Predicting the Quality of Treated Water from an Activated Carbon Based Water Purifier Using Artificial Neural Network

2019 ◽  
Vol 16 (3) ◽  
pp. 125-128
Author(s):  
Ritabrata Roy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Activated Carbon ◽  
Treated Water ◽  
Artificial Neural ◽  
Carbon Based

scholarly journals Application of the Artificial Neural Network to monitor the quality of treated water

2013 ◽  
Vol 3 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Vahid Nourani ◽  
Tohid Rezapour Khanghah ◽  
Milad Sayyadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Treatment Plant ◽  
Quality Data ◽  
Data Sets ◽  
Water Quality Data ◽  
Treated Water ◽  
Engineering Sciences ◽  
Artificial Neural

Due to importance of the quality of treated water as a drastic parameter in peoples life and engineering problems, numerous experimental and semi-experimental models were recently used by water and environmental engineers in order to estimate the quality of water. Between the used models, Artificial Neural Network (ANN) approach as an advantageous black box model was showed great authority in engineering sciences in general and in water engineering in particular. In this study, an ANN-based method was utilized to model the quality of the potable water parameters. To evaluate the model, the water quality data sets of Zarrineh Rood water treatment plant before and after treatment were used. After the statistical analysis on the recorded daily data sets, they were divided into calibration and verification sub-sets. In this paper the measured heat, PH, opacity, total hardness, and the level of calcium before the treatment process were considered as input variables of the model and the quantity of Total Dissolved Solids (TDS) and Electrical Conductivity (EC) after treatment were considered as output neurons of ANN. To have better interpretation about the model efficiency, the outcomes were compared with other classical and practical models and the results proved high merit of ANN in predicting the parameters of treated water.  

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

Artificial neural network enabled capacitance prediction for carbon-based supercapacitors

2018 ◽  
Vol 233 ◽  
pp. 294-297 ◽  
Author(s):  
Shan Zhu ◽  
Jiajun Li ◽  
Liying Ma ◽  
Chunnian He ◽  
Enzuo Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Carbon Based

scholarly journals Quality Prediction Modeling of Palm Oil Refining Plant in Malaysia Using Artificial Neural Network Models

2018 ◽  
Vol 7 (3.26) ◽  
pp. 19
Author(s):  
Nurul Sulaiha Sulaiman ◽  
Khairiyah Mohd-Yusof ◽  
Asngari Mohd-Saion
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Palm Oil ◽  
Oil Refinery ◽  
Oil Refining ◽  
Great Loss ◽  
Refining Plant ◽  
Artificial Neural ◽  
Refined Palm Oil

Malaysia is currently one of the biggest producers and exporters of palm oil and palm oil products. In the growth of palm oil industry in Malaysia, quality of the refined oil is a major concern where off-specification products will be rejected thus causing a great loss in profit. In this paper, predictive modeling of refined palm oil quality in one palm oil refining plant in Malaysia is proposed for online quality monitoring purposes. The color of the crude oil, Free Fatty acid (FFA) content, bleaching earth dosage, citric acid dosage, activated carbon dosage, deodorizer pressure and deodorizer temperature were studied in this paper. The industrial palm oil refinery data were used as input and output to the Artificial Neural Network (ANN) model. Various trials were examined for training all three ANN models using number of nodes in the hidden layer varying from 10 to 25. All three models were trained and tested reasonably well to predict FFA content, red and yellow color quality of the refined palm oil efficiently with small error. Therefore, the models can be further implemented in palm oil refinery plant as online prediction system.  

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