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Krail, Jürgen
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Official Name
Krail, Jürgen
Main Affiliation
Akademische Titel
Prof.(FH) DI DI(FH)
Email
juergen.krail@fh-burgenland.at
ORCID
Scopus Author ID
57210393827
Status
staff
Research Outputs
Now showing 1 - 3 of 3
- PublicationComparative thermodynamic analysis of an improved ORC process with integrated injection of process fluidIn contrast to water-steam Rankine cycles, the ORC process uses organic working fluids. For working fluids of the dry class, a recuperator heat exchanger is frequently installed to increase the cycle efficiency. This paper analyses an improved ORC process with these features: A liquid working fluid stream is injected into the vapour flow between the high-pressure and the medium-pressure stage of the turbine. Furthermore, the recuperator is replaced by a spray condenser. The main objective is to increase efficiency with moderate changes in the process layout. A thermodynamic comparison of the improved process with a state-of-the-art ORC process is carried out by simulations and optimisations. A significant efficiency gain for the improved ORC process is obtained by a combination of the aforementioned features, mainly because of an increase of the mass flow in the economiser of the vapour generator (better heat utilization) and a corresponding mass flow in the medium stage of the turbine (additional power production). As a use case, waste heat utilization from clinker cooler at a temperature level of 275 °C was simulated. The improved process would lead to a significant increase in the overall net efficiency by up to 14%, compared to a state-of-the-art ORC process.
124 12706Scopus© Citations 5 - Publication
223 29644Scopus© Citations 8 - PublicationEmission limited model predictive control of a small-scale biomass furnace(Elsevier, 2021-03-01)
; ; ; Kozek, MartinThis paper presents the application of an emission limiting model-based predictive controller for a small-scale biomass grate furnace. The furnace has a nominal power of 100 kW with wood pellets as fuel, but it can be operated with different fuels as well. The model predictive approach extends the existing static feedforward controller of the investigated furnace with a dynamic feedback controller that is able to improve the combustion performance. Simultaneously, the formation of carbon monoxide emissions is minimized within the prediction horizon based on an available emission estimation model for pellets. The results obtained from closed-loop measurements show that the control concept is able to reduce carbon monoxide emissions in partial load operation up to four times while the control error of the supply water temperature for heating is nearly halved during transient operation. This is achieved by incorporating the emission estimation model into the constrained optimization of the predictive controller. Additional results obtained from closed-loop experiments for different fuel types with varying water contents demonstrate the advantages of the proposed model-based approach and its robustness with respect to typical uncertainties of the combustion process.207 33764Scopus© Citations 6