Discrepancy-based control of particle processes. (February 2022)

• Record Type: Journal Article
• Authors: Otto, Eric; Behrens, Jessica; Palis, Stefan; Dürr, Robert; Kienle, Achim
• Is Part Of: Journal of process control. Volume 110(2022)
• Page Start: 99
• Page End: 109
• Publication Date: 2022-02
• Subjects: Discrepancy-based control -- Infinite-dimensional control -- Particle processes -- Agglomeration -- PI-control -- Input–output-linearization; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.11.014
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: The production of goods in particulate form is important in many industries such as the chemical, pharmaceutical and food industry. For the subsequent use, specific particle properties determine the quality of the product. In order to ensure high quality and furthermore a constant production rate, process control methods can be applied. Here we present discrepancy-based control for a generic agglomeration process with additional particle breakage. The advantage of this control approach is the direct and simple control design using the nonlinear and infinite-dimensional system description as well as guaranteed stability for the chosen process variables. Two practically relevant scenarios are investigated. First, it is shown that control laws for two moments of the particle size distribution ensure the production of agglomerates with a constant Sauter mean diameter. Then, control laws for the production rate are designed. The resulting closed-loop behavior is investigated by means of dynamic simulation. It is shown that the control laws improve systems convergence speed for both set point tracking and disturbance rejection and achieve zero steady state error even under the occurrence of non-vanishing disturbances. Highlights: Control of a particle formation population balance with aggregation and breakage. Design of discrepancy-based MIMO-control laws. Control of particle size and particle production rate. Validation of controller performance and robustness in a simulation study.