**Case 4.2: Operational change**

The second typical change is an *operational change*. This is defined as a situation in which one of the variables controlled by operators has been changed. Some examples of such variables may be pressure or flow. An operational change may influence the variables' quality. One such example of this type of situation is shown in **Figure 9**. The black line shows a change in the pressure (PRI) value. Shortly after this change, a peak in the Turbidity value is recorded (see red line in **Figure 9**).

**Figure 11** shows a change in the water source. Conductivity rose from a level of 345 mS to a level of 385 mS (within 6 hours, from 10:00 am to 4:30 pm). Together with this, the pH level

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The effect on the dynamic noise curve can be seen in **Figure 12**. The average value of the dynamic noise curve has changed. This is due to the change in the noise level of the water

A change in water source, which can take between 30 and 60 minutes and up to several hours, will result in a change in the noise of the dynamic noise curve. Sometimes, it will also cause a

Finally, we have the case of contamination. **Figure 13** shows raw data from a typical contamination event. The event starts with a drop in the Free Chlorine (see green line in **Figure 13**). This is due to chlorine consumption by the contaminator. Shortly after, the Turbidity level

dropped from 8.20 to 8.07.

**Figure 9.** Operational change - raw data.

measurements from the new source.

**Case 4.4: Contamination event**

starts to rise (see blue line in **Figure 13**).

**Figure 10.** Operational change - dynamic noise curve.

change to occur in the average value of the dynamic noise.

**Figure 10** shows the corresponding changes in the dynamic noise curve. The chart indicates that the operational change also results in the violation of the dynamic noise's "red line". However, this can be explained by the change in the operational variable.

Since operational changes are also sudden changes, the peak in the dynamic noise curve is immediate. However, the return to normal happens gradually. This is the reason why the gray area has a triangular shape in **Figure 10**.

#### **Case 4.3: Water source change**

The third case illustrates what happens when a change is made to the water source. In this case, if the attributes of the water from the new source are different, the footsteps of the water source change can also be seen in the dynamic change curve.

**Figure 8.** Noise curve for sensor malfunctioning.

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**Figure 9.** Operational change - raw data.

**Figure 8** shows the resulting dynamic noise curve of the sensor's malfunctioning. As can be seen, the drop in the values of Cl and pH causes a sharp increase in the value of the dynamic noise. After a short period, when the sensors resume functioning, the value of the dynamic

Note also that if the sensors remain non-functional for a long period of time, and the algorithm stops using the values of these sensors as part of Eq. (5), the level of the dynamic noise

The gray box around the area of the event depicts the shape of the dynamic noise curve as a rectangle. This is due to the sharp change in the values of certain sensors. This sharp change can only occur during sensor failure . A chemical change in water quality cannot occur within 1 minute.

The second typical change is an *operational change*. This is defined as a situation in which one of the variables controlled by operators has been changed. Some examples of such variables may be pressure or flow. An operational change may influence the variables' quality. One such example of this type of situation is shown in **Figure 9**. The black line shows a change in the pressure (PRI) value. Shortly after this change, a peak in the Turbidity value is recorded

**Figure 10** shows the corresponding changes in the dynamic noise curve. The chart indicates that the operational change also results in the violation of the dynamic noise's "red line".

Since operational changes are also sudden changes, the peak in the dynamic noise curve is immediate. However, the return to normal happens gradually. This is the reason why the

The third case illustrates what happens when a change is made to the water source. In this case, if the attributes of the water from the new source are different, the footsteps of the water

However, this can be explained by the change in the operational variable.

curve will be lower during steady state, since less sensors are transmitting data.

noise drops back to a level below the red threshold line (0.3).

42 Applications in Water Systems Management and Modeling

**Case 4.2: Operational change**

(see red line in **Figure 9**).

**Case 4.3: Water source change**

**Figure 8.** Noise curve for sensor malfunctioning.

gray area has a triangular shape in **Figure 10**.

source change can also be seen in the dynamic change curve.

**Figure 11** shows a change in the water source. Conductivity rose from a level of 345 mS to a level of 385 mS (within 6 hours, from 10:00 am to 4:30 pm). Together with this, the pH level dropped from 8.20 to 8.07.

The effect on the dynamic noise curve can be seen in **Figure 12**. The average value of the dynamic noise curve has changed. This is due to the change in the noise level of the water measurements from the new source.

A change in water source, which can take between 30 and 60 minutes and up to several hours, will result in a change in the noise of the dynamic noise curve. Sometimes, it will also cause a change to occur in the average value of the dynamic noise.

### **Case 4.4: Contamination event**

Finally, we have the case of contamination. **Figure 13** shows raw data from a typical contamination event. The event starts with a drop in the Free Chlorine (see green line in **Figure 13**). This is due to chlorine consumption by the contaminator. Shortly after, the Turbidity level starts to rise (see blue line in **Figure 13**).

**Figure 10.** Operational change - dynamic noise curve.

**Figure 11.** Water source change - raw data.

At the same time, the chlorination dosing system reacts to the situation and the level of Free Chlorine once again rises to a normal level; and due to the diffusion factor, the Turbidity level gradually drops. The result of this event can be seen in the dynamic noise line shown in

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As seen in **Figure 14**, when the event starts, the dynamic noise curve rapidly rises above the red line of the threshold. It is fast, but not steep, as in the case of sensor malfunctioning or water source change. Once the maximum level of contamination has been obtained, the level starts to gradually drop, due to the diffusion effect, which causes the contamination to be diluted with the incoming water. This is why the right side of the curve in **Figure 14** is not symmetric to the left side of the blue curve. The overall situation creates the shape of nonsymmetric triangle, as is illustrated by the gray area in **Figure 14**. Note that the farther the contamination's penetration to the system is from the measuring point, the less non-symmet-

The current chapter demonstrates how the simple pattern recognition of a curve created by a noise capture process, similar to a random walk, can be used to classify different types of abnormal events. The presented algorithm uses the imaginary center of gravity of the water quality measurements in order to measure the noise of the process captured as traveling distance. It has been shown that the created curve has a maximum value, due to the nature of the

Four different types of abnormal events were examined: malfunctioning of sensors, operational change, water source change and contamination events. Numerical examples based on real data show that each of the events has a different "signature", which enables the identifi-

ric and the shorter the triangle will be. This is due to the dilution effect.

process. This threshold is violated when abnormal events occur.

**Figure 14**.

**5. Concluding remarks**

**Figure 14.** Contamination event - dynamic noise.

cation of the event's nature.

**Figure 12.** Water source change - dynamic noise.

**Figure 13.** Contamination event - raw data.

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**Figure 14.** Contamination event - dynamic noise.

**Figure 11.** Water source change - raw data.

44 Applications in Water Systems Management and Modeling

**Figure 12.** Water source change - dynamic noise.

**Figure 13.** Contamination event - raw data.

At the same time, the chlorination dosing system reacts to the situation and the level of Free Chlorine once again rises to a normal level; and due to the diffusion factor, the Turbidity level gradually drops. The result of this event can be seen in the dynamic noise line shown in **Figure 14**.

As seen in **Figure 14**, when the event starts, the dynamic noise curve rapidly rises above the red line of the threshold. It is fast, but not steep, as in the case of sensor malfunctioning or water source change. Once the maximum level of contamination has been obtained, the level starts to gradually drop, due to the diffusion effect, which causes the contamination to be diluted with the incoming water. This is why the right side of the curve in **Figure 14** is not symmetric to the left side of the blue curve. The overall situation creates the shape of nonsymmetric triangle, as is illustrated by the gray area in **Figure 14**. Note that the farther the contamination's penetration to the system is from the measuring point, the less non-symmetric and the shorter the triangle will be. This is due to the dilution effect.
