Journal Paper Format - SERSC

International Journal of Software Engineering and Its Applications Vol. 8, No. 12 (2014), pp. 43-54 http://dx.doi.org/10.14257/ijseia.2014.8.12.04

Decision Support System for Blending of Multiple Water Resources and System Diagnosis in Water Treatment Plant Hanbai Park and Dal-Sik Woo Korea Interfacial Science & Engineering Institute, 400-18 Nambu-daero, Dongnam-gu, Cheonan-si, 330-270, Korea [email protected], [email protected] Abstract This study developed a decision support system in a water treatment system capable of supporting the operator to make informed decisions about the best course of action for using multiple water resources. This system consists of a process operation and diagnosis to calculate historical and real-time data to optimize water blending ratios and diagnose each water treatment unit. According to the decision support system, the operator can easily organize calculated and analyzed data and as such can expect long-term operational and analytical benefits in terms of economic, social, and environmental effects in the future. Keywords: Decision support system, Multiple water use, Fuzzy algorithms, Water treatment plant

1. Introduction With recent trends towards urbanization, population growth, and global climate change, there has been a corresponding increase in the global water demand. The world is facing a water shortage, and Korea is no exception. The use of multiple water resources is one desirable solution for overcoming this problem, though the efficient use of multiple water treatment systems remains difficult due to the inherent complexities and difficulties in controlling or monitoring these systems. As such, operators often fail to make precise decisions about inputs and outcomes in complex water treatment systems. An adequate method for conquering this problem would be to develop a suitable decision support system (DSS) for the water treatment system. However, though information technology has rapidly developed and has been applied in many scientific fields, DSSs in water treatment have only been used to assist users in choosing a consistent, near optimum solution for a particular problem in a reduced time frame (Poch et al., 2004). The common purpose for a DSS has been to provide a reliable and cost-effective method for water treatment using an automatic decision process. In DSS modeling, research has focused on the application of fuzzy logic to the development of water blending and water treatment system diagnosis to solve problems related to historical and real-time data. Several DSSs have been developed in attempts to aid decision making in water treatment systems. For example, Rodríguez et al., (2002) developed a hybrid supervisory system based on data gathering and update, diagnosis, supervision, prediction, communication, actuation, and evaluation phases for wastewater treatment plants. Joksimovic et al., (2006) then exploited a treatment plan composed of a series of processes

ISSN: 1738-9984 IJSEIA Copyright ⓒ 2014 SERSC

International Journal of Software Engineering and Its Applications Vol. 8, No. 12 (2014)

that is steadily increasing, making the selection of an optimum sequence an important challenge faced by researchers. Hidalgo et al., (2007) investigated a multi-function software decision support tool for urban wastewater treatment plants that combined a decision process with the information collected in a relational database. And Delpla et al., (2014) researched a system tool for the evaluation and mitigation of drinking water quality issues resulting from environmental changes such as steroids, pharmaceuticals, pesticides, bisphenol-a, polychlorobiphenyls, polycyclic aromatic hydrocarbons, petrochemical hydrocarbons, and disinfection by-products. This tool has been tested on various European catchments and shows promising potential for informing water managers of risks and appropriate mitigative actions. In this paper, three different water treatment systems (drinking water treatment, desalination, and treated-sewage water reuse systems) are investigated for possible DSS implementation. The drinking water system consists of multi-water blending, coagulation, sedimentation, filtration, ozone, and activated carbon. The desalination system combines ultrafiltration and reverses osmosis, and the treated sewage water reuse system includes coagulation, a Youchen disk filter, and ozonation. The implementation of DSSs into the three processes could enhance the performance of each unit process, thereby producing a much safer and stable quality and quantity of water, as well as managing an economical system in terms of cost and energy savings. This research focuses on developing a user friendly decision support system capable of combining multiple water resources and the control of water treatment systems in order to satisfy both the quality and quantity demands from consumers.

2. Methodology 2.1. Pilot Plant A pilot plant was constructed in the P. water treatment plant, Incheon, South Korea. In the pilot plant, three main processes were constructed using surface water with ground water, seawater with brackish water or ground water, and treated sewage water with rain water (Figure 1). The first process is comprised of a blending tank, coagulation/flocculation, sedimentation, slow sand filtration, ozone, granular activated carbon, and chlorination for potable water generation. The second process uses a desalination system comprised of ultrafiltration (UF) and seawater reverse osmosis (SWRO) for multiple water uses. And the third process consists of coagulation/flocculation, a Yucheon disk filter (YDF), and ozonation for agricultural and industrial water production. These systems were designed to achieve appropriate water quality parameters for obtaining multiple water resources by blending. Water quality parameters were estimated in terms of TOC, NH3-N, turbidity, electric conductivity, and pH, all of which can be measured using automatic gauges. Water blending will be automatically determined by the DSS.

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Copyright ⓒ 2014 SERSC


Figure 1. Schematic Diagram Of Pilot Plant 2.2. System Development of DSS The main purpose of DSSs capable of using multiple water resources is to provide operators a quick evaluation of the blending operation and system diagnosis based on the premise of safe and sustainable water use. The DSSs used here were designed to allow different input water qualities to be blended into the desired output water quality and to diagnose each unit in the three different systems. DSSs consisted of a user interface, analysis module, report module, database module, and gateway. They were developed using MS Visual Studio C # to work on the latest Windows OS. A user chart, table, button, and text are provided to display the results of the analysis and to transmit the control command to the user. The report module provides a variety of functions for displaying the analysis results, and the analysis module is constructed using a fuzzy algorithm and mathematical model. The gateway performs real-time communication with the human–machine interface (HMI) as database module provides the ability to query large amounts of data, such as historical data. 2.2.1. Fussy Model System This software was based on well-developed fuzzy algorithms. Fuzzy systems were first introduced by Zadeh (1965). The fuzzy logic can be used to map input parameters or patterns to provide appropriate outputs using membership functions. Fuzzy logic systems predict output data from input data using a degree of membership in the fuzzy set. In water treatment systems, fuzzy model systems have been particularly useful for system diagnoses, as fuzzy logic control systems are often used for chemical injection control (Y. Yang, 2000). In this study, a fuzzy model was applied to calculate the blending operation and system diagnosis from the input data to the output data. Fuzzy logic was used to assess water quality by developing a water quality index based on a fuzzy membership function (Figure 2). Here, each of the five input quality determinants was further divided into four categories.


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Figure 2. Membership Function of Process Operation

Input ranges and parameters for water quality determinants in the fuzzy inference system vary from each of the analytical items shown in Table 1. The data was provided based on the pilot plant unit system operation configuration. Table 1. Applied Water Quality Index in the Pilot Plant Analysis Item

System

TOC (mg/L)

Low

NO3-N (mg/L)

High

Low

Turbidity (NTU)

High

Low

Conductivity (µS/cm)

pH

High

Low

High

Low

High

Process1 Normal

1

8

1

8

2

30

6.5

8

100

200

Emergency-1

8

20

8

20

30

50

6.5

8

100

200

Emergency-2

20

40

20

50

50

200

6.5

8

100

200

Process 2 Normal

1

8

1

4

3

10

6.5

8

15,000

50,000

Emergency-1

8

15

4

10

20

30

6.5

8

7,000

50,000

Emergency-2

15

25

4

10

30

50

6.5

8

5,000

50,000

Process 3

46

Normal

3

10

1

15

3

20

6.5

8

100

600

Emergency-1

10

20

15

30

20

50

6.5

8

600

1,000

Emergency-2

20

200

30

100

50

200

6.5

8

600

1,000



Depending on the number of inputs and the number of membership functions for each input, and the shape of membership function chosen, the total number of parameters can be estimated using Equation (1). (1)

where S1 is the concentration of source 1, S2 is the concentration of source 2, x is the fraction of blending, f1 is the weight parameter of source 1, f2 is the weight parameter of source 2, and Cb is the blending concentration from sources 1 and 2. 2.2.2. DSS Architecture The DSS architecture contains two main software modules and several data structures. The basic software modules of the DSSs for the pilot plant were divided into two major parts: a blending operation and system diagnosis. Each data structure consists of system components, the community context, user values, and the properties and relationships. The blending operation supports water blending and the process algorithm (Figure 3). The system diagnosis plays a vital role in diagnosing and solving problems to ensure efficient water production and error management (Figure 4).

Figure 3. Data Flow Diagram for Blending Operation


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Figure 4. Data Flow Diagram for System Diagnosis In the blending process, DSSs measure parameters ranging from input water qualities and quantities to output water qualities and quantities. Then, if the water quality is satisfactory of its designated purpose, it goes through a diagnosis process. In the diagnosis process, DSSs diagnose each unit process to ensure safe and stable operation.

3. Results and Discussion According to the purpose of use, DSSs optimize water quantity using multiple water sources, to adjust the targeted water quality in the blending tank and gather operating data during the water treatment process. With the operation data, DSSs determine a reservoir level for scheduling the water treatment process and to estimate the demand from the targeted consumers. 3.1. Blending Operation A database of the various entities in the blending operation contains information pertaining to the input water qualities and quantities for blending multiple water resources. Here, DSSs automatically communicated with the water treatment plant SCADA system to promote faster and real-time decision making. Moreover, they optimize water blending ratios by using a fuzzy algorithm based on both historical and real-time data. Figure 5 displays the process operations for the three different water systems.

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Figure 5. Screenshot Showing Blending Operations 3.2. System Diagnosis In the system diagnosis, each unit process was defined in order to create safe and stable water operation in the water treatment plant. Depending upon the input water qualities and quantities, DSS support the diagnosis of each unit process to improve the present and future decision making processes. Figure 4 presents a sample diagnosis process for the three systems.

Figure 6. Screenshot Showing System Diagnoses


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3.3. Outputs and Applications This software is able to determine the blending effluent quality and quantity from the influent qualities and quantities for different water resources, based on the specific use of the effluent being produced. The DSSs were applied to a drinking water system, desalination system and treated-sewage water reuse system for verification. 3.3.1. Surface Water with Ground Water Blending and System Diagnosis The first scenario was developed by blending the surface water with ground water, followed by a system diagnosis (Figure 7).

Figure 7. Screenshot for Scenario 1 Operation and Data Diagnosis Water was blended from surface water obtained at a rate of 398 m3/day, with quality parameters composed of 2.5 mg/L TOC, 4 mg/L NO3-N, 5 NTU turbidity, pH 7.78, and electric conductivity (EC) of 80 µS/cm, and ground water obtained at a rate of 111 m3/day composed of 1 mg/L TOC, 0.342 mg/L NO3-N, 0.8 NTU turbidity, pH 7.0, and EC of 100 µS/cm. In the blending operation, the blended water quantities and qualities for each

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analytical result were 500 m3/day for quality parameters including: 2.167 mg/L TOC, 3.18 mg/L NO3-N, 4 NTU turbidity, pH 7.6, and EC of 84 µS/cm. The drinking water system diagnosis can be divided into four different processes: coagulation, ozonation, granular activated carbon (GAC), and disinfection processes. In the coagulation process diagnosis, a coagulant dosage of 18.5 ppm, G value of 14.88/s, and setting time of 1.44 h were calculated from the blended water qualities. In the ozonation process, a 1.5 ppm ozone dosage was calculated. In the GAC process, an empty bed contact time of 14.4 min was calculated, and in the disinfection process, a chlorine dosage of 1.2 ppm was calculated. The results were obtained based on the quality parameters of the experimental results in the pilot plant. 3.3.2. Seawater with Brackish Water Blending and System Diagnosis This scenario was established based on seawater with brackish water blending, and it was verified using results from the pilot plant (Figure 8).

Figure 8. Screenshot for Scenario 2 Operation and Data Diagnosis The results show that water was blended from sea water at 386.5 m3/day, at quality parameters of 1.5 mg/L TOC, 3 mg/L NO3-N, 4 NTU turbidity, pH 7.8, and EC of 45,000 µS/cm, and brackish water at 113.5 m3/day at quality parameters of consisting of 2 mg/L TOC, 6 mg/L NO3-N, 7 NTU turbidity, pH 7.6, and EC of 23,000 µS/cm. In the blending


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operation, blended water quantities and qualities for each analytical result were 500 m3/day at 1.614 mg/L TOC, 3.68 mg/L NO3-N, 4.7 NTU turbidity, pH 7.7, and EC of 40,000 µS/cm. The desalination system diagnosis can be divided into two processes: UF and SWRO processes. In the UF process diagnosis, a permeate flow of 340 LPM, applied pressure of 0.27 bar, and backwashing cycle of 60 s were calculated. In the SWRO process diagnosis, a permeate flow of 102 LPM, and applied pressure of 50 bar were calculated from the blended water qualities. 3.3.3. Treated Sewage Water with Rain Water Blending and System Diagnosis In this scenario, treated sewage water and rain water were used for the blending operation and the system diagnosis model was based on a blending ratio of 5:5 (Figure 9).

Figure 9. Screenshot for Scenario 3 Operation and Data Diagnosis In the blending operation, water was blended from treated sewage water at 250 m3/day, with water quality parameters consisting of 5 mg/L TOC, 12 mg/L NO3-N, 9 NTU turbidity, pH 7.4, and EC of 500 µS/cm, and rain water at 250 m3/day composed of 2 mg/L TOC, 0.3 mg/L NO3-N, 3 NTU turbidity, pH 7.6, and EC of 90 µS/cm. In the blending operation, blended water quantities and qualities for each analytical result were 500 m3/day with quality parameters being 3.5 mg/L TOC, 6.150 mg/L NO3-N, 6 NTU turbidity, pH 7.5, and EC of

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295 µS/cm. The treated sewage system diagnosis can be divided into three processes: coagulation, YDF, and advanced oxidation processes. In the coagulation process diagnosis, a coagulant dosage of 25 ppm, and G value of 14.41/s was calculated based on the blended water qualities. In the YDF process, a flocculation velocity for rapid mixing of 120 rpm and a slow mixing velocity of 35 rpm were calculated. In the ozonation process, a 1.2 ppm of ozone dosage was calculated. The results were then compared to the experimental results in the pilot plant and found to be reasonable.

4. Conclusion The purpose of this model was to provide support for determining water blending ratios and system diagnoses. The DSS model developed was able to estimate the water quality parameters from different input water resources to output appropriate water resources after blending and to diagnose each water treatment unit for optimal operation. The subsequent validation of the DSS model confirmed that the model is sound and effective for use, based on the pilot plant designed in Figure 1. The DSS model was then implemented in the pilot plant and found to be adequate for blending operation and system diagnosis. The validation data was shown in Figure 7, 8, 9. This paper presents a DDS capable of supporting the operator to make informed decisions for three water treatment systems using multiple water resources. The software developed in this paper used fuzzy logic concepts and algorithms, which have been proven to be effective tools for water treatment plant operation and diagnoses. The developed software was implemented in the operation of a pilot plant and proved valid for blending multiple water resources and diagnosing each unit process for the three water systems. According to this DSS, the operator can immediately organize calculated data and data analyzed from real-time and historical data. Therefore, long-term operations using this software will result in economic, social and environmental benefits in the water treatment plants in the near future.

Footnotes This paper is a revised and expanded version of a paper entitled [Decision support system for multiple water resource blending and drinking water treatment] presented at NGCIT 2014, Liberty Central Saigon Hotel, Hochimin, Vietnam during October 24-26, 2014.

Acknowledgments This research was supported by a grant (KW-14-SWG) from the Smart Water Grid funded by the Ministry of Land, Transport and Maritime Affairs of the Korea Government.

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Authors Hanbai Park, Senior researcher in Korea Interfacial Science & Engineering Institute Postdoctoral Associate in Hanyang University, South Korea Civil PhD from Oklahoma State University in USA Chemical Engineering MS from Oklahoma State University in USA

Dalsik Woo, Principle research engineer in Korea Interfacial Science & Engineering Institute Secretary in Environmental & Human Forum, South Korea Adversary in Mistry of Environment, Mistry of Land, Infrastructure and Transport, Mistry of Knowledge Economy, South Korea

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