The alum dosage was varied while the polymer dosage was keep constant. .... Ferric Chloride. Ferric Sulphate. Iron Sludge. Alum. Seasme Seeds. Corn.
An Artificial Neural Network Base System for the Prediction & Optimisation of Coagulant Dosing in Water Treatment Plants Akil De Leon, Beverly Chittoo and Dr. Clint Sutherland Unit of Project Management and Civil Infrastructure Systems, The University of Trinidad and Tobago Methods and Materials Results and Discussion
Introduction In the treatment of drinking water, the Coagulation – Flocculation process is used to destabilise and remove the suspended colloidal particles in water. These particles are removed via the addition of a coagulant. The most common coagulants used are Aluminum Sulfate (Alum), Ferric Sulfate and Ferric Chloride1. This process is one of the most crucial stages for the maintenance of an acceptable treated water quality and for an economical plant operation2.
A synthetic solution was prepared using distilled water and bentonite clay and varied to achieve the Initial turbidity.
The main test used to the determine the optimum coagulation conditions, is the Jar Test. In conducting the jar test one factor at a time is adjusted while the others are kept constant. Consequently, the development of a predictive model becomes restrictive.
The operational speeds and conditions of the Caroni WTP was used as a benchmark to perform the Jar Test. The coagulant and polymer was then added before rapid mixing.
The initial turbidity and pH of the solution was checked and adjusted. The alum dosage was varied while the polymer dosage was keep constant. The stock solution was pour out into six 1000ml glass beakers.
Optimum Dosage & pH
Alum dose was varied from 5 mg/l to 120 mg/l. The optimum alum dose required to achieve the desired settling turbidity increased with increased initial turbidity.
The Artificial Neural Network (ANN) approach allows the analysis of multivariable non-linear relationships and can be used to develop a model which predicts optimum coagulant dosage. The model will incorporate operational conditions as initial pH, initial Turbidity and the desired settling Turbidity in NTU. Optimisation of the Coagulation – Flocculation process using ANN can reduce the amount of chemicals being used, testing time and consequently reduced the operational cost.
Percentage Removed
60 Ferric Chloride
50
Ferric Sulphate
40
Iron Sludge
30
Alum
20
Seasme Seeds
10
Corn
0
Materials
Figure 1. Comparison of Coagulants.
Figure 3. Flocs formation.
Samples were then taken after the allowed settling time and tested. The settling turbidity vs pH vs alum dosage was plotted.
Coagulant Screening Chemical and organic coagulants were assessed to determine their ability to remove bentonite clay. Coagulants were screened using a dose of 25mg/ and turbidity of 50 NTU. Alum performed the best in removing initial Turbidity.
Theory An Artificial Neural Network is a computational system inspired by the structure, processing method and learning ability of a biological brain. It utilises available data to identify and learn input-output relationships and develop the ability to predict new relationships. A three-layer feed forward ANN model will be developed for predicting coagulant dosage. The ANN architecture is given below:
Figure 2.Comparison with Polymer. Settling Turbidity (NTU)
Optimum Dosage & pH
Optimum Dosage & pH
Table 1. Experimental Characteristic for Jar Test experiments.
Methods and Materials 70
Initial turbidity was varied from 20 NTU to 300 NTU. The plots reveal that pH was marginally affected by initial turbidity. The optimal pH was found to range between 6 and 7.
Conclusions Various chemical and organic coagulants were assessed and alum was found to remove 62% of the turbidity. Coagulant aid was found to improve the time of settling and the final turbidity using a dosage of 0.03 mg/l. Jar test was performed by varying pH, initial turbidity and coagulant does. pH was unaffected by initial turbidity while alum dose varied significantly. The ANN architecture was developed The ANN Architecture was be developed using the initial pH, initial turbidity, polymer dose and desired settling turbidity as inputs to give a prediction of the optimum coagulant dosage.
Future Work
100 90 80 70 60 50 40 30 20 10 0
Further work will be done to investigate higher initial turbidity up to 1000 NTU. The ANN model would be developed and optimised by varying training algorithms, transfer functions and the number of neurons. The predictive model will be validated using raw surface water samples.
With Polymer Without Polymer
Figure 5. Turbidity Range . 0
10
20
30 40 Alum Dosage (mg/l)
50
60
Figure 4. HACH 2100AN Turbidimeter.
References
Assessment of Polymer Polymer is often used as a flocculant aid. It helps in the creation of larger Flocs by bridging with other micro flocs. In Figure 2 it can be seen that settling turbidity is lower with the aid of the polymer of dose 0.03 mg/l. Figure 6. HACH HQ430d Flexi pH meter .
Figure 7. Treatment process.
Figure 8. ANN Architecture.
1. Zainal-Abideen, M., Aris, A., Yusof, F., Abdul-Majid, Z., Selamat, A., & Omar, S. (2012). Optimizing the coagulation process in a drinking water treatment plant – comparison between traditional and statistical experimental design jar tests. Water Science & Technology, 65(3), 496. http://dx.doi.org/10.2166/wst.2012.561 2. Wu, G., & Lo, S. (2010). Effects of data normalization and inherent-factor on decision of optimal coagulant dosage in water treatment by artificial neural network. Expert Systems With Applications,37(7), 4974-4983. http://dx.doi.org/10.1016/j.eswa.2009.12.016
