ANN-based predictive model for performance evaluation of paper and pulp effluent treatment plant
by R. Saraswathi; M.K. Saseetharan; S. Suja
International Journal of Computer Applications in Technology (IJCAT), Vol. 45, No. 4, 2012

Abstract: Neural networks (NNs) have been widely used for complex processes that are poorly described by first principle models, such as wastewater biological treatment systems. In this paper, we propose an Artificial Neural Network (ANN) based predictive model for assessing the performance of paper and pulp effluent treatment plants. Mathematical models were created for the thickener area of the clarifier by correlating process control parameters such as mean cell residence time (θc), initial suspended solid concentration (Co), underflow concentration (Cu) and recycling ratio (R). For any values of Cu, Co, R and θc, area of the secondary clarifier can be determined using the model developed based on ANN. The predicted models give a rational approach to the design of secondary clarifier. The developed models prove consistently well in the face of varying accuracy and size of input data phase.

Online publication date: Thu, 20-Dec-2012

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email