Fuzzy model predictive control for small-scale biomass combustion furnaces

Lukas Böhler, Jürgen Krail, Gregor Görtler and Martin Kozek

Applied Energy, 2020, vol. 276, issue C, No S0306261920308515

Abstract: This work presents a fuzzy model predictive controller for small-scale grate furnaces based on a newly derived biomass combustion model. Several local linear controllers are designed for a selected number of operating points utilizing a gap metric. The resulting local predictive controllers are merged with membership functions to form a global nonlinear fuzzy control structure. The presented framework intends to improve the transient and steady state operation by applying an optimal control strategy with state estimation and to cover the entire operating range of the furnace. The open loop results of the introduced combustion model are parameterized and cross-validated with measured data from a test furnace. In order to find suitable parameters for the grey-box model, a local sensitivity analysis is conducted to contribute to an efficient parameter estimation process. Closed loop simulation results of the fuzzy model predictive controller, a linear model predictive controller and a PI control algorithm are presented and compared. Based on the performance of the proposed fuzzy controller, its application, advantages and disadvantages are discussed. Additionally, the impact of the different controllers on the formation of carbon monoxide is investigated based on estimation models from literature. The simulation results show that the fuzzy model predictive controller performs best in the considered categories.

Keywords: Biomass; Modeling; Fuzzy; Predictive control; Gap metric; Emission (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920308515
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:276:y:2020:i:c:s0306261920308515

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.2020.115339

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 ().