Chaotic characteristics analysis of the sintering process system with unknown dynamic functions based on phase space reconstruction and chaotic invariables

Abstract

Because the sintering process system (SPS) of the rotary kiln is influenced by many uncertain factors, such as the composition of raw materials, the structure of the kiln crust, and the mode of operation, it displays complex nonlinear dynamic characteristics. In addition, its dynamic functions are unknown. Thus, data-based chaotic analysis methods are used to study the dynamic characteristics of the SPS. First, three sintering temperature (ST) data with different timescales are extracted, respectively, from the three SPSs. Second, the reasons why the dynamic characteristics of the SPS can be analyzed by the ST based on the phase space reconstruction (PSR) method are explained, and several potential PSR forms (reconstructed systems) are achieved. Third, the three optimal PSR forms of the three ST data (SPSs) are determined by chaotic invariables-based quantitative analysis. We determined that the three SPSs are all five-order chaotic systems. Finally, the optimal PSR forms are further verified by predicting the practical ST. The highly accurate predictions show that not only are the three optimal PSR forms applicable but also the chaotic methodologies are effective. Thus, the results in this paper not only provide extensive theoretical research values for subsequent studies of the SPS but also provide extensive industrial application value for other industries such as the nonferrous metallurgy, cement, steel, and chemical industries, in which the SPS is applied. Specifically, the PSR-based and chaotic invariables-based chaotic analysis methods can be used to analyze other industrial systems with unknown dynamic functions.