Apolinar Picado - Artificial Neural Network Modelling of Biogas

Conference paper

Geniel Talavera, Diego Rafael Magero Elihimas, Apolinar Picado, Claudia Jéssica da Silva Cavalcanti, Mauro Antonio Da Silva Sá Ravagnani
24th Brazilian Congress of Chemical Engineering (COBEQ 2023), Karen Pontes, Associação Brasileira de Engenharia Química (ABEQ), Salvador, Brazil, 2023 Oct 1, Paper No. 170821

DOI: 10.5281/zenodo.8404246

Cite

APA Click to copy
Talavera, G., Elihimas, D. R. M., Picado, A., da Silva Cavalcanti, C. J., & Ravagnani, M. A. D. S. S. (2023). Artificial Neural Network Modelling of Biogas Production from Vinasse. In K. Pontes (Ed.) (pp. Paper No. 170821). Salvador, Brazil: Associação Brasileira de Engenharia Química (ABEQ). https://doi.org/10.5281/zenodo.8404246

Chicago/Turabian Click to copy
Talavera, Geniel, Diego Rafael Magero Elihimas, Apolinar Picado, Claudia Jéssica da Silva Cavalcanti, and Mauro Antonio Da Silva Sá Ravagnani. “Artificial Neural Network Modelling of Biogas Production from Vinasse.” In , edited by Karen Pontes, Paper No. 170821. 24th Brazilian Congress of Chemical Engineering (COBEQ 2023). Salvador, Brazil: Associação Brasileira de Engenharia Química (ABEQ), 2023.

MLA Click to copy
Talavera, Geniel, et al. Artificial Neural Network Modelling of Biogas Production from Vinasse. Edited by Karen Pontes, Associação Brasileira de Engenharia Química (ABEQ), 2023, pp. Paper No. 170821, doi:10.5281/zenodo.8404246.

BibTeX Click to copy

@inproceedings{geniel2023a,
  title = {Artificial Neural Network Modelling of Biogas Production from Vinasse},
  year = {2023},
  month = oct,
  day = {1},
  address = {Salvador, Brazil},
  pages = {Paper No. 170821},
  publisher = {Associação Brasileira de Engenharia Química (ABEQ)},
  series = {24th Brazilian Congress of Chemical Engineering (COBEQ 2023)},
  doi = {10.5281/zenodo.8404246},
  author = {Talavera, Geniel and Elihimas, Diego Rafael Magero and Picado, Apolinar and da Silva Cavalcanti, Claudia Jéssica and Ravagnani, Mauro Antonio Da Silva Sá},
  editor = {Pontes, Karen},
  month_numeric = {10}
}

Artificial neural network (ANN) models to predict biogas production and chemical oxygen demand (COD) removal during the digesting of vinasse were developed. Experimental data from the literature were used for developing feedforward neural networks. The data were grouped into three subsets: training, test, and validation. Twenty different ANN topologies were evaluated by varying the number of neurons, the activation function, and the training algorithm. The best ANN model (R2 = 0.9990) includes using 15 neurons, tansing as a transfer function, and trainlm as a training algorithm. The results indicate that the ANN model can be used for forecasting and optimising biogas production from vinasse.