Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants

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Ruba D. Alsaeed
Bassam Alaji
Mazen Ibrahim


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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Alsaeed RD, Alaji B, Ibrahim M. Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants. Baghdad Sci.J [Internet]. 2022 Oct. 1 [cited 2022 Nov. 30];19(5):0951. Available from:


Haghiri S, Daghighi A, Moharramzadeh S. Optimum coagulant forecasting by modeling jar test experiments using ANNs. Drink. Water Eng Sci. 2018; 11(1): 1-8.

El-Chaghaby G, Rashad S, Moneem M A. Seasonal variation and correlation between the physical, chemical and microbiological parameters of Nile water in selected area in Egypt (Case study): physical, chemical and microbiological parameters of Nile water. Baghdad Sci J, 2020; 17(4): 1160-1160. ‏

Song C, Zhang H. Study on turbidity prediction method of reservoirs based on long short term. Ecol Modell. 2020; 432.

Saritha V, Srinivas N, Srikanth Vuppala NV. Analysis and optimization of coagulation and flocculation process. Appl Water Sci. 2017;7(1): 451-460. ‏

Al-Baidhani JH, Alameedee MA. Prediction of water treatment plant outlet turbidity using artificial neural network. Int J Curr Eng Sci. 2017; 7(4): 1559-1565.‏

Lanciné G D, Bamory K, Raymond L, Jean-Luc S, Christelle B, Jean B. Coagulation-Flocculation treatment of a tropical surface water with alum for dissolved organic matter (DOM) removal: Influence of alum dose and pH adjustment. J Int Environ Appl Sci. 2008; 3(4): 247-257. ‏

Alsaeed R, Alaji B, Ebrahim M: Predicting turbidity and Aluminum in drinking water treatment plants using Hybrid Network (GA- ANN) and GEP, Drink. Water Eng Sci Discuss. in review, 2021:1-17.

Dentel, Steven K., and James M. Gossett. Mechanisms of coagulation with aluminum salts. J Am Water Works Ass. 1988;80(4): 187-198. ‏

Mackenzie L Davis. Water and wastewater engineering: design principles and practice. McGraw-Hill Education, 2010: 6-15.

Hassan F M, Naji H F, Al-Azawey E S. The study of some physical and chemical characteristics in drinking water treatment plant of Jurf Al-Sakar Subdestric in Babylon Governorate, Iraq. Baghdad Sci J. 2007; 4(3): 338-343. ‏

Al-Kenzawi M A, Al-Haidary M J, Talib A H, Karomi M F. Environmental Study of Some Water Characteristics at Um-Al-Naaj Marsh, South of Iraq. Baghdad Sci. J. 2011; 8(1): 531-538. ‏‏

Kim C M, Parnichkun M. Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system. Appl Water Sci,2020; 7(7): 3885-3902. ‏ DOI 10.1007/s13201-017-0541-5.

Abba S I, Rabiu Aliyu Abdulkadir, Gaya M S, Saleh M, Parveneh Esmaili, Mustapha Bala Jibril. Neuro-fuzzy ensemble techniques for the prediction of turbidity in water treatment plant. 2nd International Conference of the IEEE Nigeria Computer Chapter. (Nigeria Comput Conf).IEEE.2019:1-6. DOI:10.1109/NigeriaComputConf45974.2019.8949629

Heddam S. Extremely Randomized Tree: A New Machines Learning Method for predicting Coagulant Dosage in Drinking Water Treatment Plant. Water Engineering Modelling and Mathematic Tools. Chennai: Elsevier, 2021: 475-4902021.

Heddam S, Bermad A, Dechemi N. Applications of Radial Basis Function and Generalized Regression Neural Networks for Modelling of Coagulant dosage in a Drinking Water Treatment: Comparative Study. ASCE J Environ Eng. 2011; 137(12):1209-1214. DOI: 10.1061/(ASCE)EE.1943-7870.0000435

León-Luque A J, Barajas C L, Peña-Guzmán C A. Determination of the optimal dosage of aluminum sulfate in the coagulation-flocculation process using an artificial neural network. Int J Environ Sci. Dev.2016; 7(5): 346.