A Multilayer Full Feed Forward Neural Network for Modelling and Optimizing Nutrient Medium Composition for Oxalic Acid Production from Sweet Potato Peels

Amenaghawon N. A., Agbedor M.

Abstract


This study was aimed at optimizing the composition of nutrient medium for optimum production of oxalic acid from sweet potato peel using Aspergillus niger. The fermentation experiments were designed using a three-variable Box-Behnken design (BBD). The effect of the nutrient medium components on oxalic acid yield was modelled using a multilayer full feed-forward artificial neural network (MFFF-ANN) trained with the Levenberg-Marquardt algorithm. The nutrient medium components considered and their levels of variation were KH2PO4 (0.5-1 g/L), MgSO4 (0-0.5 g/L) and NaNO3 (1-2 g/L). Assessment of the predictive capacity of the ANN showed that it was able to replicate the experimental observations with a high level of precision (R2 = 0.9996, RMSE=0.1845, MAE = 0.084, AAD = 0.030%). Analysis of the results showed that oxalic acid concentration was favoured by intermediate levels of the nutrient medium components. To maximize the yield of oxalic acid, the process input variables investigated were optimized by coupling genetic algorithm (GA) with the MFFF-ANN. The best combination of the estimated input variables (KH2PO4 = 0.770 g/l, MgSO4 = 0.194 g/l and NaNO3 = 1.999 g/l) gave the highest oxalic acid yield of 29.023 g/l. This study has demonstrated the applicability of a machine learning tool like ANN for modelling and optimising a biochemical process like oxalic acid production.



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References


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