Artificial Neural Network Modelling for Biogas Production in Biodigesters
The use of the biodigestion is considered promising for the energetic valorization of agriculture biomass such as swine farm sewage and lignocelulosic residues. The understanding of biodigesters operation and the control of their main operational variables are of great importance to improve the perf...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIDIC Servizi S.r.l.
2019-05-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/9770 |
| _version_ | 1818656474250870784 |
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| author | Artur Rego Sibele Leite Brenno Leite Alexandre V. Grillo Brunno F. Santos |
| author_facet | Artur Rego Sibele Leite Brenno Leite Alexandre V. Grillo Brunno F. Santos |
| author_sort | Artur Rego |
| collection | DOAJ |
| description | The use of the biodigestion is considered promising for the energetic valorization of agriculture biomass such as swine farm sewage and lignocelulosic residues. The understanding of biodigesters operation and the control of their main operational variables are of great importance to improve the performance of anaerobic digestion process in order to increase biogas production. In this context, mathematical modelling can be used as a tool to increase process efficiency. This work presents the development of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) to predict volume of biogas. The variables from process were temperature (ºC), pH, FOS/TAC ratio and type of biodigesters. A database was constructed with the information of the experiments, dividing them into groups of training (67 %) and test (33 %). The models were obtained using MATLAB R2018b toolbox. In the developed neural models, the data obtained from process were used as neurons in the input layer and the volume of biogas was used as the only neuron in the output layer. The performance of the neural models was evaluated by determination coefficient (R²) and error index (RMSE). The model developed from ANN and ANFIS modelling were satisfactory, showing the R² value giving the system’s complexity. In addition, the RMSE values of both models were close to each other, showing agreement of the methods used. |
| first_indexed | 2024-12-17T03:26:09Z |
| format | Article |
| id | doaj.art-4b1f1882560143719c8d976afff8c50b |
| institution | Directory Open Access Journal |
| issn | 2283-9216 |
| language | English |
| last_indexed | 2024-12-17T03:26:09Z |
| publishDate | 2019-05-01 |
| publisher | AIDIC Servizi S.r.l. |
| record_format | Article |
| series | Chemical Engineering Transactions |
| spelling | doaj.art-4b1f1882560143719c8d976afff8c50b2022-12-21T22:05:23ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162019-05-017410.3303/CET1974005Artificial Neural Network Modelling for Biogas Production in BiodigestersArtur RegoSibele LeiteBrenno LeiteAlexandre V. GrilloBrunno F. SantosThe use of the biodigestion is considered promising for the energetic valorization of agriculture biomass such as swine farm sewage and lignocelulosic residues. The understanding of biodigesters operation and the control of their main operational variables are of great importance to improve the performance of anaerobic digestion process in order to increase biogas production. In this context, mathematical modelling can be used as a tool to increase process efficiency. This work presents the development of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) to predict volume of biogas. The variables from process were temperature (ºC), pH, FOS/TAC ratio and type of biodigesters. A database was constructed with the information of the experiments, dividing them into groups of training (67 %) and test (33 %). The models were obtained using MATLAB R2018b toolbox. In the developed neural models, the data obtained from process were used as neurons in the input layer and the volume of biogas was used as the only neuron in the output layer. The performance of the neural models was evaluated by determination coefficient (R²) and error index (RMSE). The model developed from ANN and ANFIS modelling were satisfactory, showing the R² value giving the system’s complexity. In addition, the RMSE values of both models were close to each other, showing agreement of the methods used.https://www.cetjournal.it/index.php/cet/article/view/9770 |
| spellingShingle | Artur Rego Sibele Leite Brenno Leite Alexandre V. Grillo Brunno F. Santos Artificial Neural Network Modelling for Biogas Production in Biodigesters Chemical Engineering Transactions |
| title | Artificial Neural Network Modelling for Biogas Production in Biodigesters |
| title_full | Artificial Neural Network Modelling for Biogas Production in Biodigesters |
| title_fullStr | Artificial Neural Network Modelling for Biogas Production in Biodigesters |
| title_full_unstemmed | Artificial Neural Network Modelling for Biogas Production in Biodigesters |
| title_short | Artificial Neural Network Modelling for Biogas Production in Biodigesters |
| title_sort | artificial neural network modelling for biogas production in biodigesters |
| url | https://www.cetjournal.it/index.php/cet/article/view/9770 |
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