Salty Water Desalination Modeling Using Electrodialysis Process and Neural Network Technique

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Behroozi Amir Hossein; Zourmand Zahra; KASIRI NOROLLAH

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Nashrieh Shimi va Mohandesi Shimi Iran Year:2021 | Volume:39 | Issue:4 (98) Page(s): 185-190

Download Full-Text

Persian Verion

View:

229

Download:

Cites:

Information Journal Paper

Title

Salty Water Desalination Modeling Using Electrodialysis Process and Neural Network Technique

Author(s)

Behroozi Amir Hossein | Zourmand Zahra | KASIRI NOROLLAH | Issue Writer Certificate

Keywords

ElectrodialysisQ1

ModelingQ2

DesalinationQ1

Neural networkQ1

Abstract

In this study, a model is presented using the black box Neural network technique. In this model, the effects of four basic parameters, including temperature, voltage consumption, flow rate, and feed concentration on salt separation percentage from salty water, have been investigated. With the help of trial and error, the training method, transfer function, and the optimal number of neurons on each layer are selected to provide the best network performance. Multi-layer Neural network, backpropagation, and Levenberg-Marquardt algorithm are utilized as the optimum options. In this study, 135 data were used, allocating 60% of them (81 data) to network training, 20% (27 data) to training data evaluation, and the remaining 20% (27 data) to assess the network generalizability as test data. Finally, a comparison of the model results with independent laboratory data indicates that the optimal network arrangement is 4: 5: 8: 1, and the model with an error of less than 1% can predict the process behavior.

Cites

No record.

References

No record.

Cite

APA: Copy

Behroozi, Amir Hossein, Zourmand, Zahra, & KASIRI, NOROLLAH. (2021). Salty Water Desalination Modeling Using Electrodialysis Process and Neural Network Technique. NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN), 39(4 (98) ), 185-190. SID. https://sid.ir/paper/410813/en

Vancouver: Copy

Behroozi Amir Hossein, Zourmand Zahra, KASIRI NOROLLAH. Salty Water Desalination Modeling Using Electrodialysis Process and Neural Network Technique. NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN)[Internet]. 2021;39(4 (98) ):185-190. Available from: https://sid.ir/paper/410813/en

IEEE: Copy

Amir Hossein Behroozi, Zahra Zourmand, and NOROLLAH KASIRI, “Salty Water Desalination Modeling Using Electrodialysis Process and Neural Network Technique,” NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN), vol. 39, no. 4 (98) , pp. 185–190, 2021, [Online]. Available: https://sid.ir/paper/410813/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops