Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach
| Autor: | Iman Golpour, Daniel Serrano, S. Sánchez-Delgado |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Yield (engineering) 020209 energy General Chemical Engineering Energy Engineering and Power Technology 02 engineering and technology Network topology 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering Gas composition 0204 chemical engineering Process engineering Bubbling fluidized bed business.industry Bed material Organic Chemistry Process (computing) Backpropagation Fuel Technology Cascade Energías Renovables Environmental science business Gasification |
| Zdroj: | e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname |
| DOI: | 10.1016/j.fuel.2020.117021 |
| Popis: | The effect of different bed materials was included a as new input into an artificial neural network model to predict the gas composition (CO2, CO, CH4 and H2) and gas yield of a biomass gasification process in a bubbling fluidized bed. Feed and cascade forward back propagation networks with one and two hidden layers and with Levenberg-Marquardt and Bayesian Regulation learning algorithms were employed for the training of the networks. A high number of network topologies were simulated to determine the best configuration. It was observed that the developed models are able to predict the CO2, CO, CH4, H2 and gas yield with good accuracy (R2 > 0.94 and MSE |
| Databáze: | OpenAIRE |
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