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• W4200436566 endingPage "122591" @default.
• W4200436566 startingPage "122591" @default.
• W4200436566 abstract "• A novel model was developed based on bubbling bed gasifier integrated with SOFC. • 30 biomass feedstocks were used as fuel in gasification integrated SOFC cycle. • The developed Aspen Plus model was used to train ANN model. • The ANN proved to be a powerful tool for combined power generation systems. With the growing world population and industrial developments, the supply of energy from an economically feasible and widely available source is important. Biomass gasification is a promising technology that produces lower emissions and allows efficient conversion. The gas obtained from the gasification process, especially in steam gasification, consists of a considerable amount of H 2 and is used in fuel cells, especially solid oxide fuel cells (SOFC), to generate electricity. SOFC can convert the chemical energy into electricity and is considered as the most suitable fuel cell type for biomass gasification derived fuels. There are numerous research studies on integrated gasification-SOFC systems in the literature. However, these systems are still under development and studies are being conducted on the appropriate design parameters and operating conditions to achieve high energy efficiency. Modeling of the integrated gasification and SOFC system using the thermodynamic method is the simplest way to determine the process behavior. Nowadays, artificial neural networks (ANN) are one of the most popular modeling methods to represent the thermodynamic based gasification and SOFC systems. In this study, an integrated bubbling fluidized bed gasifier and SOFC model was created to generate data for training the ANN models with Aspen Plus simulation. The ANN models predicted the performance parameters in terms of electrical efficiency, net voltage and current density successfully using the varying operating conditions and 30 different biomass types as input parameters. The results showed that the developed ANN models estimated the output parameters with high accuracy by means of R 2 greater than 0.999, MAPE < 0.053 and RMSE < 0.751 for training test and validation data sets." @default.
• W4200436566 created "2021-12-31" @default.
• W4200436566 creator A5016920181 @default.
• W4200436566 creator A5044661006 @default.
• W4200436566 creator A5065481059 @default.
• W4200436566 date "2022-03-01" @default.
• W4200436566 modified "2023-10-17" @default.
• W4200436566 title "Artificial Intelligence Approach in Gasification Integrated Solid Oxide Fuel Cell Cycle" @default.
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• W4200436566 doi "https://doi.org/10.1016/j.fuel.2021.122591" @default.
• W4200436566 hasPublicationYear "2022" @default.
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