Volf 2020a - Revision history

JSanchez at 14:14, 21 September 2020

2020-09-21T14:14:03Z

Scipediacontent: Scipediacontent moved page Draft Content 494437638 to Volf 2020a

2020-09-14T11:41:15Z

Scipediacontent moved page Draft Content 494437638 to Volf 2020a

Scipediacontent: Created page with "== Abstract == Wastewater treatment plants (WWTP) are complex and dynamic systems whose management and sustainability can be improved by using different modelling and predict..."

2020-09-14T11:23:59Z

Created page with "== Abstract == Wastewater treatment plants (WWTP) are complex and dynamic systems whose management and sustainability can be improved by using different modelling and predict..."

New page

== Abstract ==

Wastewater treatment plants (WWTP) are complex and dynamic systems whose management and sustainability can be improved by using different modelling and prediction approaches of their work. A machine learning tool for development of model trees was used in this paper in order to develop a model for chemical oxygen demand (COD) in the wastewater effluent from the WWTP with activated sludge to increase its sustainability and helps in its management purposes.Measured data, both in influent and effluent of the WWTP were used for modelling. For the COD model, machine learning tool Weka and algorithm for development of model trees M5P were used.Obtained model has a high descriptive power and correlation coefficient and thus can be used for prediction and modelling purposes, which can help in management and sustainability of the WWTP.Also, the purpose of this paper is to show the benefits of using machine learning tools for developing WWTP models.

== Full document ==
<pdf>Media:Draft_Content_494437638p571.pdf</pdf>

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 == Abstract ==
-Wastewater treatment plants (WWTP) are complex and dynamic systems whose management and sustainability can be improved by using different modelling and prediction approaches of their work. A machine learning tool for development of model trees was used in this paper in order to develop a model for chemical oxygen demand (COD) in the wastewater effluent from the WWTP with activated sludge to increase its sustainability and helps in its management purposes.Measured data, both in influent and effluent of the WWTP were used for modelling. For the COD model, machine learning tool Weka and algorithm for development of model trees M5P were used.Obtained model has a high descriptive power and correlation coefficient and thus can be used for prediction and modelling purposes, which can help in management and sustainability of the WWTP.Also, the purpose of this paper is to show the benefits of using machine learning tools for developing WWTP models.
+Wastewater treatment plants (WWTP) are complex and dynamic systems whose management and sustainability can be improved by using different modelling and prediction approaches of their work. A machine learning tool for development of model trees was used in this paper in order to develop a model for chemical oxygen demand (COD) in the wastewater effluent from the WWTP with activated sludge to increase its sustainability and helps in its management purposes.Measured data, both in influent and effluent of the WWTP were used for modelling. For the COD model, machine learning tool Weka and algorithm for development of model trees M5P were used.Obtained model has a high descriptive power and correlation coefficient and thus can be used for prediction and modelling purposes, which can help in management and sustainability of the WWTP. Also, the purpose of this paper is to show the benefits of using machine learning tools for developing WWTP models.
 == Full document ==
 <pdf>Media:Draft_Content_494437638p571.pdf</pdf>

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