The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion
This work aims at optimizing the media constituents for citric acid production from oil palm empty fruit bunches (EFB) as renewable resource using artificial neural networks (ANN) approach. The bioconversion process was done through solid state bioconversion using Aspergillus niger. ANN model was bu...
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| Hoofdauteurs: | , , |
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| Formaat: | Journal Contribution |
| Taal: | English |
| Gepubliceerd in: |
2017
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| Onderwerpen: | |
| Online toegang: | http://agris.upm.edu.my:8080/dspace/handle/0/12798 |
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| Samenvatting: | This work aims at optimizing the media constituents for citric acid production from oil palm empty fruit bunches (EFB) as renewable resource using artificial neural networks (ANN) approach. The bioconversion process was done through solid state bioconversion using Aspergillus niger. ANN model was built using MATLAB software. A dataset consists of 20 runs from our previous work was used to develop ANN. The predictive and generalization ability of ANN and the results of RSM were compared. The determination coefficients (R2-value) for ANN and RSM models were 0.997 and 0.985, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. Validation process was done and the maximum citric acid production (147.74 g/kg-EFB) was achieved using the optimal solution from ANN which consists of 6.1% sucrose, 9.2% mineral solution and 15.0% inoculum. |
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