Modeling and Optimization of Phenylacetyylcarbinol Synthesis via Benzaldehyde: A Case of Artificial Neural Network vs. Response Surface Methodology
In this study, a comparative optimization of biotransformation of benzaldehyde to L-Phenylacetylcarbinol via free cells of Saccharomyces cerevisae using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) was done. A polynomial regression model was developed and RSM optimum process was determined. In developing ANN model, performance of ANN is heavily influenced by its network structure, five-level-five-factors design was applied, which generated 50 experimental runs from CCD design of RSM. The inputs for the ANN were cell mass (wet. wt), incubation duration (min), concentration of acetaldehyde (mg/100 ml), concentration of benzaldehyde (mg/100 ml), and β-cyclodextrin level (%): X5. The learning algorithms used was QP with MNFF and the transfer function was Tanh. The RMSE, R2, AAD and predicted values were used to compare the performance of the RSM and ANN models. The extrapolative fitness of ANN model was found to be higher than RSM extrapolative fitness model. Thus, it can be concluded that even though RSM is mostly used method for experimental optimization, the ANN methodology present a better alternative.
Volume (Year): 1 (2014)
Issue (Month): 2 ()
|Contact details of provider:|| Postal: S22 Sin Ming Lane #06-76 Midview City Singapore 573969|
Web page: http://www.conscientiabeam.com/journal/81
When requesting a correction, please mention this item's handle: RePEc:pkp:enerev:2014:p:56-75. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Editorial Office)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.