Implementing artificial neural network models for real-time water colour forecasting in a water treatment plant

2004 ◽  
Vol 3 (Supplement 1) ◽  
pp. S15-S23 ◽  
Author(s):  
Qing J Zhang ◽  
Audrey A Cudrak ◽  
Riyaz Shariff ◽  
Stephen J Stanley
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Real Time ◽  
Treatment Plant ◽  
Network Models ◽  
Water Treatment Plant ◽  
Neural Network Models ◽  
Water Colour ◽  
Artificial Neural Network Models

Coagulant Dosage Determination in a Water Treatment Plant Using Dynamic Neural Network Models

2015 ◽  
Vol 14 (03) ◽  
pp. 1550013 ◽  
Author(s):  
Leonaldo Silva Gomes ◽  
Francisco Alexandre A. Souza ◽  
Ricardo Silva Thé Pontes ◽  
Tobias R. Fernandes Neto ◽  
Rui Alexandre M. Araújo
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Treatment Plant ◽  
Network Models ◽  
Water Treatment Plant ◽  
Raw Water ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
Input Variables

A common step in most of water treatment plants is the chemical coagulation. The chemical coagulation is the process of destabilizing the colloidal particles suspended in raw water by the addition of coagulants. Generally, the determination of the quantity of coagulant to be added to water is made manually by jar tests. However, the manual control has slow response to changes of raw water and it requires intensive laboratory analysis. To reduce the manual effort and to improve the response to change in raw water quality, this work proposes the determination of the coagulant dosage using dynamic neural network modeling using the available sensors as input of the model. The case of study is a large scale water treatment plant in Ceará, Brazil, where the kinds of coagulants added to water are the aluminum sulphate (AS) and poly aluminum chloride (PAC). Several dynamic neural network models with different combinations of the input variables have been evaluated. The best solution found is composed by a nonlinear autoregressive with exogenous input (NARX) model having three input variables, the pH in raw and coagulated water, and the turbidity in the coagulated water, with coefficient of determination of R2 = 0.95 and R2 = 0.91 for the AS and PAC dosage prediction, respectively.

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

Sign in / Sign up

Export Citation Format

Share Document