Performance prediction of fluidised bed gasification of biomass using experimental data-based simulation models

Sreejith, C. C.; Muraleedharan, C.; Arun, P.

doi:10.1007/s13399-013-0083-5

Cited by 32 publications

(15 citation statements)

References 52 publications

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“…352,387 A model was constructed to derive exit gas characteristics and heat content along with the temperature profile in a FBG. 387 Predictions were very close to the experimental data. At a S/B of 2.53, the highest H 2 yield was reported to be 29.1% in experiments.…”

Section: Artificial Neural Network Modellingmentioning

confidence: 99%

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An overview of advances in biomass gasification

Sikarwar

Zhao

Clough

et al. 2016

Energy Environ. Sci.

967

406

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Biomass gasification is a widely used thermochemical process for obtaining products with more value and potential applications than the raw material itself. Cutting-edge, innovative and economical gasification techniques with high efficiencies are a prerequisite for the development of this technology. This paper delivers an assessment on the fundamentals such as feedstock types, the impact of different operating parameters, tar formation and cracking, and modelling approaches for biomass gasification. Furthermore, the authors comparatively discuss various conventional mechanisms for gasification as well as recent advances in biomass gasification. Unique gasifiers along with multi-generation strategies are discussed as a means to promote this technology into alternative applications, which require higher flexibility and greater efficiency. A strategy to improve the feasibility and sustainability of biomass gasification is via technological advancement and the minimization of socio-environmental effects. This paper sheds light on diverse areas of biomass gasification as a potentially sustainable and environmentally friendly technology

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Section: Artificial Neural Network Modellingmentioning

confidence: 99%

“…Very few researchers have modelled biomass gasification using the ANN approach. 352,387 A model was constructed to derive exit gas characteristics and heat content along with the temperature profile in a FBG. 387 Predictions were very close to the experimental data.…”

Section: Artificial Neural Network Modellingmentioning

confidence: 99%

An overview of advances in biomass gasification

Sikarwar

Zhao

Clough

et al. 2016

Energy Environ. Sci.

967

406

View full text Add to dashboard Cite

show abstract

“…Two AI studies include reactor information. One used the distance from the bottom of a reactor (Sreejith et al, 2013) as an input. The other quantified the impacts of biomass composition in different reactors by establishing FNN models for circulating fluidized bed gasifier (CFB) and bubbling fluidized bed (BFB; Puig‐Arnavat et al, 2013).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

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“…Al igual que el modelo anterior se enfoca en realizar un análisis del gas de síntesis producto del proceso y predecir su rendimiento en función de su composición en un volumen de control en un tiempo dado (Janajreh et al, 2013). En algunos trabajos estos modelos se validan con ayuda de resultados experimentales (Sreejith et al, 2013). Con ayuda de este tipo de modelos se predicen los perfiles tanto de composición del gas como del perfil de temperatura dentro del reactor en condiciones de operación dadas.…”

Section: Introductionunclassified

Modelado por redes neuronales artificiales de los indicadores de desempeño de operación en instalaciones de gasificación termoquímica downdraft

Gualotuña

Mera

Ruiz

2019

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<p>En el trabajo se presentan resultados investigativos obtenidos por los autores en el modelado de indicadores de desempeño de la operación de instalaciones de gasificación de la biomasa con ayuda de redes neuronales artificiales, a partir de la clasificación de la información derivada del análisis sistémico de su operación. Se realizó el estudio bibliográfico de los trabajos de investigaciones previos relacionados. A partir de un plan experimental 3N con tres réplicas se elaboran, utilizando las técnicas de las redes neuronales, cuatro modelos correspondientes a respectivos indicadores de desempeño, los que se comparan con los obtenidos por modelos de regresión no lineal. Como biomasa se utiliza el Oryza sativa.</p>

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Performance prediction of fluidised bed gasification of biomass using experimental data-based simulation models

Cited by 32 publications

References 52 publications

An overview of advances in biomass gasification

An overview of advances in biomass gasification

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Modelado por redes neuronales artificiales de los indicadores de desempeño de operación en instalaciones de gasificación termoquímica downdraft

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