Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters
A predictive model based on artificial neural networks (ANNs) for modeling primary settling tanks' (PSTs) behavior in wastewater treatment plants was developed in this study. Two separate ANNs were built using input data, raw wastewater characteristics, and operating conditions. The output data...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-06-01
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| Series: | Water Science and Technology |
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| Online Access: | http://wst.iwaponline.com/content/85/12/3451 |
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| author | Carlos Veloz Esteban Pazmiño-Arias Andrea M. Gallardo Jhon Montenegro Alicia Sommer-Márquez Marvin Ricaurte |
| author_facet | Carlos Veloz Esteban Pazmiño-Arias Andrea M. Gallardo Jhon Montenegro Alicia Sommer-Márquez Marvin Ricaurte |
| author_sort | Carlos Veloz |
| collection | DOAJ |
| description | A predictive model based on artificial neural networks (ANNs) for modeling primary settling tanks' (PSTs) behavior in wastewater treatment plants was developed in this study. Two separate ANNs were built using input data, raw wastewater characteristics, and operating conditions. The output data from the ANNs consisted of the total suspended solids (TSS) concentration and chemical oxygen demand (COD) as predictions of PSTs’ typical effluent parameters. Data from a large-scale wastewater treatment plant was used to illustrate the applicability of the predictive model proposal. The ANNs model showed a high prediction accuracy during the training phase. Comparisons with available empirical and statistical models suggested that the ANNs model provides accurate estimations. Also, the ANNs were tested using new experimental data to verify their reproducibility under actual operating conditions. The predicted values were calculated with satisfactory results, having an average absolute deviation of <20%. The model could be adapted to any large-scale wastewater plant to monitor and control the operation of primary settling tanks, taking advantage of the ANNs' learning capacity. HIGHLIGHTS
A predictive model of the PSTs behavior using ANNs was developed.;
The proposed model accurately predicts the TSS concentration and COD in the effluent.;
The wastewater treatment plant (WWTP) in Ibarra, Ecuador, was considered a case study to show the applicability and reproducibility of the model.;
A reliable predictive model would improve the monitoring of WWTPs.; |
| first_indexed | 2024-04-13T11:03:25Z |
| format | Article |
| id | doaj.art-8260f33287064be3b4b7455010f99165 |
| institution | Directory Open Access Journal |
| issn | 0273-1223 1996-9732 |
| language | English |
| last_indexed | 2024-04-13T11:03:25Z |
| publishDate | 2022-06-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Science and Technology |
| spelling | doaj.art-8260f33287064be3b4b7455010f991652022-12-22T02:49:21ZengIWA PublishingWater Science and Technology0273-12231996-97322022-06-0185123451346410.2166/wst.2022.186186Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parametersCarlos Veloz0Esteban Pazmiño-Arias1Andrea M. Gallardo2Jhon Montenegro3Alicia Sommer-Márquez4Marvin Ricaurte5 Grupo de Investigación Aplicada en Materiales y Procesos (GIAMP), School of Chemical Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador School of Biological Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador Grupo de Investigación Aplicada en Materiales y Procesos (GIAMP), School of Chemical Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador Grupo de Investigación Aplicada en Materiales y Procesos (GIAMP), School of Chemical Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador Catalysis Theory and Spectroscopy Research Group (CATS), School of Chemical Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador Grupo de Investigación Aplicada en Materiales y Procesos (GIAMP), School of Chemical Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador A predictive model based on artificial neural networks (ANNs) for modeling primary settling tanks' (PSTs) behavior in wastewater treatment plants was developed in this study. Two separate ANNs were built using input data, raw wastewater characteristics, and operating conditions. The output data from the ANNs consisted of the total suspended solids (TSS) concentration and chemical oxygen demand (COD) as predictions of PSTs’ typical effluent parameters. Data from a large-scale wastewater treatment plant was used to illustrate the applicability of the predictive model proposal. The ANNs model showed a high prediction accuracy during the training phase. Comparisons with available empirical and statistical models suggested that the ANNs model provides accurate estimations. Also, the ANNs were tested using new experimental data to verify their reproducibility under actual operating conditions. The predicted values were calculated with satisfactory results, having an average absolute deviation of <20%. The model could be adapted to any large-scale wastewater plant to monitor and control the operation of primary settling tanks, taking advantage of the ANNs' learning capacity. HIGHLIGHTS A predictive model of the PSTs behavior using ANNs was developed.; The proposed model accurately predicts the TSS concentration and COD in the effluent.; The wastewater treatment plant (WWTP) in Ibarra, Ecuador, was considered a case study to show the applicability and reproducibility of the model.; A reliable predictive model would improve the monitoring of WWTPs.;http://wst.iwaponline.com/content/85/12/3451artificial neural networkschemical oxygen demandprimary settling tanksprocess modelingtotal suspended solidswastewater treatment plants |
| spellingShingle | Carlos Veloz Esteban Pazmiño-Arias Andrea M. Gallardo Jhon Montenegro Alicia Sommer-Márquez Marvin Ricaurte Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters Water Science and Technology artificial neural networks chemical oxygen demand primary settling tanks process modeling total suspended solids wastewater treatment plants |
| title | Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters |
| title_full | Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters |
| title_fullStr | Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters |
| title_full_unstemmed | Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters |
| title_short | Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters |
| title_sort | predictive modeling of the primary settling tanks based on artificial neural networks for estimating tss and cod as typical effluent parameters |
| topic | artificial neural networks chemical oxygen demand primary settling tanks process modeling total suspended solids wastewater treatment plants |
| url | http://wst.iwaponline.com/content/85/12/3451 |
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