Development of feed-forward model predictive control for applications in biomass bubbling fluidized bed boilers. (July 2022)

• Record Type: Journal Article
• Authors: Zlatkovikj, Milan; Li, Hailong; Zaccaria, Valentina; Aslanidou, Ioanna
• Is Part Of: Journal of process control. Volume 115(2022)
• Page Start: 167
• Page End: 180
• Publication Date: 2022-07
• Subjects: Biomass fueled CHP -- Biomass properties -- Bubbling fluidized bed -- Model predictive control (MPC) -- Feed-forward (FF) -- Dynamic simulations; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.05.005
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In order to accommodate more intermittent renewable energy sources, biomass fueled combined heat and power plants (bio-CHPs) can contribute towards sustainable and flexible energy systems. However, the varying properties of biomass, such as moisture contents and heating values, can clearly affect the combustion in boilers, which further affects the flexibility provided by bio-CHPs. In order to achieve better control, this paper proposes a feed-forward model predictive controller (FF MPC) to handle the variation of biomass properties. A dynamic model was built in Dymola to simulate the performance of a bubbling fluidized bed boiler, which was validated against the real operation data. Based on the simulation, the key manipulated variables were optimized for the given controlled variables. The advantages of the proposed FF MPC were demonstrated through comparisons with proportional–integral (PI), FF PI and MPC. The results of FF MPC show the best performance, such as the lowest magnitude of fluctuations for 3 outputs (thermal load, steam and fluidized bed temperature), and the most stable operation. Consequently, FF MPC can potentially increase the electricity generation and further lead to an economic benefit. Using one week in winter as an example, compared to PI, FF PI and MPC, FF MPC can generate more electricity and improve revenues by 14.77 MWh/590 €, 4.1 MWh/164 € and 5.03 MWh/211.2 € respectively. Graphical abstract: Highlights: Development of dynamic model for large scale boiler and validation with plant data. Identification of linear state space to develop model predictive control (MPC). Comparison of MPC and PI control results on various metrics and scenarios. MPC showed better control during most scenarios and analyzed variables. Benefits for the boiler are higher revenues, less deviation and better capacity use.