Artificial neural network model for the prediction of kinetic parameters of biomass pyrolysis from its constituents
| Autor: | Pornpote Piumsomboon, Sasithorn Sunphorka, Benjapon Chalermsinsuwan |
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| Rok vydání: | 2017 |
| Předmět: |
Order of reaction
Chemical substance Artificial neural network Chemistry 020209 energy General Chemical Engineering Organic Chemistry Energy Engineering and Power Technology Biomass 02 engineering and technology Kinetic energy Fuel Technology 020401 chemical engineering Chemical engineering Contour line 0202 electrical engineering electronic engineering information engineering Range (statistics) 0204 chemical engineering Biological system Pyrolysis |
| Zdroj: | Fuel. 193:142-158 |
| ISSN: | 0016-2361 |
| DOI: | 10.1016/j.fuel.2016.12.046 |
| Popis: | This study applied artificial neural networks (ANN) for constructing the correlation between biomass constituents and the kinetic parameters (activation energy ( E a ), pre-exponential factor ( k 0 ) and reaction order ( n )) of biomass pyrolysis. Three ANN models were developed, one for each of the three kinetic parameters. A total of 150 experimental thermogravimetric analyses from a diverse range of biomass compositions were used to develop and test the networks. The relationships between the main biomass components and the output parameters were non-linear and could potentially be predicted by the selected ANN models (R 2 > 0.9). Using a mean standard error limit of 0.001, the number of neurons in the hidden and the output layer and the model parameter weights and biases were optimized, with 20, 17 and 30 neurons, for log k 0 , log E a and log n , respectively. The generated contour plots revealed that cellulose required the highest k 0 , E a and n values, as well as the non-linearity and complexity of the system. |
| Databáze: | OpenAIRE |
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