Model predictive control of a dual fluidized bed gasification plant

Lukas Stanger, Alexander Bartik, Martin Hammerschmid, Stefan Jankovic, Florian Benedikt, Stefan Müller, Alexander Schirrer, Stefan Jakubek and Martin Kozek

Applied Energy, 2024, vol. 361, issue C, No S0306261924003003

Abstract: Dual fluidized bed (DFB) gasification is a promising method for producing valuable gaseous energy carriers from biogenic feedstocks as a substitute for fossil fuels. State-of-the-art DFB gasification plants mainly rely on manual operation or single-input single-output control loops, and scientific contributions only exist for controlling individual process variables. This leaves a research gap in terms of comprehensive control strategies for DFB gasification. To address this gap, we propose a multivariate control strategy that focuses on crucial process variables, such as product gas quantity, gasification temperature, and bed material circulation rate. Our approach utilizes model predictive control (MPC), which enables effective process control while explicitly considering process constraints. A simulation study is given demonstrating how different MPC parametrizations influence the behavior of the closed-loop system. Experimental results from a 100kW pilot plant at TU Wien demonstrate the successful control achieved by the proposed control algorithm.

Keywords: Model predictive control; Automatic control; DFB; Gasification; Biomass; Fluidized bed (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924003003
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:361:y:2024:i:c:s0306261924003003

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.122917

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().