A novel self-learning fuzzy predictive control method for the cement mill: Simulation and experimental validation. (April 2023)

• Record Type: Journal Article
• Authors: Ma, Tianyu; Li, Zhipeng; Liu, Jinping; Alkhateeb, Abdulhameed F.; Jahanshahi, Hadi
• Is Part Of: Engineering applications of artificial intelligence. Volume 120(2023)
• Publication Date: 2023-04
• Subjects: Fuzzy predictive control -- Cement grinding process -- Difference step response; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105868
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The quality and ratio of clinker, the fineness of cement are key factors affecting the strength of cement. In order to realize the target tracking control of cement strength, a self-learning fuzzy predictive control algorithm is proposed to calculate the adjustment variables of cement grinding process Considering the serious hysteresis of cement strength detection, the 3-day cement strength needs to be predicted, statistical models cannot actually guide the production for the loss of working conditions. In this paper, a 3-day strength prediction model is established based on step response which is trained by difference data. The cement grinding process is a complex industrial process which is composed of multiple sub-processes and the variables are coupled each other, according to the characteristic, a multi-variable distributed fuzzy control algorithm (MVDFC) is proposed, and the fuzzy rules are mined by Apriori algorithm. The verification results from the actual field data demonstrate the effectiveness and superiority of the proposed method, which can completely meet the actual needs of the process. Highlights: A Self-Learning Fuzzy Predictive Control Algorithm for Calculation of the Adjustment Variables of Cement Grinding Process. Creating a 3-day cement strength prediction model based on step response and training it by difference data. Proposing a multi-variable distributed fuzzy control algorithm and extracting fuzzy rules by Apriori algorithm.