Open AccessModeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants
Author(s) -
Ruba Alsaeed,
Bassam Alaji,
Mahmoud Ibrahim
Publication year - 2022
Publication title -
mağallaẗ baġdād li-l-ʿulūm
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.167
H-Index - 6
eISSN - 2411-7986
pISSN - 2078-8665
DOI - 10.21123/bsj.2022.6452
Subject(s) - alum , turbidity , alkalinity , gene expression programming , correlation coefficient , mathematics , coefficient of determination , mean squared error , raw water , aluminium sulfate , coagulation , pulp and paper industry , chemistry , statistics , environmental engineering , environmental science , biology , ecology , flocculation , engineering , psychology , organic chemistry , machine learning , psychiatry , computer science
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