Asset Details
Do you wish to reserve the book?
Application of artificial neural network model for the development of optimized complex medium for phenol degradation using Pseudomonas pictorum (NICM 2074)
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Application of artificial neural network model for the development of optimized complex medium for phenol degradation using Pseudomonas pictorum (NICM 2074)
Do you wish to request the book?
Application of artificial neural network model for the development of optimized complex medium for phenol degradation using Pseudomonas pictorum (NICM 2074)
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Application of artificial neural network model for the development of optimized complex medium for phenol degradation using Pseudomonas pictorum (NICM 2074)
2007
Overview
Biodegradation of phenol using Pseudomonas pictorum (NICM 2074) a potential biodegradant of phenol was investigated for its degrading potential under different operating conditions. The neural network input parameter set consisted of the same set of four levels of maltose (0.025, 0.05, 0.075 g/l), phosphate (3, 12.5, 22 g/l), pH (7, 8, 9) and temperature (30 degrees C, 32 degrees C, 34 degrees C) on phenol degradation was investigated and a Artificial Neural Network (ANN) model was developed to predict the extent of degradation. The learning, recall and generalization characteristic of neural networks was studied using phenol degradation system data. The efficiency of the model generated by the ANN, was tested and compared with the results obtained from an established second order polynomial multiple regression analysis (MRA). Further, the two models (ANN and MRA) were used to predict the percentage of degradation of phenol for blind test data. Performance of both the models were validated in the cases of training and test data, ANN was recommended based on the following higher coefficient of determination R (2); lower standard error of residuals and lower mean absolute percentage deviation.
Publisher
Springer,Springer Nature B.V
