Prediction of the Chemical Oxygen Demand (COD) Using ...

Nile Basin Water Science & Engineering Journal, Vol.5, Issue 2, 2012 70

Prediction of the Chemical Oxygen Demand (COD) Using Artificial Neural Networks in the Rosetta Nile River Branch of Egypt

Soliman M. S.S.1, Maha M. Ali1, Alaa E. Abdin1 and Gehad G. Mohamed2

1 National Water Research Center (Egypt) 2 Chemistry Department, Faculty of Science, Cairo University (Egypt)

Abstract

Owing to discharge of primary, secondary and un-treated wastewater to surface water, associated with increases in population, urbanization and industrialization, the water quality of the Nile has been badly affected. Thus, there is an urgent need for new tools to improve process knowledge to support pollution control. One such tool is the Artificial Neural Network (ANN) modelling, which can be effective for understanding and predicting non-linear relationships. This paper discusses the development of an Artificial Neural Networks (ANNs) model for the prediction of the Chemical Oxygen Demand (COD) in the Rosetta Branch, in the north-west of the Nile Delta, EGYPT. After many runs, by the model, with different water quality parameters and verifying the model outputs with the actual values measured in the laboratory, the following variables were decided to be the input data to the model: biological oxygen demand (BOD), dissolved oxygen (DO) and water temperature for the prediction of chemical oxygen demand in the Rosetta branch. Four hidden-layers were developed, with four neurons for each layer, using the hyperbolic function. The study showed that the error percentage, between the actual and predicted records, ranged from close to 0 to 14 %. However, it is demonstrated that the artificial neural network (ANN) is capable of modeling the chemical oxygen demand concentration with good accuracy when proper variables are used. Key words: COD prediction, Artificial Neural Networks, Rosetta branch, Rahawy drain

1. INTRODUCTION

The River Nile is the main source of fresh water in Egypt. It has a dominating influence on the country’s culture, public health, and socio-economics. Rosetta Branch is one of the two Nile Delta branches which flows north west through the Delta to the Mediterranean Sea. It is about 220 km in length (starting from Delta at El-Kalubia governorate to Kafr El-Sheikh governorate) with an average width of 180 m (area of about 40 km2) and a depth varying between 2 & 4 m. It receives drainage water from five agricultural drains, which import sediments, animal and domestic wastes, crop residues, inorganic salts, minerals, chemical fertilizers, and pesticides to the delta. The branch also receives industrial effluent from three point sources of pollution at Kafr El-Zayat City which are El-Malaya El-Sinaaya, Soda and Salt and Kafr El-Zayat for pesticides companies. The industrial wastes may include toxic metals, chemicals, organic materials and biological contaminants (Donia and Farage 2005). Also there are many drinking water treatment plants discharging their wastes to the branch. El-Rahawy drain, one of the five agricultural drains, receives part of the Greater Cairo wastewater and carries the combined discharge from the agricultural drainage system and the two sewerage plants, Zenein and Abo-Rawash (Mohamed 2006). El Gammal and El Shazely (2008) estimates the pollution load of the drain as:

• 400,000 m3/day primary treated wastewater from Abo Rawash wastewater treatment plant. • 600,000 m3/day untreated wastewater as a bypass from Abo Rawash plant. • 430,000 m3/day of secondary treated wastewater from Zeinen wastewater treatment plant.

It is difficult to quantify the interactions and relationships that exist between the biological, chemical and physical processes inside the Rosetta Branch. There is an urgent need for new tools to improve pollution control. One such tool is the Artificial Neural Network (ANN) which has proven its ability to simulate and model various water quality problems using its architecture, non-linearity nature compared with the other classical modelling techniques (Najah, Elshafie et al. 2009).



2. PROBLEM IDENTIFICATION

Owing to the continuous release of wastes to the Rosetta branch, the water quality of the branch has deteriorated, with biological, physical and chemical water quality variables interacting in complex and, likely non-linear, nested ways. This complexity needs to be incorporated in considering amelioration measures. This paper presents a framework for developing an ANN model for the prediction of the chemical oxygen demand (COD). Determining COD requires costly analysis which places a financial burden on the national water quality monitoring network. This paper takes measuring points before, at and after El-Rahawy drain outfalls at Rosetta branch as examples of contamination. The potential of developing the ANN is to predict the concentration of some variables that are expensive to analyse, so as to support cost-effective monitoring.

3. ARTIFICIAL NEURAL NETWORK APPROACH AND MODELLING

The relationship between inputs and outputs, of the neural networks, can be non-linear or fuzzy, so the neural networks model learns this relationship through training. The Artificial Neural Networks (ANNs) have received a great deal of attention and are considered as effective computational tools to support environmental management. Neural networks are typically organized in layers. Layers are made up of a number of interconnected 'nodes' which contain an 'activation function'. Patterns are presented to the network via the 'input layer', which communicates to one or more 'hidden layers' (layers additional to the input and output layers, not connected externally) where the actual processing is done via a system of weighted 'connections'. The hidden layers then link to an 'output layer' where the answer is output as shown in Figure 1 (Caudill 1989).

Figure 1: Neural network of the model

The output of each neuron is a function of its inputs. In particular, the output of the jth neuron in any layer is described by two sets of equations: Uj = Σ (Xi * wij) and Yj = Fth (Uj + tj)

For every neuron, j, in a layer, each of the i inputs, Xi, to that layer is multiplied by a previously established weight, wij. These are all summed together, resulting in the internal value of this operation, Uj. This value is then biased by a previously established threshold value, tj, and sent through an activation function, Fth. There are many activation functions, and the one which give the best results in this study is the Hyperbolic function. The resulting output, Yj, is an input to the next layer or it is a response of the neural network if it is the last layer. In mathematics (Wikimedia Foundation 2010), hyperbolic functions are analogs of the ordinary circular functions. The basic hyperbolic functions are the hyperbolic sine "sinh", and the hyperbolic cosine "cosh", from which are derived the hyperbolic tangent "tanh", widely used in neural networks. It is expressed by the following equation (Nissen 2003):



Where t is the value that pushes the center of the activation function away from zero and s is a steepness parameter. The hyperbolic function is shaped exactly as shown in Figure 2, ranging from -1 to +1 rather than 0 to 1 for other functions. Thus, it has the interesting property that there is inhibition near 0, but values at extremes will be excited to full levels, but in opposite directions. A hyperbolic function incorporates a switch, or transition zone.

Figure 2: Shape of Hyperbolic function.

The widely used technique in ANN development is the "backward propagation of errors", generally known as the backpropagation. The backpropagation neural network has been shown to be an effective neural design for forecasting stream water quality variables (Lek, Guiresse et al. 1999).

4. WATER QUALITY RELATIONSHIPS

The chemical oxygen demand (COD) is a measure of the amount of organic compounds in water. Most applications of COD determine the amount of organic pollutants found in surface water, making COD a useful measure of water quality. The biological oxygen demand (BOD) is the amount of dissolved oxygen (DO) needed by aerobic biological organisms in a body of water to break down organic material present in a given water sample at certain temperature over a specific time period. There is a clear relationship between COD concentration and BOD concentration, where, in clean water, the concentration of the COD parameter is almost stable and equals to 1.6 of the BOD concentration (Tchobanoglous and Burton 2003). BOD is similar in function to COD, in that both measure the amount of organic compounds in water. However, COD is less specific, since it measures everything that can be chemically oxidized, rather than just levels of biologically active organic matter. Organic matter is degraded, by oxygen, to smaller intermediates and finally to carbon dioxide (CO2) and water (H2O). The overall reaction equation is given by: Organic Matter + O2 CO2 + n H2O It is very important to measure the COD values in order to determine organic contamination of the Rosetta branch. Unfortunately, COD concentration is a high cost analysis parameter, so it is highly advantageous to predict COD values using less costly analysis. One such tool is "Artificial Intelligence (AI)"; the theory and development of computer systems able to perform tasks that normally require human intelligence. A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an information processing paradigm inspired by the way biological nervous systems, such as the brain process information. It is composed of a large

sinh (s(X+t))

cosh (s(X+t)) Fth =

es(X+t) – e-s(X+t)

es(X+t) + e-s(X+t)Fth = tanh (s(X +t)) =

Input

Output



number of highly interconnected processing elements (neurons) working in unison to solve specific problems. The goal of this network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown (Notton, Paoli et al. 2013).

5. RESEARCH METHODOLOGY AND MODEL DEVELOPMENT

In Rosetta branch many parameters affect the COD. After many chemical analyses and computational efforts by the ANN model, it was found that, beside the BOD, dissolved oxygen (DO) concentration and water temperature affecting COD concentration. The number of epochs per update is set to be to 1, the learning algorithm set to a learning rate of 1 which determines the magnitude of the correction term applied to adjust each neuron's weights during the training process. The training tolerance is reduced to 0.001 to define the percentage error allowed in comparing the neural network output to the target value during the training process. The testing tolerance is equal to 0.003 which is similar to the training tolerance but for the test data. The momentum, which determines the "lifetime" of a correction term, is equal to 0.9, while the input noise, which represents a percentage of the range for an input, is equal to 0. The software used for this study is the Neuralyst version 1.42. The particular version used to run the simulation is the Excel module. The model was applied, by introducing 6 layers as follows:

1. Inputs layer where it consists of 3 neurons; BOD, DO and water temperature. 2. Four hidden layers with 4 neurons for each layer. 3. Outputs layer with only one neuron which is the COD values.

Figure 3 shows the architecture of the model.

Figure 3: Neural network architectures of COD prediction.

Three location sites were chosen in this paper (Figure 4): Rosetta branch before its connection with El-Rahawy drain (R1), El-Rahawy drain outlet (R2) and Rosetta branch after its connection El-Rahawy drain (R3).



Figure 4: Location sites at Rosetta branch.

The data was arranged in ascending order of the BOD values, as it is considered the main parameter that the COD values depend on, and then the model was run 3 times for each measuring point, where: 1. In the first run: The first third of the data was taken as the model testing phase (i.e. the part

that would be predicted by the model), while the other two thirds were considered as the model training phase (i.e. the part that is used to develop the ANN model). For example, if the number of the readings was 51, the first 17 readings would be predicted by the model, while the remaining 34 readings would be used as the base values that the model would use to develop the network.

2. In the second run: The second third of the data was taken as the model testing phase, while the first and last thirds were considered as the model training phase.

3. In the third run: The last third of the data was taken as the model testing phase, while the first and second thirds were considered as the model training phase.



6. RESULTS & DISCUSSION

The results showed that the COD values of actual and the model outputs, of the model training phases, are almost identical with error percentage nearly 0%. The following sections illustrate the model testing phases.

6.1. Prediction of COD Concentration for R1 Measuring Point.

There are 52 readings at R1 measuring point from January 2003 to February 2010. The following figures illustrate the actual data and the data predicted by the model, where: Figure 5 refers to a comparison between the actual and predicted values, by the model, of COD for the first 17 samples. The error percentage ranged between 0.06% at sample number 17, and 14% at sample number 6 except for sample number 8 where the error percentage reached to 15.64 because of the water closer period, at winter season, that causes more pollutants (municipal wastes, organic matter and metals) to concentrate in the branch. These pollutants cause much interference and disturbance to the branch's system, and as a result of this disturbance, the model error percentage increases. While, Figure 6 shows a comparison between the actual and predicted COD values for samples from 18 to 34. The error percentage ranged between 0.1% at samples number 18 and 25, and 6% at samples number 19 and 22. Figure 7 refers to a comparison between the actual and predicted values of COD for samples from 35 to 52. The error percentage ranged between about 0% at sample number 52, and 3.5% at sample number 40.

2

5

8

11

14

17

20

0 2 4 6 8 10 12 14 16 18

CO

D (m

g/L)

Samples

Actual COD

Figure 5: Actual and predicted values of COD for R1 measuring point (1st Run "Model Testing

Phase").

12

16

20

24

28

32

CO

D (m

g/L)

Figure 6: Actual and predicted values of COD for R1 measuring point (2nd Run "Model Testing

Phase").



14

18

22

26

30

34

38

CO

D (m

g/L)

Figure 7: Actual and predicted values of COD for R1 measuring point

(3rd Run "Model Testing Phase").

Statistical analysis, consisted of the determination of mean, median, minimum and maximum values for the actual and predicted COD concentrations for the three thirds of the data (at the three runs), were performed, and it is shown in table 1.

Table 1: Statistical parameters for COD concentrations at R1 location site.

COD concentration (mg/L) Mean Median Minimum Maximum Standard

Deviation

1st th

ird

of th

e da

ta

(1st r

un)

Actual values 8.3 7.3 4 20 4.167

Predicted values 8.56 7.27 4 20.41 4.168

2nd th

ird

of th

e da

ta

(2nd

run

)

Actual values 13.24 12 6.5 30 4.99

Predicted values 13.11 11.98 6.48 29.81 4.85

3rd th

ird

of th

e da

ta

(3rd

run

)

Actual values 23.1 21.75 12.7 35 6.34

Predicted values 22.74 21 12.71 35 6.52



6.2. Prediction of COD Concentration for R2 Measuring Point.

There are 50 readings at R2 measuring point from January 2003 to February 2010. The following figures illustrate the actual data and the data predicted by the model, where: Figure 8 illustrates a comparison between the actual and predicted values, by the model, of COD for the first 17 samples. The error percentage ranged between 0.165% at sample number 12, and 11% at sample number 15. Figure 9 shows a comparison between the actual and predicted COD values for samples from 18 to 34. The error percentage ranged between 0.065% at sample number 31, and 8.88% at sample number 25. Figure 10 refers to a comparison between the actual and predicted values of COD for samples from 35 to 50. The error percentage ranged between about 0.199% at sample number 50, and 5% at sample number 40 except for sample number 37 where the error percentage reached to 14.55% because of the water closer period.

125

130

135

140

145

150

155

160

165

170

175

0 2 4 6 8 10 12 14 16 18

CO

D (m

g/L)

Samples

Actual COD

ANN Prediction


(1st Run "Model Testing Phase").

140

160

180

200

220

240

CO

D (m

g/L)


(2nd Run "Model Testing Phase").



180

200

220

240

260

280

300

320

340

34 36 38 40 42 44 46 48 50 52

CO

D (m

g/L)

Samples

Actual CODANN Prediction

Figure 10: Actual and predicted values of COD for R2 measuring point (3rd Run "Model Testing

Phase").

Statistical analysis for the actual and predicted COD concentrations is shown in table 2.

Table 2: Statistical parameters for COD concentrations at R2 location site.


Deviation

1st th

ird

of th

e da

ta

(1st r

un)

Actual values 146.74 139 130 174 12.11


2nd th

ird

of th

e da

ta

(2nd

run

)

Actual values 165.88 165 136.5 220 23.47


3rd th

ird

of th

e da

ta

(3rd

run

)

Actual values 274.63 299.5 197 330 6.34


6.3. Prediction of COD concentration for R3 measuring point.

There are 53 readings at R3 measuring point from January 2003 to February 2010. The following figures illustrate the actual data and the data predicted by the model, where: Figure 11 illustrates a comparison between the actual and predicted values by the model of COD for the first 18 samples. The error percentage ranged between 0.133% at sample number 17, and 3.18% at sample number 4. Figure 12 shows a comparison between the actual and predicted COD values for samples from 19 to 36. The error percentage ranged between 0.046% at sample number 20, and 13% at sample number 28. Figure



13 refers to a comparison between the actual and predicted values of COD for samples from 37 to 53. The error percentage ranged between about 0.6% at samples number 50 and 51, and 8.43% at sample number 39.

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12 14 16 18

COD

(mg/

L)

Samples

Actual CODANN Prediction


(1st Run "Model Testing Phase").

60

70

80

90

100

110

120

130

140

150

CO

D (m

g/L)


(2nd Run "Model Testing Phase").



160

180

200

220

240

260

280

CO

D (m

g/L)

Figure 13: Actual and predicted values of COD for R3 measuring point (3rd Run "Model Testing

Phase").

The statistical analysis for the actual and predicted COD concentrations is shown in table 3.

Table (3). Statistical parameters for COD concentrations at R3 location site.


Deviation

1st th

ird

of th

e da

ta

(1st r

un)

Actual values 38.54 36.5 6 76.5 22.26


2nd th

ird

of th

e da

ta

(2nd

run

)

Actual values 105.03 108.5 46.6 140 21.83


3rd th

ird

of th

e da

ta

(3rd

run

)

Actual values 187.88 176 140 264 38.76


7. CONCLUSION

Based on the model outputs the following conclusions can be stated: • COD actual concentrations at R2 measuring point are higher than the corresponding values at

both R1 and R3 points. In general the concentrations ranged in the following order: R2>R3>R1.



Accordingly, the error percentage of the model outputs at R2 point was higher than the other points. This can be attributed to the fact that the water quality of El-Rahawy drain is much lower than that of the Nile branch, so it can be concluded that as the water quality deteriorates, the interference of wastes between each other increases and the model performance minimizes. So, the model performance depends on the degree of water contamination. Also this phenomenon may be attributed to that there are other variables affecting on COD concentration other than those taken into consideration (e.g. the exact amount of sediments, wastes and chemicals imported to the branch and its timing, is it in an ordered or random ways and quantities?...etc).

• The error percentage, between the model outputs and actual COD values, increases at the water closer period more than the other part of the year.

• Although the interfered, complicated, and non-linear nature of the Rosetta branch water quality parameters, the artificial neural networks succeeded in predicting COD concentrations with acceptable error percentage.

8. REFERENCES

1. Caudill, M., 1989) "A Basic Introduction to Neural Networks.", Retrieved 22 June 2010, from http://pages.cs.wisc.edu/~bolo/shipyard/neural/neural.html.

2. Donia, N. and H. Farage, 2005, "Development of the Nile River Water Quality Information System", First International Conference,9 - 11 April 2005, Ain Shams University, Egypt

3. El Gammal, H. and H. El Shazely, 2008, "Water Quality Management Scenarios in Rosetta River Nile Branch, Egypt", Twelfth International Water Technology Conference, IWTC12Alexandria, Egypt,901 - 912

4. Lek, S., M. Guiresse, et al., 1999, "Predicting Stream Nitrogen Concentration from Watershed Features Using Neural Networks", Water Research 33(16): 3469-3478.

5. Mohamed, M. M. Y. (2006). "Studies on Water, Sediments and Pollution Interaction through Rossetta and Damietta Nile Branches", Chemistry, Zagazig University, Doctor of Philosophy in Chemistry Science

6. Najah, A., A. Elshafie, et al., 2009, "Prediction of Johor River Water Quality Parameters Using Artificial Neural Networks", European Journal of Scientific Research 28(3): 422-435.

7. Nissen, S., 2003,"Neural Network Theory", Retrieved 25 June 2010, from http://fann.sourceforge.net/report/node3.html.

8. Notton, G., C. Paoli, et al., 2013, "Neural Network Approach to Estimate 10-Min Solar Global Irradiation Values on Tilted Planes", Renewable Energy 50: 576-584.

9. Tchobanoglous and Burton, 2003, "Wastewater Engineering - Treatment and Reuse" 10. Wikimedia Foundation, I., 2010, "Hyperbolic Function,

Http://En.Wikipedia.Org/Wiki/Hyperbolic_Function." WIKIPEDIA, Retrieved 25 June 2010, from

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