Using Artificial Neural Network to modeling of valuable metals bioleaching from fuel oil fly ash using Acidithiobacillus ferrooxidans

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.

RASTEGAR SEYED OMID; Beigzadeh Reza

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

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF MINING ENGINEERING (IRJME) Year:2020 | Volume:15 | Issue:47 Page(s): 68-75

Download Full-Text

Persian Verion

View:

153

Download:

Cites:

Information Journal Paper

Title

Using Artificial Neural Network to modeling of valuable metals bioleaching from fuel oil fly ash using Acidithiobacillus ferrooxidans

Author(s)

RASTEGAR SEYED OMID | Beigzadeh Reza | Issue Writer Certificate

Keywords

ModelingQ2

BioleachingQ1

Artificial neural networksQ1

fuel oil ashQ1

Abstract

In this study, the Modeling of vanadium, nickel and copper Bioleaching from fuel oil ash ash using Artificial neural networks was investigated. In the obtained models, the extraction percentage of metals was investigated as a function of factors such as initial pH (from 1-2. 5), initial Fe2+ concentration (from 0 – 9 g/l), initial bacterial inoculation (from 1 – 10%) and process time (from 0-15 day). Three neural network models were presented to estimate the extraction percentage of metals. The propagation error method and Levenberg– Marquardt algorithm were used for training. Furthermore, trial and error method was used to determine the optimal number of neurons. One quarter of the data were used to evaluate the model and were not used for training process. The Mean Relative Errors (MRE) were obtained 5. 35%, 3. 07% and 2. 82% for V, Ni and Cu, respectively. Also the higher 0. 99 of R2 indicates the validity of the obtained models.

Cites

No record.

References

No record.

Cite

APA: Copy

RASTEGAR, SEYED OMID, & Beigzadeh, Reza. (2020). Using Artificial Neural Network to modeling of valuable metals bioleaching from fuel oil fly ash using Acidithiobacillus ferrooxidans. IRANIAN JOURNAL OF MINING ENGINEERING (IRJME), 15(47 ), 68-75. SID. https://sid.ir/paper/414143/en

Vancouver: Copy

RASTEGAR SEYED OMID, Beigzadeh Reza. Using Artificial Neural Network to modeling of valuable metals bioleaching from fuel oil fly ash using Acidithiobacillus ferrooxidans. IRANIAN JOURNAL OF MINING ENGINEERING (IRJME)[Internet]. 2020;15(47 ):68-75. Available from: https://sid.ir/paper/414143/en

IEEE: Copy

SEYED OMID RASTEGAR, and Reza Beigzadeh, “Using Artificial Neural Network to modeling of valuable metals bioleaching from fuel oil fly ash using Acidithiobacillus ferrooxidans,” IRANIAN JOURNAL OF MINING ENGINEERING (IRJME), vol. 15, no. 47 , pp. 68–75, 2020, [Online]. Available: https://sid.ir/paper/414143/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops