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Rudarsko-geološko-naftni zbornik, Vol. 33 No. 1, 2018.

Prethodno priopćenje
https://doi.org/10.17794/rgn.2018.1.3

A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND

Rana Kasem ; Graduate Faculty of Environment, University of Tehran, Tehran, Iran, PhD Candidate in Environmental Engineering-Water Resources
Dimah ALabdeh ; Graduate Faculty of Environment, University of Tehran, Tehran, Iran, PhD Candidate in Environmental Engineering-Water Resources
Roohollah Noori ; Graduate Faculty of Environment, University of Tehran, Tehran, Iran, Assistant Professor of Environmental Engineering
Abdulreza Karbassi ; Graduate Faculty of Environment, University of Tehran, Tehran, Iran, Associate Professor of Environmental Engineering

Puni tekst: engleski, pdf (911 KB) str. 15-22 preuzimanja: 100* citiraj
APA 6th Edition
Kasem, R., ALabdeh, D., Noori, R. i Karbassi, A. (2018). A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND. Rudarsko-geološko-naftni zbornik, 33 (1), 15-22. https://doi.org/10.17794/rgn.2018.1.3
MLA 8th Edition
Kasem, Rana, et al. "A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND." Rudarsko-geološko-naftni zbornik, vol. 33, br. 1, 2018, str. 15-22. https://doi.org/10.17794/rgn.2018.1.3. Citirano 26.03.2019.
Chicago 17th Edition
Kasem, Rana, Dimah ALabdeh, Roohollah Noori i Abdulreza Karbassi. "A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND." Rudarsko-geološko-naftni zbornik 33, br. 1 (2018): 15-22. https://doi.org/10.17794/rgn.2018.1.3
Harvard
Kasem, R., et al. (2018). 'A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND', Rudarsko-geološko-naftni zbornik, 33(1), str. 15-22. doi: https://doi.org/10.17794/rgn.2018.1.3
Vancouver
Kasem R, ALabdeh D, Noori R, Karbassi A. A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND. Rudarsko-geološko-naftni zbornik [Internet]. 2018 [pristupljeno 26.03.2019.];33(1):15-22. doi: https://doi.org/10.17794/rgn.2018.1.3
IEEE
R. Kasem, D. ALabdeh, R. Noori i A. Karbassi, "A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND", Rudarsko-geološko-naftni zbornik, vol.33, br. 1, str. 15-22, 2018. [Online]. doi: https://doi.org/10.17794/rgn.2018.1.3
Puni tekst: hrvatski, pdf (53 KB) str. 23-23 preuzimanja: 33* citiraj
APA 6th Edition
Kasem, R., ALabdeh, D., Noori, R. i Karbassi, A. (2018). PROGRAMSKI SENZORI ZA PETODNEVNO TERENSKO OPAŽANJE BIOKEMIJSKE POTROŠNJE KISIKA. Rudarsko-geološko-naftni zbornik, 33 (1), 23-23. https://doi.org/10.17794/rgn.2018.1.3
MLA 8th Edition
Kasem, Rana, et al. "PROGRAMSKI SENZORI ZA PETODNEVNO TERENSKO OPAŽANJE BIOKEMIJSKE POTROŠNJE KISIKA." Rudarsko-geološko-naftni zbornik, vol. 33, br. 1, 2018, str. 23-23. https://doi.org/10.17794/rgn.2018.1.3. Citirano 26.03.2019.
Chicago 17th Edition
Kasem, Rana, Dimah ALabdeh, Roohollah Noori i Abdulreza Karbassi. "PROGRAMSKI SENZORI ZA PETODNEVNO TERENSKO OPAŽANJE BIOKEMIJSKE POTROŠNJE KISIKA." Rudarsko-geološko-naftni zbornik 33, br. 1 (2018): 23-23. https://doi.org/10.17794/rgn.2018.1.3
Harvard
Kasem, R., et al. (2018). 'PROGRAMSKI SENZORI ZA PETODNEVNO TERENSKO OPAŽANJE BIOKEMIJSKE POTROŠNJE KISIKA', Rudarsko-geološko-naftni zbornik, 33(1), str. 23-23. doi: https://doi.org/10.17794/rgn.2018.1.3
Vancouver
Kasem R, ALabdeh D, Noori R, Karbassi A. PROGRAMSKI SENZORI ZA PETODNEVNO TERENSKO OPAŽANJE BIOKEMIJSKE POTROŠNJE KISIKA. Rudarsko-geološko-naftni zbornik [Internet]. 2018 [pristupljeno 26.03.2019.];33(1):23-23. doi: https://doi.org/10.17794/rgn.2018.1.3
IEEE
R. Kasem, D. ALabdeh, R. Noori i A. Karbassi, "PROGRAMSKI SENZORI ZA PETODNEVNO TERENSKO OPAŽANJE BIOKEMIJSKE POTROŠNJE KISIKA", Rudarsko-geološko-naftni zbornik, vol.33, br. 1, str. 23-23, 2018. [Online]. doi: https://doi.org/10.17794/rgn.2018.1.3

Sažetak
Due to the time-consuming procedure for determining the 5-day biochemical oxygen demand (BOD5), the present study developed two software sensors based on artificial intelligence techniques to estimate this indicator instantaneously. For this purpose, feed-forward and radial basis function neural networks (FFANN and RBFANN, respectively) were tuned to estimate the maximum values of BOD5 (BOD5(max)) as a function of average, maximum and minimum dissolved oxygen in the Sefidrood River. Also, Levenberg-Marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG) algorithms were used to optimize the FFANN parameters. The results demonstrated that the performance of LM algorithm in tuning the FFANN was better than others, in verification step. Besides, the performance of both FFANN and RBFANN models for prediction of the BOD5(max) was approximately the same.

Ključne riječi
FFANN; RBFANN; Dissolved oxygen; Calibration; BOD5

Hrčak ID: 192501

URI
https://hrcak.srce.hr/192501

[hrvatski]

Posjeta: 215 *