Biomass Steam Gasification: A Comparison of Syngas Composition between a 1-D MATLAB Kinetic Model and a 0-D Aspen Plus Quasi-Equilibrium Model

Marcantonio, Vera; Ferrario, Andrea Monforti; Carlo, Andrea Di; Zotto, Luca Del; Monarca, Danilo; Bocci, Enrico

doi:10.3390/computation8040086

Cited by 25 publications

(14 citation statements)

References 40 publications

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“…The main reactions of the gasification process that have been considered in the modeling are shown in Table 6 19 . In this study, the gasification agents steam.…”

Section: System Modelingmentioning

confidence: 99%

Evaluation of performance improvement of the combined biomass gasifier power cycle using low‐temperature bottoming cycles: Organic Rankine cycle, Kalina and Goswami

Hosseinpour

Ozgoli

Mirzahosseini

et al. 2022

Env Prog and Sustain Energy

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A novel combination of a conventional combined power plant and low-temperature power generators as a cogeneration cycle coupled with a biomass gasifier is presented in this study. For this purpose, mathematical modeling of organic Rankine cycle (ORC), Kalina, and Goswami cycles was performed as a bottoming power system. To achieve the goals of sustainable energy development, a portion of the fuel required for the proposed cycle has been provided using renewable biomass fuels. The results indicated that the base cycle heat recovery in all proposed scenarios would increase energy efficiencies by 6.1%, 3.3%, and 1.1%, respectively, to ORC, Kalina, and Goswami cycles. Furthermore, the results of the parametric studies on principal factors such as steam to biomass and equivalence ratios in the gasifier, working fluid types in the ORC, mass fraction of ammonia-water mixture in Kalina and Goswami cycles, as well as thermodynamic properties of the expander and condenser can be highly considered for the energy industry developers.

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“…The main reactions of the gasification process that have been considered in the modeling are shown in Table 6 19 . In this study, the gasification agents steam.…”

Section: System Modelingmentioning

confidence: 99%

Evaluation of performance improvement of the combined biomass gasifier power cycle using low‐temperature bottoming cycles: Organic Rankine cycle, Kalina and Goswami

Hosseinpour

Ozgoli

Mirzahosseini

et al. 2022

Env Prog and Sustain Energy

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“…The HHV and LHV of biomass in MJ/kg are calculated using Equations () and () 54 :

{HHV}_{B} = ((), 0.3491 \times C) + ((), 1.1783 \times H) - ((), 0.1034 \times O) - ((), 0.0151 \times N) - ((), 0.0211 \times A),

{LHV}_{B} = {HHV}_{B} - 21.97 ([], H),

where C , H , O , N , and A are the mass fractions of carbon, hydrogen, oxygen, nitrogen, and ash in the ultimate analysis, respectively. The LHV of the syngas can be determined by the standard heating value of combustible components of the syngas according equation 4 55 :

{LHV}_{SG} = H_{CO} \times W_{CO} + H_{{normalH}_{2}} \times W_{{normalH}_{2}} + H_{{CH}_{4}} \times W_{{CH}_{4}},

where LHV SG is the lower heating value of syngas (MJ/kg); W CO ,

W_{{normalH}_{2}}

, and

W_{{CH}_{4}}

represent the mass fractions of CO, H 2, and CH 4 , respectively; H is standard LHV (120, 10, and 50 MJ/kg for H 2 , CO, and CH 4, respectively).…”

Section: Gasifier Performance Evaluationmentioning

confidence: 99%

Hydrogen production via integrated configuration of steam gasification process of biomass and water‐gas shift reaction: Process simulation and optimization

Babatabar

Saidi

2021

Int J Energy Res

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Summary A comprehensive model is developed to predict and optimize the hydrogen production via integrated configuration of steam gasification process of biomass and water‐gas shift reaction by taking advantage of the ASPEN plus software and sensitivity analysis techniques. The steam gasification process of three different generations of biomass, including corn cob as the first generation, wood residue and rice husk as second generation, and Spirulina algae as the third, are investigated and evaluated to achieve maximum hydrogen production. This model is successfully validated with experimental data reported in the literature about steam gasification of rice husk in the fluidized bed reactor. The impact of main operating parameters is considered in terms of products composition, hydrogen yield, CO conversion, H2/CO ratio in the gas products stream, and cold gas efficiency (CGE). The results indicated that the maximum hydrogen concentration is achieved at the highest steam to biomass (S/B) ratio in the gasifier and water‐gas shift (WGS) reactor and at the lowest WGS reaction temperature, whereas there is an optimum value about the gasification temperature. The highest CO conversion and H2/CO molar ratio belong to rice husk biomass at all considered range of temperature, whereas they are almost the same for wood residue and Spirulina. The predicted results confirmed that CGE of all feedstocks improves with increasing gasification temperatures and S/B ratio. The steam gasification performance of different feedstocks at 750°C is ranked as wood residue (83.56%) > spirulina (83.47%) > corn cob (74.94%) > rice husk (69.86%). The presented configuration can be applied as a novel approach for process evaluation and optimization through the downdraft biomass gasification integrated with water‐gas shift reaction to intensify the hydrogen production.

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“…For example, Safarian et al simulated a gasification process in Aspen Plus using a Gibbs reactor to calculate the equilibrium point minimizing the Gibbs free energy [2]. Marcantonio et al also modeled gasification using a Gibbs reactor inside Aspen Plus, which they compared against a more accurate kinetic model simulated in MATLAB [3].…”

Section: Introductionmentioning

confidence: 99%

Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers

Binns

Ayub

2021

Sustainability

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Various modeling approaches have been suggested for the modeling and simulation of gasification processes. These models allow for the prediction of gasifier performance at different conditions and using different feedstocks from which the system parameters can be optimized to design efficient gasifiers. Complex models require significant time and effort to develop, and they might only be accurate for use with a specific catalyst. Hence, various simpler models have also been developed, including thermodynamic equilibrium models and empirical models, which can be developed and solved more quickly, allowing such models to be used for optimization. In this study, linear and quadratic expressions in terms of the gasifier input value parameters are developed based on linear regression. To identify significant parameters and reduce the complexity of these expressions, a LASSO (least absolute shrinkage and selection operator) shrinkage method is applied together with cross validation. In this way, the significant parameters are revealed and simple models with reasonable accuracy are obtained.

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Biomass Steam Gasification: A Comparison of Syngas Composition between a 1-D MATLAB Kinetic Model and a 0-D Aspen Plus Quasi-Equilibrium Model

Cited by 25 publications

References 40 publications

Evaluation of performance improvement of the combined biomass gasifier power cycle using low‐temperature bottoming cycles: Organic Rankine cycle, Kalina and Goswami

Evaluation of performance improvement of the combined biomass gasifier power cycle using low‐temperature bottoming cycles: Organic Rankine cycle, Kalina and Goswami

Hydrogen production via integrated configuration of steam gasification process of biomass and water‐gas shift reaction: Process simulation and optimization

Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers

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