A semi-empirical approach towards predicting producer gas composition in biomass gasification. (January 2019)

• Record Type: Journal Article
• Authors: Pradhan, Priyabrata; Arora, Amit; Mahajani, Sanjay M.
• Is Part Of: Bioresource technology. Volume 272(2019)
• Page Start: 535
• Page End: 544
• Publication Date: 2019-01
• Subjects: Equilibrium model -- Biomass feedstock -- Gasification -- Producer gas -- Correlations; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2018.10.073
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: Formulation and comparison of five equilibrium models for biomass gasification. The best predictive model, with specific set of reactions, was identified. Model was used to predict product composition for 50 different biomass species. A new semi-empirical approach was used to develop correlations for gas composition. Abstract: The paper provides a comparison of five distinct models, often used in thermodynamic equilibrium modeling that allows the study of feedstock effect on gasification process. The five models were thus formulated and solved using MATLAB software. The results were compared with published experimental data. The model based on equilibrium constant derived using methane formation reaction and water gas shift reaction showed comparatively better performance. Once the model was selected, the response surface analysis was employed to predict the parameters, such as reactor temperature and feedstock moisture content, for a maximum heating value of the producer gas. Simulations were performed for 50 different biomass feedstocks and simplified correlations were developed from simulated producer gas composition using multiple linear regression. These correlations may be suited for the quick comparison of different feedstocks in gasification process.