A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND
| Autor: | Dimah ALabdeh, Abdulreza Karbassi, Rana Kasem, Roohollah Noori |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
lcsh:TN1-997
In situ Biochemical oxygen demand business.industry Computer science lcsh:QE1-996.5 Geology Geotechnical Engineering and Engineering Geology lcsh:Geology FFANN Dissolved oxygen BOD5 General Energy Software RBFANN Calibration General Earth and Planetary Sciences business Process engineering lcsh:Mining engineering. Metallurgy Water Science and Technology |
| Zdroj: | Rudarsko-geološko-naftni Zbornik, Vol 33, Iss 1, Pp 15-22 (2018) |
| ISSN: | 1849-0409 0353-4529 |
| DOI: | 10.17794/rgn.2018.1.3 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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