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Optimal Alum Dosage Prediction Required to Treat Effluent Water Turbidity using Artificial Neural Network

Author : Jabbar H. Al-Baidhani and Mohammed Adnan Alameedee

Pages : 1552-1558
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The present study was conducted for the purpose of simulation of some units that was used intensively in a conventional water treatment plant. A pilot plant used to test the simulated raw water by mixing kaolinite clay with tap water and treated with difference alum dosage. The simulation tool that was used recently based on artificial intelligence and experimental tests, which is the neural network (ANN). The ANN was used to simulate and predict the required alum dosage for treated the turbidity in raw water. The experimental work was studied in a pilot plant scale, where a rig was designed and manufactured to simulated these treatment units. The synthetic turbidity was used to create the turbidity water that was used in this work. The experimental study was consisted of four runs with different inlet flow rate; each run contained five different influent turbidity sets (25, 50, 75, 100, 150) NTU and ten series of alum dosage varies form (5 – 50) mg/l for each turbidity set. The collected data for the neural network was about 200 set of data. The inverse model had a nine input parameters and one-output parameters, which is the alum dosage. The correlation coefficient of this model was 0.96, while the error indices were 4.1 mg/l, 2.84 mg/l, and 13.3% for RMSE, MAE, and MAPE respectively.

Keywords: Artificial Intelligence; Artificial Neural Network (ANN); Inverse Model; Correlation Coefficient (R); Root Mean Square Error; Mean Absolute Error; and Mean Absolute Percentage Error

Article published in International Journal of Current Engineering and Technology, Vol.7, No.4 (Aug-2017)

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