Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants
Coagulation is the most important process in drinking water treatment. Alum coagulant increases the aluminum residuals, which have been linked in many studies to Alzheimer's disease. Therefore, it is very important to use it with the very optimal dose. In this paper, four sets of experiments w...
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University of Baghdad, College of Science for Women
2022-10-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6452 |
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| author | Ruba D. Alsaeed Bassam Alaji Mazen Ibrahim |
| author_facet | Ruba D. Alsaeed Bassam Alaji Mazen Ibrahim |
| author_sort | Ruba D. Alsaeed |
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Coagulation is the most important process in drinking water treatment. Alum coagulant increases the aluminum residuals, which have been linked in many studies to Alzheimer's disease. Therefore, it is very important to use it with the very optimal dose. In this paper, four sets of experiments were done to determine the relationship between raw water characteristics: turbidity, pH, alkalinity, temperature, and optimum doses of alum [ .14 O] to form a mathematical equation that could replace the need for jar test experiments. The experiments were performed under different conditions and under different seasonal circumstances. The optimal dose in every set was determined, and used to build a gene expression model (GEP). The models were constructed using data of the jar test experiments: turbidity, pH, alkalinity, and temperature, to predict the coagulant dose. The best GEP model gave very good results with a correlation coefficient (0.91) and a root mean square error of 1.8. Multi linear regression was used to be compared with the GEP results; it could not give good results due to the complex nonlinear relation of the process. Another round of experiments was done with high initial turbidity like the values that comes to the plant during floods and heavy rain. To give an equation for these extreme values, with studying the use of starch as a coagulant aid, the best GEP gave good results with a correlation coefficient of 0.92 and RMSE 5.1
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| format | Article |
| id | doaj-art-b698172ca70a40b4ac5470c19ddc7e02 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | University of Baghdad, College of Science for Women |
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| series | مجلة بغداد للعلوم |
| spelling | doaj-art-b698172ca70a40b4ac5470c19ddc7e022025-08-20T03:38:43ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862022-10-0119510.21123/bsj.2022.6452Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment PlantsRuba D. Alsaeed 0Bassam Alaji1Mazen Ibrahim2Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Damascus University, Damascus, SyriaDepartment of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Damascus University, Damascus, Syria.Department of Engineering Management and Construction, Faculty of Civil Engineering, Damascus University, Damascus, Syria Coagulation is the most important process in drinking water treatment. Alum coagulant increases the aluminum residuals, which have been linked in many studies to Alzheimer's disease. Therefore, it is very important to use it with the very optimal dose. In this paper, four sets of experiments were done to determine the relationship between raw water characteristics: turbidity, pH, alkalinity, temperature, and optimum doses of alum [ .14 O] to form a mathematical equation that could replace the need for jar test experiments. The experiments were performed under different conditions and under different seasonal circumstances. The optimal dose in every set was determined, and used to build a gene expression model (GEP). The models were constructed using data of the jar test experiments: turbidity, pH, alkalinity, and temperature, to predict the coagulant dose. The best GEP model gave very good results with a correlation coefficient (0.91) and a root mean square error of 1.8. Multi linear regression was used to be compared with the GEP results; it could not give good results due to the complex nonlinear relation of the process. Another round of experiments was done with high initial turbidity like the values that comes to the plant during floods and heavy rain. To give an equation for these extreme values, with studying the use of starch as a coagulant aid, the best GEP gave good results with a correlation coefficient of 0.92 and RMSE 5.1 https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6452Alum dose, Artificial intelligence, Coagulant, Gene expression, Multi linear regression, Turbidity |
| spellingShingle | Ruba D. Alsaeed Bassam Alaji Mazen Ibrahim Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants مجلة بغداد للعلوم Alum dose, Artificial intelligence, Coagulant, Gene expression, Multi linear regression, Turbidity |
| title | Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants |
| title_full | Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants |
| title_fullStr | Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants |
| title_full_unstemmed | Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants |
| title_short | Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants |
| title_sort | modeling jar test results using gene expression to determine the optimal alum dose in drinking water treatment plants |
| topic | Alum dose, Artificial intelligence, Coagulant, Gene expression, Multi linear regression, Turbidity |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6452 |
| work_keys_str_mv | AT rubadalsaeed modelingjartestresultsusinggeneexpressiontodeterminetheoptimalalumdoseindrinkingwatertreatmentplants AT bassamalaji modelingjartestresultsusinggeneexpressiontodeterminetheoptimalalumdoseindrinkingwatertreatmentplants AT mazenibrahim modelingjartestresultsusinggeneexpressiontodeterminetheoptimalalumdoseindrinkingwatertreatmentplants |