Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process
Predicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artifici...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-01-01
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| Series: | Adsorption Science & Technology |
| Online Access: | http://dx.doi.org/10.1155/2022/9739915 |
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| author | Mohamed K. Mostafa Ahmed S. Mahmoud Mohamed S. Mahmoud Mahmoud Nasr |
| author_facet | Mohamed K. Mostafa Ahmed S. Mahmoud Mohamed S. Mahmoud Mahmoud Nasr |
| author_sort | Mohamed K. Mostafa |
| collection | DOAJ |
| description | Predicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artificial neural network (ANN) for predicting biochemical oxygen demand (BOD) removal under different adsorption conditions. Nanozero-valent iron encapsulated into cellulose acetate (CA/nZVI) was synthesized, characterized by XRD, SEM, and EDS, and used as an efficient adsorbent for BOD reduction. Results indicated that the medium pH and adsorption time should be adjusted around 7 and 30 min, respectively, to maintain the highest BOD removal efficiency of 96.4% at initial BOD concentration Co=100 mg/L, mixing rate=200 rpm, and adsorbent dosage of 3 g/L. An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal (R2: 0.972, Adj-R2: 0.971, RMSE: 1.449, and SSE: 56.680). Based on the ANN sensitivity analysis, the relative importance of the adsorption factors could be arranged as pH>adsorbent dosage>time≈stirring speed>Co. A quadratic regression model was developed to visualize the impacts of adsorption factors on the BOD removal efficiency, optimizing pH at 7.3 and time at 46.2 min. The accuracy of the quadratic regression and ANN models in predicting BOD removal was approximately comparable. Hence, these computational-based methods could further maximize the performance of CA/nZVI material for removing BOD from wastewater under different adsorption conditions. The applicability of these modeling techniques would guide the stakeholders and industrial sector to overcome the nonlinearity and complexity issues related to the adsorption process. |
| format | Article |
| id | doaj-art-0959f72b234c4c57a850dcae20529e0b |
| institution | Kabale University |
| issn | 2048-4038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj-art-0959f72b234c4c57a850dcae20529e0b2025-01-02T23:44:58ZengSAGE PublishingAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/9739915Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption ProcessMohamed K. Mostafa0Ahmed S. Mahmoud1Mohamed S. Mahmoud2Mahmoud Nasr3Faculty of Engineering and TechnologyScientific Research Development UnitSanitary and Environmental Engineering Institute (SEI)Environmental Engineering DepartmentPredicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artificial neural network (ANN) for predicting biochemical oxygen demand (BOD) removal under different adsorption conditions. Nanozero-valent iron encapsulated into cellulose acetate (CA/nZVI) was synthesized, characterized by XRD, SEM, and EDS, and used as an efficient adsorbent for BOD reduction. Results indicated that the medium pH and adsorption time should be adjusted around 7 and 30 min, respectively, to maintain the highest BOD removal efficiency of 96.4% at initial BOD concentration Co=100 mg/L, mixing rate=200 rpm, and adsorbent dosage of 3 g/L. An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal (R2: 0.972, Adj-R2: 0.971, RMSE: 1.449, and SSE: 56.680). Based on the ANN sensitivity analysis, the relative importance of the adsorption factors could be arranged as pH>adsorbent dosage>time≈stirring speed>Co. A quadratic regression model was developed to visualize the impacts of adsorption factors on the BOD removal efficiency, optimizing pH at 7.3 and time at 46.2 min. The accuracy of the quadratic regression and ANN models in predicting BOD removal was approximately comparable. Hence, these computational-based methods could further maximize the performance of CA/nZVI material for removing BOD from wastewater under different adsorption conditions. The applicability of these modeling techniques would guide the stakeholders and industrial sector to overcome the nonlinearity and complexity issues related to the adsorption process.http://dx.doi.org/10.1155/2022/9739915 |
| spellingShingle | Mohamed K. Mostafa Ahmed S. Mahmoud Mohamed S. Mahmoud Mahmoud Nasr Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process Adsorption Science & Technology |
| title | Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process |
| title_full | Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process |
| title_fullStr | Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process |
| title_full_unstemmed | Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process |
| title_short | Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process |
| title_sort | computational based approaches for predicting biochemical oxygen demand bod removal in adsorption process |
| url | http://dx.doi.org/10.1155/2022/9739915 |
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