Abstract
The stable control of the calciner plant is vital for the clinker quality and energy consumption in the cement calcination process. However, traditional calciner control strategies cannot efficiently deal with complicated characteristics, such as the changes in the material component, process disturbances and uncertainty, and meanwhile cannot obtain knowledge from data. For these challenges, an intelligent control strategy based on an interval type-2 fuzzy logic controller (IT2FLC) is proposed by making full use of process data in this work. The feedback IT2FLC combines with feedforward IT2FLC, taking into account various disturbances. An improved interval type-2 fuzzy C-means (IT2FCM) clustering algorithm is used to extract membership functions and rules, taking into account process uncertainty. Finally, the proposed control strategy is applied to control a calciner simulation process with practical data. The results show that the proposed strategy has better performance than the type-1 fuzzy logic controller (T1FLC) and can better meet the demand for real-life applications.
Highlights
A calciner is a complicated system that is employed in the cement calcination process
For the cement calcination process, this study proposes an intelligent control structure based on interval type-2 fuzzy logic controller (IT2FLC) that is able to deal with process uncertainty, disturbances, and achieve precise control
The different gain parameters of lower membership functions (LMFs) are listed in Table.4, and the results show that parameter k = 0.9 has better performance with the minimum error
Summary
A calciner is a complicated system that is employed in the cement calcination process. The stable control of the calciner process has proved to be a challenging task due to the complexity of the process characteristics, uncertainties in coal kinetics, and inherent process uncertainties. For this challenging task, the traditional strategy based on a single controller, such as a proportional-integral-derivative (PID) controller, cannot cope with the complicated process control, and their performance may be sub-optimal. The traditional strategy based on a single controller, such as a proportional-integral-derivative (PID) controller, cannot cope with the complicated process control, and their performance may be sub-optimal For this problem, this study proposes an intelligent control strategy by
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