A comprehensive review on Advanced Process Control of cement kiln process with the focus on MPC tuning strategies. (January 2023)

• Record Type: Journal Article
• Authors: Ramasamy, Valarmathi; Kannan, Ramkumar; Muralidharan, Guruprasath; Sidharthan, Rakesh Kumar; Veerasamy, Gomathi; Venkatesh, Sivanandam; Amirtharajan, Rengarajan
• Is Part Of: Journal of process control. Volume 121(2023)
• Page Start: 85
• Page End: 102
• Publication Date: 2023-01
• Subjects: Cement kiln -- Model Predictive Controller -- Kilnmodel -- Weight tuning -- Plant model mismatch; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.12.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: The cement kiln is one of the major energy-intense processes that need efficient controllers to minimise fuel consumption, enhance clinker production, and improve cement quality. A significant volume of research has been reported on modelling and controlling the cement kiln process, making it an ongoing research problem. Hence, there is a need for a comprehensive review to provide insight into cement kiln control strategies, which have been presented in this work. A brief introduction to cement kiln operation is presented to understand the complexities and challenges of controller design. A literature survey on cement kiln modelling indicates both traditional and intelligent techniques. Box Jenkins and Elman-based neural network models are reported to capture cement kiln dynamics accurately. The process model, controller, and kiln control parameters reported in various literature are analysed and presented. It indicates the predominant use of Model Predictive Control (MPC) to control the Burning Zone Temperature (BZT) of the cement kiln. MPC is one of the APC technology which can accommodate practical constraints in the form of bounds and provide optimal control action making it the preferred controller for cement kilns. Receding horizons, MPC weights, and prediction models are the major parameters that govern MPC control performance. Various tuning strategies to tune these MPC parameters have been reported in the literature.