Multi-rate modeling and economic model predictive control of the electric arc furnace. (April 2016)

• Record Type: Journal Article
• Authors: Rashid, Mudassir M.; Mhaskar, Prashant; Swartz, Christopher L.E.
• Is Part Of: Journal of process control. Volume 40(2016:Apr.)
• Page Start: 50
• Page End: 61
• Publication Date: 2016-04
• Subjects: Economic model predictive control -- Multi-rate models -- Data-driven models -- Electric arc furnace; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.12.012
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract : Highlights: Built data-driven models for the electric arc furnace (EAF) process. Identified multi-rate models for available infrequent and frequent EAF measurements. Formulated and implemented a two-tiered economic model predictive controller (EMPC). EMPC determines the best achievable end-point and minimizes the operating costs. Simulation studies demonstrate that end-points are satisfied while minimizing costs. Abstract: In this manuscript, we consider the problem of multi-rate modeling and economic model predictive control (EMPC) of electric arc furnaces (EAF), which are widely used in the steel industry to produce molten steel from scrap metal. The two main challenges that we address are the multi-rate nature of the measurement availability, and the requirement to achieve final product of a desired characteristic, while minimizing the operation cost. To this end, multi-rate models are identified that include predictions for both the infrequently and frequently measured process variables. The models comprise local linear models and an appropriate weighting scheme to capture the nonlinear nature of the EAF. The resulting model is integrated into a two-tiered predictive controller that enables achieving the target end-point while minimizing the associated cost. The EMPC is implemented on the EAF process and the closed-loop simulation results subject to the limited availability of process measurements and noise illustrate the improvement in economic performance over existing trajectory-tracking approaches.