and Identification of Nonlinear Rotary Cement Kiln System with Neuro-Fuzzy ANFIS Network by Using Feature Selection with Genetic Algorithm

Due to the status of Rotary Kiln Cements (RKCs) in different industries and lack of a mature model for these systems, identification and prediction of the Kiln system are necessary for any simulation and automation approaches. Intrinsically, RKCs are non-linear and time variant systems. This paper proposes a novel approach of using ANSFI to predict the status of a RKC system in a scale of few minutes in advance. Since the data used in this research has been extracted from a real system, pre-analysis of data is one of the critical parts of identification process. In addition to the system inputs, dynamic of the system which has been selected according to the LIPSCHITZ method with a system’s genuine delay are applied as inputs for Neural Network system with one step phase lag. Genetic algorithm has been utilized as a characteristic selection and phasor rules reduction method due to the existing challenges on the number of rules in phasor systems specifically with a large number of variables to be applied to the Neural Network. To verify the performance of the proposed identification and prediction method on a non-linear industrial system, simulation results have been carried out on a real data extracted from SAVEH Cement Company.