Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater
This study was aimed at evaluating the artificial neural network (ANN), genetic algorithm (GA), adaptive neurofuzzy interference (ANFIS), and the response surface methodology (RSM) approaches for modeling and optimizing the simultaneous adsorptive removal of chemical oxygen demand (COD) and total or...
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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/10.1155/2022/7874826 |
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| author | Areej Alhothali Hifsa Khurshid Muhammad Raza Ul Mustafa Kawthar Mostafa Moria Umer Rashid Omaimah Omar Bamasag |
| author_facet | Areej Alhothali Hifsa Khurshid Muhammad Raza Ul Mustafa Kawthar Mostafa Moria Umer Rashid Omaimah Omar Bamasag |
| author_sort | Areej Alhothali |
| collection | DOAJ |
| description | This study was aimed at evaluating the artificial neural network (ANN), genetic algorithm (GA), adaptive neurofuzzy interference (ANFIS), and the response surface methodology (RSM) approaches for modeling and optimizing the simultaneous adsorptive removal of chemical oxygen demand (COD) and total organic carbon (TOC) in produced water (PW) using tea waste biochar (TWBC). Comparative analysis of RSM, ANN, and ANFIS models showed mean square error (MSE) as 5.29809, 1.49937, and 0.24164 for adsorption of COD and MSE of 0.11726, 0.10241, and 0.08747 for prediction of TOC adsorption, respectively. The study showed that ANFIS outperformed the ANN and RSM in terms of fast convergence, minimum MSE, and sum of square error for prediction of adsorption data. The adsorption parameters were optimized using ANFIS-surface plots, ANN-GA hybrid, RSM-GA hybrid, and RSM optimization tool in design expert (DE) software. Maximum COD (88.9%) and TOC (98.8%) removal were predicted at pH of 7, a dosage of 300 mg/L, and contact time of 60 mins using ANFIS-surface plots. The optimization approaches showed the performance in the following order: ANFIS-surface plots>ANN-GA>RSM-GA>RSM. |
| format | Article |
| id | doaj-art-6d4ad59692c84ce7b7658aa5ff6edc6c |
| 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-6d4ad59692c84ce7b7658aa5ff6edc6c2025-01-02T23:45:15ZengSAGE PublishingAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/7874826Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in WastewaterAreej Alhothali0Hifsa Khurshid1Muhammad Raza Ul Mustafa2Kawthar Mostafa Moria3Umer Rashid4Omaimah Omar Bamasag5Department of Computer SciencesDepartment of Civil & Environmental EngineeringDepartment of Civil & Environmental EngineeringDepartment of Computer SciencesInstitute of Nanoscience and Nanotechnology (ION2)Center of Excellence in Smart Environment ResearchThis study was aimed at evaluating the artificial neural network (ANN), genetic algorithm (GA), adaptive neurofuzzy interference (ANFIS), and the response surface methodology (RSM) approaches for modeling and optimizing the simultaneous adsorptive removal of chemical oxygen demand (COD) and total organic carbon (TOC) in produced water (PW) using tea waste biochar (TWBC). Comparative analysis of RSM, ANN, and ANFIS models showed mean square error (MSE) as 5.29809, 1.49937, and 0.24164 for adsorption of COD and MSE of 0.11726, 0.10241, and 0.08747 for prediction of TOC adsorption, respectively. The study showed that ANFIS outperformed the ANN and RSM in terms of fast convergence, minimum MSE, and sum of square error for prediction of adsorption data. The adsorption parameters were optimized using ANFIS-surface plots, ANN-GA hybrid, RSM-GA hybrid, and RSM optimization tool in design expert (DE) software. Maximum COD (88.9%) and TOC (98.8%) removal were predicted at pH of 7, a dosage of 300 mg/L, and contact time of 60 mins using ANFIS-surface plots. The optimization approaches showed the performance in the following order: ANFIS-surface plots>ANN-GA>RSM-GA>RSM.http://dx.doi.org/10.1155/2022/7874826 |
| spellingShingle | Areej Alhothali Hifsa Khurshid Muhammad Raza Ul Mustafa Kawthar Mostafa Moria Umer Rashid Omaimah Omar Bamasag Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater Adsorption Science & Technology |
| title | Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater |
| title_full | Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater |
| title_fullStr | Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater |
| title_full_unstemmed | Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater |
| title_short | Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater |
| title_sort | evaluation of contemporary computational techniques to optimize adsorption process for simultaneous removal of cod and toc in wastewater |
| url | http://dx.doi.org/10.1155/2022/7874826 |
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