Intelligent Decision Support System (DSS) Software for System Operation and Multiple Water Resources Blending in Water Treatment Facilities

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1 , pp Intelligent Decision Support System (DSS) Software for System Operation and Multiple Water Resources Blending in Water Treatment Facilities Dal-sik Woo *, Sung-Hoon Shin, Hak-Soo Lee, Jaekyeong Lee Korea Interfacial Science & Engineering Institute, Nambu-daero, Dongnam-gu, Cheonan-si, , Korea Abstract. In this paper, we presented the developed decision support software for water treatment facilities. This software are used for blending of multiple water resources and system management to support the operator to make best decision and action for water treatment facilities. This system is composed of a source selection and operation management parts for operation support by calculating time-series data and optimizing the blending process. By using this software, the operator can make best decision and take economic, environmental benefit related with water treatment facilities. Keywords: Decision support software, Blending of multiple water resources, Water treatment facilities 1 Introduction Recently, According to the population/economic growth, the global water demand are increasing. We are facing a usable water resources shortage. The use of multiple water resources is available way to solve this problem. Surely, the efficient selection and blending of multiple water resources are difficult due to the matrix effect of water and difficulties of management and control of water treatment facilities. As such, operators often are pushed to make best decision about operation of water treatment systems. To solve this problem, various methods are developed like decision support system (DSS) for water treatment system. But, though this kind of statistical technology has fast developed and applied in various fields for operation and management of plant, DSSs in environmental parts have just been used as scientific tool or near optimum solution for a particular problem (Poch et al., 2004). The DSS software need to provide a suitable and costeffective solution for water treatment facilities with an automation and real-time simulation (Hidalgo et al., 2007). In this paper, we present the DSS software for various water resources and three water treatment process (drinking water treatment, desalination, and treated-sewage water reuse systems) and investigated for DSS implementation in water treatment facilities. The implementation of this software in three ISSN: ASTL Copyright 2015 SERSC

2 processes can provide a much safer and suitable quality and quantity of water with cost and energy saving. 2 Methodology 2.1 Water Treatment Processes in Pilot Plant A pilot plant is made up with three main processes to treat surface water with ground water, seawater with brackish water or ground water, and treated sewage water with rain water. The first process is comprised of coagulation/flocculation, sedimentation, sand filtration, ozone, granular activated carbon (GAC), and chlorination. The second process is comprised of ultrafiltration (UF) and seawater reverse osmosis (SWRO). And the third process consists of coagulation/flocculation, disk filter, and ozonation. 2.2 Software development of DSS for system operation and management The main purpose of DSS software is to provide suitable information for operators. So, fast time-series analysis and evaluation of the blending operation process are need for the premise of safe and sustainable water use. The DSS software were designed to Blending different water resources and make the desired treated water quality and quantity for each unit in the three different processes. DSSs are composed of a user interface, analysis module, report module, database module. These were developed by MS Visual Studio C #. A user interface is made up with user chart, table, button, and text to display the analytical results and control commend for operator. Based on this DSS with operation and management, coagulation dosing are supported for operator. Figure 1 shows the flow chart for support algorithm of coagulation dosing. Coagulation dosing can be optimized by 3 method like optimum value setting, ph/turbidity based optimization, and regression function. Copyright 2015 SERSC 205

3 Fig. 1. Coagulation Dosing Algorithm 2.3 Fussy Algorithm for Blending of Water Resources This DSS software s main algorithm is well-developed fuzzy algorithms. This fuzzy algorithm can analysis the time-series and patterns to provide appropriate result and decision (Zadeh et al., 1965; Y., Yang, 2000). In this water treatment system, fuzzy algorithm is used to determine blending ratio of various water resources and diagnosis of system operation. For the implementation of this algorithm, TOC, ammonia, nitrate, turbidity, conductivity, and ph are selected as suitable water quality parameters. Fuzzy algorithm is used to assess water particular water quality by developing a water quality index based on fuzzy membership function. Input ranges and parameters for water quality determinants in this DSS software vary based on water quality parameters. Table 1 shows the applied water quality indexes. Based on these water quality indexes and fuzzy membership function, the number of parameters are predicted. Table 1. Applied Water Quality Indexes for this DSS software TOC Nitrate Ammonia Turbidity ph Conductivity System Hig Low High Low Low High Low High Low High Low High h Process 1 Normal Emerge ncy 206 Copyright 2015 SERSC

4 Process Normal Emerge ncy 0 Process 3 Normal Emerge ncy 3 Results and Discussion 3.1. System Operation & Management In the system operation management, each unit processes and their operation parameters are presented for safe and stable system operation. Depending on the influent water quality and quantities, DSS software support, diagnosis the each unit processes, and coagulation dosing can be optimized. Figure 2 presents a system operation & management screen for the three systems. Fig. 2. Screen of System Operation & Management Copyright 2015 SERSC 207

5 3.2. Determination of Blending Ratio For determination of blending multiple water resources, real-time influent water quality, quantity data are used. This DSS software communicates with pilot plant SCADA system for real-time decision making. By using this system, DSS software present optimum water blending ratios by using a fuzzy algorithm. Figure 3 shows the display of multiple water resources blending process for the three different water treatment systems. Fig. 3. Screen of Multiple Water Resources Blending Process 4 Conclusion In this study, we present the DSS software for operation management and multiple water blending of water treatment facilities. The developed DSS software can predict the water quality parameters for water resources blending and support system operation and management for each water treatment unit processes. And then, according to this DSS, the operator can operate and management the water treatment facilities. Therefore, long-term operations by DSS software will result in economic and environmental benefits in the multiple water resources blending & water treatment facilities in near future. This DSS software will be implemented in the pilot plant and evaluated for adaptable of this software. 208 Copyright 2015 SERSC

6 Acknowledgments. This research was supported by a grant (12-TI-C01) from the Advanced Water Management Research Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean government. References 1. M. Poch, J. Comas, I. Rodriguez-Roda, M. Sanchez-Marre and U. Cortes, Designing and building real environmental decision support systems, Environ. Modell. Softw., 19 (2004) 2. D. Hidalgo, R. Irusta, L. Martinez, D. Fatta and A. Papadopoulos, Development of a multifunction software decision support tool for the promotion of the safe reuse of treated urban wastewater, Desalination, 215 (2007) 3. L. Zadeh, The concept of a linguistic variable and its application to approximate reasoning I, Inform. Sci., 8 (1975) 4. Y. Yang, Integrating environmental impact minimization into conceptual chemical process design a process system engineering review, Comput. Chem. Eng., 24 (2000) Copyright 2015 SERSC 209