Thermal modelling of three-way mixing valves using Bézier curves for parameter estimation applications. (June 2020)

• Record Type: Journal Article
• Authors: Fürst, Yannick; Brandt, Stefan; Kriegel, Martin
• Is Part Of: Journal of process control. Volume 90(2020)
• Page Start: 56
• Page End: 62
• Publication Date: 2020-06
• Subjects: System identification -- Parameter estimation -- Mixing valve -- Physical modelling -- Grey-box model -- Thermal behaviour -- JModelica.org; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.04.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Non-linear grey-box modelling approach based on physical relationships to describe the thermal behaviour of mixing valves. Compared to state space models and Hammerstein models, the proposed model is able to describe the validation data much more reliable. Although actuator size limitations are not present in the training data, they are reproduced correctly. The trained model can be used in feed-forward control or for the tuning of PID controllers. The parameter estimation is performed in Python using the Open Source platform JModelica.org. Abstract: Each mixing valve is characterized by its k vs -value and the valve characteristics. In mounted condition, the valve characteristics are deformed and the degree of the deformation is determined by the valve authority. Considering already installed mixing valves, neither the valve authority nor the course of the undeformed valve characteristics are exactly known. The lack of information poses a challenge when physically modelling the relationship between the inlet temperatures, the control signal and the outlet temperature. Based on Bézier curves, a universal non-linear grey-box model to describe the thermal valve behaviour is proposed. It is compared with a state space as well as a Hammerstein model and shows the advantages over black-box approaches. The trained model is very robust, able to predict measurement data very accurately and can be used in control design. The parameter estimation is performed using the Open Source platform JModelica.org .