| dc.creator |
Yigit, Nevzat Özgü |
|
| dc.creator |
Civelekoglu, Gökhan |
|
| dc.creator |
DIAMADOPOULOS, E. |
|
| dc.creator |
Kitis, Mehmet |
|
| dc.date |
2006-12-31T22:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:24:44Z |
|
| dc.date.available |
2020-10-06T09:24:44Z |
|
| dc.identifier |
0ddda2b3-fe81-4b06-a9c7-c18d60c0476e |
|
| dc.identifier |
10.1080/01919510701549327 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/0ddda2b3-fe81-4b06-a9c7-c18d60c0476e/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/53244 |
|
| dc.description |
The main objective of this study was to develop simple models for the prediction of bromate formation in ozonated bottled waters, using rapidly and practically measurable raw water quality and/or operational parameters. A total of 6 multi-linear regression ( MLR) with or without principal component analysis (PCA) and 2 artificial neural networks ( ANN) models with multilayer perceptron architecture were developed for the prediction of bromate formation. PCA was employed to better identify relations between variables and reduce the number of variables. Experimental data used in modeling was provided from the ozonation of samples from 5 groundwater sources at various applied ozone dose and contact time. MLR models#1 and #2 well-predicted bromate formation although correlations ( i.e., the signs of regression constants) among pH ( as input variable) and bromate concentrations did not agree with the chemistry. MLR model#6, containing practical input parameters that are measured on-line in full-scale treatment plants, adequately predicted bromate formation and agreed with the chemistry, although fewer input parameters were used compared to MLR#1 and #2. Although both of the ANN models exhibited high regression coefficients ( R 2) ( 0.97 for both) ANN#1 was found to provide better prediction of bromate formation based on mean square error (MSE) values. However, since ANN#2 included easily measurable input parameters it may be practically used by water companies employing ozonation. Results overall indicated that ANN models have stronger prediction capabilities of bromate formation than MLR models. ANN modeling appears to be a strong tool in situations where the relations between variables are nonlinear, interactive and complex, as in the bromate formation by ozonation. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Prediction of bromate formation using multi-linear regression and artificial neural networks |
|
| dc.type |
info:eu-repo/semantics/article |
|