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 |
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Sage Publications
2022
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| _version_ | 1811137580391989248 |
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| author | Alhothali, Areej Khurshid, Hifsa Mustafa, Muhammad Raza Ul Moria, Kawthar Mostafa Rashid, Umer Bamasag, Omaimah Omar |
| author_facet | Alhothali, Areej Khurshid, Hifsa Mustafa, Muhammad Raza Ul Moria, Kawthar Mostafa Rashid, Umer Bamasag, Omaimah Omar |
| author_sort | Alhothali, Areej |
| collection | UPM |
| 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. |
| first_indexed | 2024-09-25T03:36:33Z |
| format | Article |
| id | upm.eprints-101325 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-09-25T03:36:33Z |
| publishDate | 2022 |
| publisher | Sage Publications |
| record_format | dspace |
| spelling | upm.eprints-1013252024-08-05T07:44:05Z http://psasir.upm.edu.my/id/eprint/101325/ Evaluation of contemporary computational techniques to optimize adsorption process for simultaneous removal of COD and TOC in wastewater Alhothali, Areej Khurshid, Hifsa Mustafa, Muhammad Raza Ul Moria, Kawthar Mostafa Rashid, Umer Bamasag, Omaimah Omar 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. Sage Publications 2022 Article PeerReviewed Alhothali, Areej and Khurshid, Hifsa and Mustafa, Muhammad Raza Ul and Moria, Kawthar Mostafa and Rashid, Umer and Bamasag, Omaimah Omar (2022) Evaluation of contemporary computational techniques to optimize adsorption process for simultaneous removal of COD and TOC in wastewater. Adsorption Science & Technology, spec.. pp. 1-16. ISSN 2048-4038; ESSN: 0263-6174 https://www.hindawi.com/journals/ast/2022/7874826/ 10.1155/2022/7874826 |
| spellingShingle | Alhothali, Areej Khurshid, Hifsa Mustafa, Muhammad Raza Ul Moria, Kawthar Mostafa Rashid, Umer Bamasag, Omaimah Omar Evaluation of contemporary computational techniques to optimize adsorption process for simultaneous removal of COD and TOC in wastewater |
| 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 |
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