Project Description

Austria is one of the pioneers in the development and commissioning of biomass boiler systems and makes an environmentally friendly contribution to district heating. However, with the size of the biomass boiler plants, the requirements for combustion controller and flue gas cleaning also increase. The EmiL project creates the prerequisites for efficient and low-emission biomass boilers on the basis of fundamental studies.

Project Goal

The goal of low-emission biomass combustion is to be achieved by combining primary measures in the area of ​​combustion sensors and combustion control as well as secondary measures in the area of ​​cost-efficient particulate matter separation technology. The desired project results are developed in close cooperation between the research institute, university and boiler manufacturers.

The research area Control Engineering and Process Automation investigates the modeling of a model-predictive control for combustion optimization, which is tested in an experimental setup.

Results

The methodology for researching the project results is based on experimental investigations on test benches and in real plant operation. An innovative model predictive control is developed, which is experimentally investigated by implementation in a LabVIEW® environment via an interface to the boiler control. In the field of fine dust separation, the integration of fine dust separators in the boiler body is being tested on the basis of CFD simulations and experimental fundamental investigations on a test vehicle.

In the first step, a MIMO model of the boiler was created on the basis of combustion tests on the boiler test bench. The modeling is based on the underlying physical principles, while the thermochemical reactions in the boiler were approximated using so-called gray box models. Unknown parameters and non-measurable quantities could be determined from existing measurement data with the help of non-linear optimization methods.

The nonlinear boiler model was then subdivided into favorable operating points using a so-called “gap-metric”, a measure of the difference between two transfer functions, around which a linearization was carried out in a further step. The combination of the above methods makes it possible to implement a linear, model-based state controller on the test bench that covers the entire working range of the boiler. In particular, the previously mentioned model-predictive controller was selected, which, in addition to its predictive property, also elegantly takes into account manipulated variable limitations without the need for classic measures such as anti-windup. An important feedback variable for the control is the residual oxygen content in the exhaust gas, which is determined using lambda sensors.

Video Presentation

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Video Title: MPC of Small-Scale Biomass Furnaces

Publications

Böhler, Lukas, Markus Fallmann, Gregor Görtler, Jürgen Krail, Florian Schittl, and Martin Kozek. "Emission limited model predictive control of a small-scale biomass furnace., opens an external URL in a new window" Applied Energy 285 (2021): 116414.

Böhler, Lukas, Jürgen Krail, Gregor Görtler, and Martin Kozek. "Fuzzy model predictive control for small-scale biomass combustion furnaces., opens an external URL in a new window" Applied Energy 276 (2020): 115339.

Fallmann, Markus, Lukas Böhler, and Martin Kozek. "Linear Model Predictive Control of Small-Scale Furnaces., opens an external URL in a new window" In Sciene. Research. Pannonia, pp. 281-287. Leykam Buchverlagsgesellschaft mbH., 2020.

Böhler, Lukas, Gregor Görtler, Jürgen Krail, and Martin Kozek. "Carbon monoxide emission models for small-scale biomass combustion of wooden pellets., opens an external URL in a new window" Applied Energy 254 (2019): 113668.

Böhler, Lukas, and Martin Kozek. "Implementation of a fuzzy model predictive controller for biomass combustion., opens an external URL in a new window" In enova 2017, pp. 53-60. Leykam, 2017.

Böhler, Lukas, and Martin Kozek. "Key Influence Factors in Modelling of Biomass Combustion in Small and Medium Scale Furnaces., opens an external URL in a new window" In enova 2016. Leykam, 2016.

Cooperation Partners

Duration

  • April 2016 - September 2020

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Contact

Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Martin Kozek

Send email to Martin Kozek