Modelling of grate combustion in a medium scale biomass furnace for control purposes

Bauer, Robert A.; Gölles, Markus; Brunner, T.; Dourdoumas, Nicolaos; Obernberger, Ingwald

doi:10.1016/j.biombioe.2009.12.005

Cited by 67 publications

(37 citation statements)

References 26 publications

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“…∆H CO Enthalpy of CO formation (kJ/mol) ∆H CO 2 Enthalpy of CO 2 formation (kJ/mol) ∆H evap Enthalpy of vaporization (kJ/kg) ∆H p,i Pyrolysis enthalpy of component i (kJ/kg) A Pre-exponential factor (s −1 ) C CH 4 Concentration of methane (mol/cm 3 ) C CO Concentration of carbon monoxide (mol/cm 3 ) C H 2 O Concentration of steam (mol/cm 3 ) C O 2 Concentration of oxygen (mol/cm 3 ) C p,O 2 Heat capacity of oxygen (J/(kg K))…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…Rate constant for the char reaction with oxygen (kg/(m 3 ·s)) k R,CO 2 Rate constant for the char reaction with carbon dioxide (kg/(m 3 ·s)) Rate of moisture evaporation due to the heat transfer between gas and solid r evap,rad…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…Rate of moisture evaporation due to the radiative heat transfer from char layer r g,CH 4 Oxidation rate of methane (kg/(m 3 ·s)) r g, CO Oxidation rate of the carbon monoxide (kg/(m 3 ·s)) r g,H 2 Oxidation rate of hydrogen (kg/(m 3 ·s)) Stoichiometric coefficient of pyrolytic gas combustion…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…Similarly, the most recent application of this technique discusses modelling of boilers of various scales. Bauer et al [2] developed a gray box model whose structure was based on process knowledge obtained by analyzing mathematical correlations of char combustion. In order to fit the predictions of the model, to the process data empirical coefficients were obtained through model identification.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Simplification of a Mechanistic Model of Biomass Combustion for On-Line Computations

Boriouchkine

Jämsä‐Jounela

2016

Energies

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Increasing utilization of intermittent energy resources requires flexibility from energy boilers which can be achieved with advanced control methods employing dynamic process models. The performance of the model-based control methods depends on the ability of the underlying model to describe combustion phenomena under varying power demand. This paper presents an approach to the simplification of a mechanistic model developed for combustion phenomena investigation. The aim of the approach is to simplify the dynamic model of biomass combustion for applications requiring fast computational times while retaining the ability of the model to describe the underlying combustion phenomena. The approach for that comprises three phases. In the first phase, the main mechanisms of heat and mass transfer and limiting factors of the reactions are identified in each zone. In the second phase, each of the partial differential equations from the full scale model are reduced to a number of ordinary differential equations (ODEs) defining the overall balances of the zones. In the last phase, mathematical equations are formulated based on the mass and energy balances formed in the previous step. The simplified model for online computations was successfully built and validated against industrial data.

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Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simplification of a Mechanistic Model of Biomass Combustion for On-Line Computations

Boriouchkine

Jämsä‐Jounela

2016

Energies

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“…A change in moisture content of the fuel has to be detected within seconds so that the control system is able to make correct adjustments to the combustion air supply and the fuel feed system. There is, thus, a strong need to supply control systems or operators with information about moisture content for necessary adjustments of the combustors [4].…”

Section: Introductionmentioning

confidence: 99%

Fuel moisture soft-sensor and its validation for the industrial BioPower 5 CHP plant

Kortela

Jämsä‐Jounela

2013

Applied Energy

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This paper presents a soft-sensor for on-line monitoring of fuel moisture in a furnace. The method utilizes combustion power estimation and a dynamic model of the secondary superheater. In addition, the time delays in detecting changes in moisture content of the fuel are small enough for the method to be used for controlling air and fuel feed and preventing steam and pressure oscillations. To verify the fuel moisture soft-sensor, experiments were performed at a BioPower 5 CHP plant, which utilizes BioGrate combustion technology for very wet biomass fuels with a moisture content as high as 65%. Finally, the results are analyzed and discussed.

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Transformation of Biomass (Pyrolysis, Gasification, Combustion)

Ramsurn

Gupta

2016

Handbook of Combustion

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Biomass is one of the largest primary resources available, and its use results in no net carbon emissions and lower SO x and NO x . However, the transformation of biomass into fuels is very challenging because of its complex lignocellulosic structure. Although biological processes have been used to convert biomass, they require catalysts which are usually selective and generate low yields of the desired products. Thermochemical routes include gasification, pyrolysis and combustion, all of which are energy‐intensive. Although combustion, through co‐firing of biomass and coal, is the only proven technology, improvements are still needed to reduce environmental impacts. Research is also being conducted to implement sufficient gas mixing in the furnaces, as well as optimization of the furnace design. Because of its greater overall conversion efficiency and proven gasifier performance, gasification is more likely to be commercially viable; however, pyrolysis has great potential to produce liquid fuels as well as value‐added products. Further research is needed to investigate catalytic gasification and pyrolytic processes and the catalytic upgrading of the bio‐oils and gases produced.

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Modelling of grate combustion in a medium scale biomass furnace for control purposes

Cited by 67 publications

References 26 publications

Simplification of a Mechanistic Model of Biomass Combustion for On-Line Computations

Simplification of a Mechanistic Model of Biomass Combustion for On-Line Computations

Fuel moisture soft-sensor and its validation for the industrial BioPower 5 CHP plant

Transformation of Biomass (Pyrolysis, Gasification, Combustion)

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