Prediction of species concentration in syngas produced through gasification of different bamboo biomasses: a numerical approach

Gopan, Gokul; Hauchhum, Lalhmingsanga; Pattanayak, Satyajit; Kalita, Pankaj; Krishnan, R.P.

doi:10.1007/s40095-022-00492-7

Cited by 4 publications

(4 citation statements)

References 60 publications

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“…Figure 1 shows that the trend of K 1 , K 2 , K 5 are similar. This similarity was explained by Gopan et al [28] and Zainal et al [24], who found that Boundaourd (R1) and a heterogeneous water gas shift (R2) may generate a water gas shift reaction (R5). Therefore, the trend of their reaction constants (K i ) and reactions (Ri) could be similar.…”

Section: Basis Modelsupporting

confidence: 53%

“…The mole number of the carbon (n 7 ) in models M4, M5, and M6 has not been defined in Equations ( 5)-( 8) and ( 25)- (28). Therefore, Equation ( 40) is applied to estimate "n 7 " [25,33]…”

Section: Simulated Modelmentioning

confidence: 99%

“…The change of the equilibrium constant of the relevant reaction was further determined by the number of gas molecules produced during the gasification. The chemical composition of n 1 -n 8 can be determined by solving Equations ( 5)-( 8) and ( 25)- (28). The equilibrium constant of each reaction (R3, R4, R5, and R6) is computed using Equation ( 46), and the results in terms of ∆H, yield of tar, and RMSE of factor model A-D for each developed model M1-M6 are displayed in Table 11.…”

Section: Correction Factors and New Empirical Correlation For The Equ...mentioning

confidence: 99%

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Development Models of Stoichiometric Thermodynamic Equilibrium for Predicting Gas Composition from Biomass Gasification: Correction Factors for Reaction Equilibrium Constants

Suparmin,

Nelwan,

Mardjan

et al. 2024

Applied Sciences

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A complex thermochemical process during biomass gasification includes many chemical reactions. Therefore, a stoichiometric model can be applied to predict the composition of the producer gas during gasification. However, the prediction of methane and hydrogen gas is still limited by a significant margin using the present stoichiometric models. The purpose of this research was to develop novel stoichiometric models that account for the reaction equilibrium constant with correction factors. The new models would enable forecasting of the composition of CO, CO2, CH4, H2, N2, tar, lower heating value (LHV), and cold gasification efficiency (CGE). Model development consisted of two stages, whereas the development of the models and their validation adopted an artificial neural network (ANN) approach. The first stage was calculating new correction factors and defining the new equilibrium constants. The results were six stoichiometric models (M1–M6) with four sets of correction factors (A–D) that built up the new equilibrium constants. The second stage was validating the models and evaluating their accuracy. Validation was performed by the Root Mean Square Error (RMSE), whereas accuracy was evaluated using a paired t-test. The developed models predicted the composition of the producer gas with an RMSE of less than 3.5% and ΔH-value of less than 0. The models did not only predict the composition of the producer gas, but they also predicted the tar concentration. The maximum tar concentration was predicted by M2C with 98.733 g/Nm3 at O/C 0.644, H/C 1.446, ER 0.331, and T 923 K. The composition of producer gases (CO, CO2, H2, and N2) was accurately predicted by models M1D, M2C, and M3C. This research introduces new models with variables N/C, O/C, H/C, ER, and T to simulate the composition of CO, CO2, CH4, H2, N2, and LHV-gas, with R2 > 0.9354, tar (C6H6)-R2 of 0.8638, and CGE-R2 of 0.8423. This research also introduces correction factors and a new empirical correlation for the reaction equilibrium constants in new stoichiometric models using steam reforming.

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Section: Basis Modelsupporting

confidence: 53%

“…The mole number of the carbon (n 7 ) in models M4, M5, and M6 has not been defined in Equations ( 5)-( 8) and ( 25)- (28). Therefore, Equation ( 40) is applied to estimate "n 7 " [25,33]…”

Section: Simulated Modelmentioning

confidence: 99%

Section: Correction Factors and New Empirical Correlation For The Equ...mentioning

confidence: 99%

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Development Models of Stoichiometric Thermodynamic Equilibrium for Predicting Gas Composition from Biomass Gasification: Correction Factors for Reaction Equilibrium Constants

Suparmin,

Nelwan,

Mardjan

et al. 2024

Applied Sciences

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“…This is sufficient to generate electricity that can meet 17.37% of the energy consumption in Northeast India. In a separate study, Gopan et al (2022) used modeling to predict syngas concentration for six bamboo species ( B. balcooa , Chimonobambusa callosa Munro, B. vulgaris cv. Wamin, Oxytenanthera albociliata Munro, and Dendrocalamus longispathus Kurz.)…”

Section: Introductionmentioning

confidence: 99%

Potential use of bamboo resources in energy value‐added conversion technology and energy systems

et al. 2023

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Bamboo has been identified as a promising solution to the energy crisis and climate change as a source of biomass energy. Due to its rapid growth and high‐value products, bamboo is considered as a potential source of biomass energy. Bamboo contains a significant amount of cellulose and hemicellulose, which can be converted to sugar constituents, making it an ideal raw material for energy production. This article reviews the different processes of producing bioethanol, biogas, biochar, and bio‐oil from bamboo biomass using techniques such as pyrolysis, hydrothermal liquefaction, fermentation, and anaerobic digestion, and discusses the opportunities and challenges of these conversion technologies. It also reviews the main types and morphological characteristics of energy bamboo species and proposes an evaluation system for energy bamboo species, which optimizes the utilization efficiency of bamboo biomass energy and maximizes benefits by adopting appropriate methods for producing bioenergy based on the characteristics of different bamboo species.

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Bamboo as Source of Energy

Jalil

Elham

Sarif

et al. 2023

Multifaceted Bamboo

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Prediction of species concentration in syngas produced through gasification of different bamboo biomasses: a numerical approach

Cited by 4 publications

References 60 publications

Development Models of Stoichiometric Thermodynamic Equilibrium for Predicting Gas Composition from Biomass Gasification: Correction Factors for Reaction Equilibrium Constants

Development Models of Stoichiometric Thermodynamic Equilibrium for Predicting Gas Composition from Biomass Gasification: Correction Factors for Reaction Equilibrium Constants

Potential use of bamboo resources in energy value‐added conversion technology and energy systems

Bamboo as Source of Energy

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