Solid fuel decomposition modelling for the design of biomass gasification systems

Brown, David; Fuchino, Tetsuo; Maréchal, François

doi:10.1016/s1570-7946(06)80286-5

Cited by 29 publications

(13 citation statements)

References 13 publications

(10 reference statements)

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“…The gasification reactions have been modelled with temperature difference parameters for chemical equilibrium calculations, i.e., mimetic estimators of the non-equilibrium product distribution of biomass gasification [13]. Firstly, light gas species, total tar and char concentration were verified by mass balance reconciliation.…”

Section: Gasification Reaction Modellingmentioning

confidence: 99%

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Thermo-economic analysis for the optimal conceptual design of biomass gasification energy conversion systems

Brown

Gassner

Fuchino

et al. 2009

Applied Thermal Engineering

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Section: Gasification Reaction Modellingmentioning

confidence: 99%

“…[9][10][11], and the heat exchanger network structure resolved by a sub-problem that uses the heat cascade concept [12]. A stoichiometric equilibrium model with reaction temperature difference parameter regressions has been implemented to account for the non-equilibrium distribution of gasification products [13].…”

Section: Introductionmentioning

confidence: 99%

Thermo-economic analysis for the optimal conceptual design of biomass gasification energy conversion systems

Brown

Gassner

Fuchino

et al. 2009

Applied Thermal Engineering

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“…It was concluded that the neural network model has better precision over the traditional model (Dong et al, 2003). A combined non-stoichiometric equilibrium approach with an artificial neural networks regression model was developed to predict product composition in an atmospheric air gasification fluidized bed reactor (Brown et al, 2006). A complete set of stoichiometric equations were formulated to explain the non-equilibrium behaviour for gas, tar, and char formation by reaction temperature difference.…”

Section: Introductionmentioning

confidence: 99%

“…The artificial neural networks regression related temperature differences to fuel composition and operational variables. This first principle approach, illustrated with FB data, improves the accuracy of the equilibrium based model and reduces the data requirement by preventing neural network to learn from atomic and heat balances (Brown et al, 2006). The combination of equilibrium and artificial neural networks models were further investigated and improved by the same authors (Brown et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

Pandey

Pan

Das

et al. 2015

Bioresource Technology

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A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalize well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based datadriven modelling strategy can be a good candidate for developing models for other types of fuels as well.

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“…[11], modelizations can be divided into the simpler and more general, thermo-chemical equilibrium approaches and the more complex, but case-specific, kinetic-rate modeling ones. There are also methods which combine both above approaches and even completely different ones, such as those based on neural networks [12,13].…”

mentioning

confidence: 99%

Performance Analysis of a Woodchip Downdraft Gasifier: Numerical Prediction and Experimental Validation

Manzino¹,

Olampi²,

Pittaluga³

2015

JEPE

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Abstract:The study deals with a multi-faceted theoretical approach, symbolic, analytical and numerical, based on the chemical equilibrium assumption, addressed at predicting the performance trends of downdraft wood-gasification processes so to assess the optimal ranges of input parameters, in particular the equivalence ratios, suitable to achieving the highest cold gas efficiencies whilst keeping the more the possible tar-free the produced bio-syngas. The time-steady, zero-dimensional model has been developed within MATLAB® (the computing language and interactive environment from Matrix Laboratory) and solved by enforcing the constraints posed by the equilibrium constants in relation to two reactions, gas-water shift and methanation. Particular care is devoted toward verifying the real attainment of the equilibrium condition, as attested by an actual presence of products from the equilibrium reactions together with a zero difference ∆ between the energy flows entering and exiting the system, an issue often overlooked. With respect to other similar theoretical approaches, the numerical model, assisted by the symbolic counterpart for better interpretation and intrinsic validation of results, shows a distinct advantage in predicting rather accurately the syngas composition for varying gasification temperatures, as attested by cross comparisons with experimental data directly taken on an instrumented, dedicated, small-scale downdraft gasifier operational at DIME/SCL (the Savona Combustion Laboratory of DIME, the Dept. of Mechanical, Energy, Management and Transportation Engineering of Genova University). The behavior of cold gas efficiency clearly points out that, from an energy conversion point of view, the optimal gasification temperatures turn out comprised between 900 °C and 1,000 °C: this range is indeed characterized by the highest concentrations in the energy-rich syngas components CO and H 2 . For higher temperatures, as induced by higher air-to-fuel ratios, the progressive oxidation of above components, together with increasing nitrogen levels, would decrease the bio-syngas heat values.

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Solid fuel decomposition modelling for the design of biomass gasification systems

Cited by 29 publications

References 13 publications

Thermo-economic analysis for the optimal conceptual design of biomass gasification energy conversion systems

Thermo-economic analysis for the optimal conceptual design of biomass gasification energy conversion systems

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

Performance Analysis of a Woodchip Downdraft Gasifier: Numerical Prediction and Experimental Validation

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