KR101020943B1 - MIB control decision support system - Google Patents

Abstract

The present invention relates to a taste and smell control decision support system for selecting an optimal water treatment process in consideration of the water quality conditions and design factors in controlling the MIB, which is the main cause of taste and smell substances in water treatment.

Taste smell control decision support system according to the present invention is a system for supporting the decision of process selection for taste control, water quality conditions, design factors, target concentration required for the process for taste odor control A decision condition input unit 100 to which decision condition information including a) is input; According to the decision condition input unit 100 inputted through the decision condition input unit 100, modeling processing for each process for taste control is performed to calculate processing costs for each process, and the calculated processing cost is analyzed to optimize the process. An optimal process selection processing unit 200 for selecting a; And a result analysis output unit 300 which gives priority to each process selected by the optimum process selection processing unit 200 and outputs the process. The optimal process is provided by integrating process modeling and cost analysis within a system. Select processes to support decision making.

Taste, MIB ((methylisoborneol), Decision Making, Water Treatment, Water Quality, Advanced Water Treatment

Description

Taste Control Decision Support System {MIB control decision support system}

The present invention relates to a taste control decision support system, and in particular, to control the MIB, which is the main cause of taste odor substances in water treatment, to control the taste odor control to consider the optimal water treatment process considering water quality conditions and design factors. A decision support system.

Taste and Odor Compounds are recognized as one of the biggest problems water utilities face when treating water. Most of the taste-odorous substances are known to be caused by the metabolic process of microorganisms, and because they are not health hazards, they are defined as Secondary Standard, which means Recommendation around the world. Miso (methylisoborneol) is a representative odorous substance that causes soil and mold odors and is known as algae by-products. Therefore, it occurs mainly in surface water treatment, and the Odor Threshold Contration (OTC), which can be sensed by humans, is about 10ng / L, and even if a very small amount is present in the water, the smell can be detected. It is characterized by the difficulty of treatment in general water treatment process.

In general, the frequency, duration, and peak concentration of MIB have been reported to vary regionally and seasonally. According to a recent survey conducted by the American Water Association Research Foundation (AWWARF) of about 100 water purification plants in the United States (Graham et al, 2000), the frequency is about two times a year on average. Duration of occurrence by frequency was about 2 weeks. In addition, the average occurrence concentration is about 11ng / L, and there is a case where a high concentration of up to 1,700ng / L occurs in some regions because of regional variation. According to the research results so far, the removal of taste odorous substances (MIB) includes granular activated carbon (GAC) process, powder activated carbon (PAC) process, ozone (O ₃ ) process, advanced oxidation (UV / H ₂ O _{2 )} process, PAC / It is reported that advanced water treatment processes such as UF hybrid process, biofiltration process, and nanomembrane process are required. However, these processes are not only expensive but also expensive to maintain, and thus, optimal process selection is required in consideration of inflow concentration, generation cycle, and water quality. For example, in general, if the odor-generating characteristic is continuous loading throughout the year and high level influent concentration is introduced, the granular activated carbon (GAC) process is effective, while the generating characteristic is intermittent loading and low concentration (Low). In the case of level influent concentration, the powder activated carbon (PAC) process is known to be more efficient than the granular activated carbon (GAC) process.

Thus, granular activated carbon (GAC) process, powder activated carbon (PAC) process, ozone (O ₃ ) process, advanced oxidation (UV / H ₂ O _{2 )} process, PAC / UF Hybrid process The optimal process should be selected according to the characteristics of taste odorous substances in biofiltration process, nanofiltration process, etc. Conventionally, the characteristics such as inflow concentration, development cycle, and water quality of taste odorous substances are determined. A comprehensive decision support system could not be provided to derive optimal process selection, taking into account comprehensively.

In addition, granular activated carbon (GAC) process, powder activated carbon (PAC) process, ozone (O ₃ ) process, advanced oxidation (UV / H ₂ O _{2 )} process, PAC / UF Hybrid process, There was a problem that it was difficult to select an optimal taste and smell removal process because a comprehensive decision system was not provided for the optimal process selection reflecting the investment cost and maintenance cost for the biofiltration process and the nanomembrane process.

Accordingly, the present invention has been proposed to solve the difficulty of selecting a process for removing a taste of smell in the prior art, and an object of the present invention is to remove water and smell, and to generate water quality conditions and design factors, etc. It is to provide a taste control decision support system that supports the selection of the optimal process considering this.

The taste smell control decision support system according to the present invention for achieving the above object is a system for supporting the decision of process selection for taste control, water quality conditions and design factors required for the process for taste smell control, A decision condition input unit for inputting decision condition information including a target concentration; According to the decision condition information input through the decision condition input unit, the modeling process for each process for taste control is performed to calculate the processing cost for each process, and the optimized process is selected by analyzing the calculated processing cost. An optimal process selection processing unit; And a result analysis output unit to give priority to each process selected by the optimum process selection processing unit and output the priority process.

The decision condition input unit includes a taste smell generation characteristic input module to which taste smell generation characteristic information including an inflow MIB concentration, a MIB generation cycle and a duration is input, and set target information including a user-defined effluent MIB treatment concentration. A set target input module being input; It includes a water quality / design factor input module in which water quality / design factor information including TOC, pH, and temperature is input as a water quality / design factor, and a consideration input module in which consideration information including process-specific constraints is input. .

The optimal process selection processor may include a process modeling module for calculating design factors and operating conditions required to satisfy a set target input through a decision condition input unit, and a cost analysis module for performing a cost analysis based on the process result of the process modeling. And an optimization module for selecting an optimization process that meets the conditions of the decision condition input unit through the cost analysis processed through the cost analysis module.

The result analysis output unit outputs a modeling result output module displaying design factors and operating conditions processed through the process modeling module of the optimal process selection processing unit, and a cost analysis result output module displaying cost analysis results processed through the cost analysis module. And a priority process selection output module displaying a priority process selection result processed through the optimization module.

The priority process selection output module displays the installation cost, the maintenance cost, and the total cost for each process, and the priority process for the total cost is preferably displayed sequentially.

Process modeling is performed through the process modeling module, including granular activated carbon (GAC) process, powder activated carbon (PAC) process, ozone (O ₃ ) process, biofiltration process, advanced oxidation (UV / H ₂ O). ₂ ) process, PAC / UF Hybrid process, nano-membrane (Nanomembrane) process is included.

The taste control decision support system according to the present invention provides an integrated process modeling and cost analysis within a system to select an optimal process in establishing a strategy for selecting a process for taste control in a water purification plant. It has the effect of supporting decision making.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is an overall configuration diagram of a taste control decision support system according to an embodiment of the present invention, Figure 2 shows a detailed configuration diagram of the taste control decision support system.

1 and 2, the taste control decision support system according to the present invention (MIB-control Decision Support System, hereinafter abbreviated as 'MDSS') is a decision that the process conditions for the decision is entered A condition selection unit 100 and an optimal process selection processing unit 200 for selecting an optimal process by performing modeling and cost analysis for each process according to the conditions of the decision condition input unit 100, and the optimum process selection processing unit ( And a result analysis output unit 300 for analyzing and outputting the process modeling and cost analysis results of 200).

In the MDSS according to the embodiment of the present invention, water quality conditions, design factors, target concentrations, and the like are used as input values of the decision condition input unit 100, and the target value is the target value of the result analysis output unit 300. Appropriate doses, regeneration cycles, etc. are provided for realizing the values. MDSS also provides inter-process priorities through cost analysis under equal conditions (same inflow concentrations and target concentrations) to assist in decision making for optimal process selection at the planning stage. It is composed.

The decision condition input unit 100 may include a taste smell generation characteristic input module 110 to which decision condition information such as inflow MIB concentration, MIB generation period and duration are input, and a user-defined effluent MIB treatment concentration. Set target input module 120 for inputting information, water quality / design factor input module 130 for inputting water quality / design factor information such as TOC, pH, temperature, and other process constraints Considerations are made including the input module 140.

The optimal process selection processing unit 200 includes a process modeling module 210, a cost analysis module 220, and an optimization module 230. The process modeling module 210 calculates design factors and operating conditions required to satisfy the set target based on the information input through the decision condition input unit 100, and the cost analysis module 220 processes the process modeling. The cost analysis is performed based on the result, and the optimization module 230 selects an optimization process suitable for the conditions of the decision condition input unit 100 based on the cost analysis result.

Processes processed in the process modeling module 210 are granular activated carbon (GAC) process, powder activated carbon (PAC) process, ozone (O ₃ ) process, advanced oxidation (UV / H ₂ O _{2 )} process, PAC / UF Hybrid process The process consists of seven processes, namely, biofiltration process and nanomembrane process. The selection of each process model evaluates the applicability of the algorithm of the previous model and the next model is further modified or newly developed. Was applied. In the embodiment of the present invention, the granular activated carbon (GAC) model was applied to the model developed by Kim in 2006, and the powdered activated carbon (PAC) model was applied to the power of the plant to the previous model developed by Graham et al. (2000). It has been modified to take account of its peculiarities (Cho, 2007). The ozone (O ₃ ) model and the highly oxidized (UV / H ₂ O ₂ ) process (Rosenfeldt et al., 2005) are applied to the existing model, and the biofiltration model (Meyer et al., 2005) The characteristics of temperature, intermittent load and media type were considered (Chae et al., 2006) and modified. The nanomembrane process model was applied to K-water (2004).

Cost analysis through the cost analysis module 220 is applied to the cost model that was developed in USEPA (Malcolm Pirnie, 2000), the cost analysis module 220 is applied to integrate the following existing cost models. 1) The Very Small Systems Best Available Technology Cost Document (VSS model; Malcolm Pirnie, 1993), 2) The Water Model (Culp / Wesner / Culp, 1984), 3) The Water Cost Model (Culp / Wesner / Culp, 1994 ). The cost analysis model 220 deals with capital, operation and maintenance (O & M) costs for several processes, with capital costs ranging from 0.024 to 430 MGD flow and O & M costs ranging from 0.0056 to 270 MGD. It became.

The result analysis output unit 300 is processed through a modeling result output module 310 and a cost analysis module 220 in which a process modeling result processed by the process modeling module 210 of the optimum process selection processing unit 200 is displayed. The process ranking output selection module 330 which displays the priority based on the process selected by the cost analysis result output module 320 and the optimization module 230 in which the cost analysis result is displayed, and other recommendations and a recommendation / comment output module 340 for providing information such as a recommendation or an opinion. In case of cost analysis, if the set target and water quality conditions are different between each process, it is provided on the result screen so that the user can check it, and the priority of the process is selected based on the total cost by introducing the concept of relative cost. do.

In the embodiment of the present invention, the MSDD development uses a Visual Basic Application (VBA) linking MS-EXCEL and a Visual Basic program, and a user interface (GUI) between the decision condition input unit 100 and the result analysis output unit 300. ) Is composed of Visual Basic, and process modeling, cost analysis, and optimization processing of the optimal process selection processing unit 200 algorithm are performed in Excel, and they are designed to be easily accessed by users by linking them with mutual menu method. .

3 shows an example of an initial screen of MDSS according to an embodiment of the present invention.

As shown in FIG. 3, seven processes (granular activated carbon (GAC) process, powdered activated carbon (PAC) process, ozone (O ₃ ) process, advanced oxidation (UV / H ₂ O ₂ ) process) are shown on the left side of the MDSS initial screen. Modeling button 10 for selecting PAC / UF Hybrid process, Biofiltration process, and Nanomembrane process, and process modeling and cost analysis button and comparison for decision support. The decision support button 20 is provided, and on the right side, each process selection button 30 for selecting a comparison target is provided.

In addition, a flow rate value input menu 40 is provided at the upper left of the screen. When a predetermined value is input to the flow rate value input menu 40, seven process flow rate values are applied to the same value. The efficiency evaluation of the process is made.

4 is a flowchart of the overall processing of MDSS according to an embodiment of the present invention.

Steps S110 and S120: First, after the system is turned on (S110), a process of performing modeling processing and cost analysis among the seven modeling buttons 10 displayed on the left side of the menu displayed on the MDSS initial screen of FIG. 3 is performed. Select (S120).

Steps S130 and S140: After the modeling process is selected, if the process modeling process and cost analysis button 20 is selected from a menu located in the middle for modeling processing and cost analysis of the process (S130), the process modeling is performed and the cost analysis is performed. This is performed so that the processing result is displayed on the screen (S140).

Step S150: The user who confirms the process modeling process result and the cost analysis for each process, first selects a process to be compared to the process for decision support on the right side of the screen in order to select an optimized process for eliminating the odor. The selection is made at 30. When the process selection button 30 is selected, two or more processes may be selected for comparison.

Steps S160 and S170: If a plurality of processes are selected in the above process and then the comparison / decision support button 20 located in the middle of the screen is selected (S160), the comparison is made according to the cost analysis of the selected processes and the optimized process is displayed on the screen. It becomes (S170). In the result screen according to the cost analysis comparison of each process, the installation cost, the maintenance cost, and the total cost of the comparison process are presented, and finally, the priority process with respect to the total cost is sequentially presented.

Hereinafter, each process processed by the MDSS optimal process selection processing unit 200 will be described.

< Granular activated carbon ( GAC ) Process>

5 shows a flowchart of a granular activated carbon (GAC) process according to an embodiment of the present invention.

As shown in FIG. 5, granular activated carbon (GAC) process model calculation is performed by dividing process modeling and cost analysis.

In process modeling, the input module of granular activated carbon process is classified into 4 groups of input data groups.In the first group, the input MIB concentration, MIB generation cycle (F) and generation period (D) are inputted into the taste and smell generation characteristics data. In the 2nd group, the user-defined effluent MIB treatment concentration (eg 5ng / L, 10ng / L, 20ng / L) is input to the treatment target setting.In the 3rd group, the influent TOC concentration and the granular activated carbon process Activated carbon filter paper (activated carbon filter paper (FA-GAC), which replaces existing sand and anthracite media with GAC), activated carbon sorbent paper (activated carbon sorbent paper (PFA-GAC) installed behind existing filter paper), tower contact time (EBCT) Activated carbon size is entered, and granular activated carbon operating time (T) is entered before MIB generation as a process-specific constraint in other considerations in Group 4.

After input, when the process starts, it is determined whether there was an operation time of the granular activated carbon process before the taste odor event occurs.If the operation time corresponds to 'Yes', it is determined whether there is a continuous load of MIB. If yes, the granular activated carbon life is stored as 0. If no, the granular activated carbon life is stored as 12 minus the generation period (F) times the generation period (D).

In this step, if the O time is 'no', it is determined whether there is a continuous load of the MIB. If yes, the granular activated carbon life is stored as the granular activated carbon operating time (T). Is stored as the granular activated carbon operating time plus 12 minus the occurrence period (F) times the occurrence period (D).

Then, to calculate the support volume to the treatment target, the model performs the following equation.

Where BV _x = support volume from granular activated carbon to the predicted breakthrough point,

α ₁ = GAC age factor,

β ₁ = granular activated carbon particle size coefficient,

TOC = total organic carbon concentration (mg / L),

MIB = MIB Concentration (ng / L),

After calculating the support volume to the treatment target, the model performs the following equation to obtain the granular activated carbon operating cycle.

Through the above process, the ground volume and the operating cycle to the treatment target are calculated, and then the flow enters the cost analysis step and the inflow flow is input.

After the inflow flow rate is input, it is determined whether the existing water purification plant or the new water purification plant is inputted, and if the existing water treatment plant is an existing regeneration frequency (RF), only the operation & management cost is calculated according to the granular activated carbon operation cycle. The maintenance cost is zero if the operation cycle is longer than the existing regeneration cycle, and the maintenance cost is calculated if the existing regeneration cycle is greater than the operation cycle.

In this step, if it is determined that the existing water purification plant or a new water purification plant, it is determined whether the new water purification plant is activated carbon adsorption paper or activated carbon filter paper.

If the new water treatment plant is an activated carbon sorbent, the calculation is performed according to the following conditions.

-Installation cost is calculated according to the tower stay time and flow rate.

-Maintenance costs are calculated according to the granular activated carbon operating cycle (paper life).

-"New turret contact time" is calculated according to the optimization.

-We present improved opinion for existing tower contact time.

In this step, if the new water purification plant is activated carbon filter paper, it is calculated according to the following conditions.

-Installation cost is calculated based on the amount of initial granular activated carbon.

-Maintenance costs are calculated according to the granular activated carbon operating cycle (paper life).

FIG. 6 illustrates an example of an input screen of granular activated carbon (GAC) process modeling performed according to the granular activated carbon (GAC) process flowchart of FIG. 5.

When selecting the granular activated carbon (GAC) process from the modeling button 10 for the process provided on the left side in the initial screen of FIG. 3, and selecting the cost analysis / execution button 20 for decision support provided in the middle The Granular Activated Carbon (GAC) process input screen of FIG. 6 appears.

As shown in FIG. 6, four groups of input windows are activated on the input screen, a taste smell characteristic input window is located at the upper left side, a set target input window at the lower left, and a water quality / design factor input window at the upper right, a lower right corner. There is an input window for other considerations.

The inflow MIB concentration, MIB generation period and duration are input to the taste and smell generation characteristic input window, and the effluent MIB treatment concentration defined by the user is selected and input among 5, 10 and 20 in the set target input window. The influent TOC concentration is input to the water quality / design factor input window, and one of activated carbon filter paper and activated carbon adsorption paper is selected. If the activated carbon filter paper is selected, the tower contact time and activated carbon particle size are selected and designated on the next screen (not shown), and the tower contact time and activated carbon particle size are selected and specified on the next screen even when activated carbon sorbent paper is selected. In addition, in the other consideration input window, the number of granular activated carbon operation months is input before the MIB is generated as a process-specific constraint.

After the input process is finished, press the left process model operation button at the bottom to go to the next step, press the return to the main menu on the right to go to the previous step.

FIG. 7 illustrates an example of an output screen of the granular activated carbon (GAC) process modeling according to the input of FIG. 6.

As shown in FIG. 7, two groups of output windows appear on the output screen of the granular activated carbon (GAC) process modeling, and the modeling factor value selected in the input window appears on the left side of the screen, and the modeling result is presented on the right side of the screen. The selected modeling factor on the left side of the screen displays the influent MIB concentration, the effluent treatment target MIB concentration, the selected tower contact time (EBCT), the selected granular activated carbon particle size, and the selected granular activated carbon operating cycle. The number of support volumes up to the effluent MIB concentration and the calculated granular activated carbon operating cycle are displayed.

After the output process is finished, press the left doctor support analysis button at the bottom to go to the next step, press the return to the main menu to the right to go to the previous step.

8 shows an example of an input screen of granular activated carbon (GAC) process cost modeling.

As shown in FIG. 8, the input screen of the granular activated carbon (GAC) process cost modeling is a GAC process cost analysis-input screen, and is composed of three groups of input data, cost analysis results, and recommendation opinion. In the input data group, the design flow rate is input in m3 / day, and whether the granular activated carbon system is an existing facility is selected as 'yes' or 'no'. As a result of cost analysis, the group shows the tower contact time value selected in the previous step, and the optimal tower contact time value obtained through the model is presented. The comment group compares the results of the cost analysis model with the selected design factor values.

After the above process, press the left cost model operation button at the bottom to go to the next step, and press the return to the main menu on the right to go to the previous step.

Through this process, granular activated carbon (GAC) process modeling is performed.

<Powdered activated carbon ( PAC ) Process>

9 shows a flowchart of a powder activated carbon (PAC) process according to an embodiment of the present invention.

As shown in FIG. 9, powder activated carbon (PAC) process model calculation is performed by dividing process modeling and cost analysis.

In the process modeling, the input modules of the powder activated carbon (PAC) process are classified into four groups of input data groups. In the first group, the inflow MIB concentration, MIB generation cycle (F) and generation period (D) The effluent MIB treatment concentration (eg, 5ng / L, 10ng / L, 20ng / L) defined by the user is input to the treatment target setting in Group 2, and the influent TOC concentration and powder activated carbon in water quality and design factors in Group 3 are inputted. The residence time (HRT), powder activated carbon treatment capacity (adsorption capacity) values are entered, and the use of chlorine at the injection point of powder activated carbon is entered as a constraint for other processes in Group 4.

Once entered, the next step is to calculate the EBC concentration of the influent by the following equation.

Where TOC = dissolved organic carbon concentration (mg / L).

Next, the kinetic reduction factor R _K is calculated according to the following formula.

Here, HRT = powdered activated carbon residence time (minutes).

Next, it is judged whether chlorine is used together with the powdered activated carbon, and if it is used together with the powdered activated carbon, the amount of chlorine injected is input, and the chlorine reduction factor R _cl is calculated.

After the chlorine reduction factor R _cl is calculated, the total reduction factor R _T is calculated according to the following formula.

After the total reduction factor R _T is calculated, the set target concentration of the modified MIB is calculated according to the following equation.

Where C _e = Effluent MIB concentration (ng / L), R _T = Total reduction factor.

After the set target concentration of the modified MIB is calculated, the model is performed with the modified treatment target concentration as follows.

-The model is simulated for the effluent MIB concentration compared to the powdered activated carbon injection volume.

-Find the powder activated carbon injection amount according to MIB treatment goal.

-Determine the amount of powdered activated carbon injected by the interference method.

After the model is performed, it is determined whether the injected amount of powder activated carbon exceeds 60 mg / L, and if it exceeds 60 mg / L, it is determined that the powder activated carbon process is not applicable to the next step.

If it does not exceed 60 mg / L in this step, the cost analysis step of 'no' is performed.

In the cost analysis step, the annual average powder activated carbon usage is calculated as follows according to the input flow rate.

-Calculate the frequency of taste smell (MIB) generation (F × D).

The total amount of activated carbon is calculated according to the following formula.

Total PAC = PAC Injection × Flow × Generation Cycle (F × D)

When the total amount of activated carbon (PAC) is calculated, it is determined whether the existing water purification plant is a new water purification plant. If there is an existing water purification plant, only the maintenance cost is calculated according to the total amount of activated carbon required to control the taste and smell substances during the occurrence. Done.

In this step, if it is a new water purification plant, the cost is calculated as follows.

-Installation cost is calculated according to HRT and flow rate.

Maintenance costs are calculated according to the total activated carbon required for the control of taste odor during the event.

FIG. 10 illustrates an example of an input screen of powder activated carbon (PAC) process modeling performed according to the powder activated carbon (PAC) process flowchart of FIG. 9.

In the initial screen of FIG. 3, the powder activated carbon (PAC) process is selected from the modeling button 10 for the process provided on the left side, and the cost analysis / execution button 20 for decision support provided in the middle is provided. When selected, the powder activated carbon (PAC) process input screen of FIG. 10 appears.

As shown in FIG. 10, four groups of input windows are activated on the input screen. The inflow MIB concentration, MIB generation cycle and duration are inputted into the taste-smelling characteristic input window on the upper left side, and the set target input window on the lower left side is input. User-defined effluent MIB treatment concentration is selected from 5, 10, 20 and inputted.

In addition, the influent TOC concentration is input in the water quality / design factor input window at the upper right, the powdered activated carbon hydraulic retention time is input in minutes, and the powder activated carbon adsorption capacity is selected.

In the lower right, 'Yes' and 'No' are selected depending on whether or not chlorination is carried out together with the current activated carbon in the other considerations input window.

After the input process is finished, press the left process model operation button at the bottom to move to the next step, press the return to the main menu on the right to move to the previous step.

Figure 11 shows an example of the output screen of the powder activated carbon (PAC) process modeling.

As shown in FIG. 11, five output windows appear in the output screen of the powder activated carbon (PAC) process modeling, and the modeling factor values selected in FIG. 10 are displayed on the left side of the screen, and the modeling results performed are displayed on the right side of the screen. .

At the top of the group of model factors on the left side of the screen, the selected influent MIB concentration, effluent treatment concentration, and powdered activated carbon hydraulic retention time are displayed.In the middle of the screen, distilled water Freundlich factor, K value and 1 / n value are used. At the bottom, the modeling factor values used are EBC K, EBC 1 / n, and EBC initial concentration.

At the top of the modeling results group on the right side of the screen, the selected removal rate and the required amount of activated powdered carbon are displayed, with comments on the results below.

After the above process, press the left doctor support analysis button at the bottom to go to the next step, press the return to the main menu on the right to go to the previous step.

<Ozone ( O ₃ ) Process>

12 shows a flowchart of an ozone (O ₃ ) process according to an embodiment of the invention.

As shown in FIG. 12, ozone (O ₃ ) process model calculation is performed by dividing process modeling and cost analysis.

In process modeling, the input module of ozone process is classified into 4 groups of input data groups, and the input MIB concentration, MIB generation cycle (F) and duration (D) are input to taste and smell generation characteristics data in group 1, In the 2nd group, the user-defined effluent MIB settling concentration (e.g. 5ng / L, 10ng / L, 20ng / L) is input to the setpoint setting.In the 3rd group, the influent TOC concentration, pH and alkalinity, Ozone contact time, water temperature are entered, and bromide concentration is entered as a constraint for other processes in Group 4.

Once the conditions of the ozone process are entered, the ozone dose required for MIB control is determined as follows.

Calculate residual ozone at 30 seconds to determine initial ozone dose.

-Simulate the effluent MIB concentration versus ozone injection by model.

-Obtain ozone injection amount according to treatment target MIB concentration.

In addition, the final ozone injection amount to remove the MIB in consideration of the temperature effect is determined as follows.

-Final ozone dose = ozone dose / water temperature factor obtained above

Simultaneously with the above procedure, the ozone dose for CT regulations (for disinfection purposes) is calculated as follows.

 -Select CT according to the water temperature.

 -Calculate residual ozone amount according to CT.

Calculate the ozone dose based on the residual ozone dose obtained above.

When the ozone injection amount calculation is completed, the flow rate is moved to the cost analysis step and the inflow flow rate is input.

When the inflow flow is input, it is determined whether the ozone injection amount (O _{3 , MIB} ) for the _MIB is greater than the ozone injection amount (O _{3 , CT} ) for the _CT . If it is determined to be less than 10 ug / L, if no, the ozone process is not applicable. If the bromate concentration in the step is less than 10ug / L 'yes', it is determined whether the existing water purification plant or a new water purification plant, if the existing water purification plant is a maintenance fee for MIB only during the generation of taste and smell substances It is the difference between ozone injection amount (O _{3 , MIB} ) minus ozone injection amount (O _{3 , CT} ) for _CT , and if it is a new water treatment plant, installation cost is calculated based on HRT and flow rate, and maintenance cost is MIB during taste and smell substance generation. It is calculated as the ozone injection amount (O _{3 , MIB} ) for, while it is calculated as the ozone injection amount (O _{3 , CT} ) for CT while no taste odor is generated.

In the step of determining whether the ozone injection amount (O _{3 , MIB} ) for the _MIB is greater than the ozone injection amount (O _{3 , CT} ) for _CT , if no, determine whether the bromate concentration is less than 10ug / L, If no, ozone process is not applicable. If the bromate concentration in the step is less than 10ug / L 'yes' to determine whether the existing water purification plant or a new water purification plant, if the existing water purification plant is not required installation and maintenance costs. In this step, if the new water treatment plant, the installation cost is calculated based on the HRT and the flow rate by the EPA model, the maintenance cost is calculated as the ozone injection amount (O _{3 , CT} ) for CT while the smell smell material is not generated.

FIG. 13 illustrates an example of an input screen of ozone (O ₃ ) process modeling performed according to the ozone (O ₃ ) process flowchart of FIG. 12.

In the above-described initial screen of FIG. 3, the ozone (O ₃ ) process is selected from the modeling button 10 for the process provided on the left side, and the cost analysis / execution button 20 for decision support provided in the middle is selected. The ozone (O ₃ ) process input screen of FIG. 13 appears.

As shown in FIG. 13, four groups of input windows are activated on the input screen. The inflow MIB concentration, MIB generation period and duration are inputted into the taste-smelling characteristic input window on the upper left side, and the set target input window on the lower left side. The user-defined effluent MIB set target concentrations are selected from 5, 10, and 20 and input.

In addition, the influent TOC concentration, alkalinity, pH, temperature, and ozone contact time are input in the water quality / design factor input window on the upper right side, and the bromide concentration is input in μg / L in the other consideration input window on the lower right.

After the process is finished, press the left process model operation button at the bottom to move to the next step, press the return to the main menu on the right to move to the previous step.

Figure 14 shows an example of the output screen of the ozone (O ₃ ) process modeling according to an embodiment of the present invention.

As shown in FIG. 14, four output windows appear on the output screen of the ozone process modeling. The modeling factor values selected in FIG. 13 are displayed on the left side of the screen, and the modeling results performed are presented on the right side of the screen.

At the top of the model factor group on the left side of the screen, the selected influent MIB concentration, effluent treatment target concentration, and ozone contact time are displayed, and at the bottom, the model factor values used are R _CT , K _{O3, MIB} , K _{OH , and MIB} .

At the top of the modeling results group on the right side of the screen, the selected removal rate, the required ozone dose, and the amount of bromate produced are presented, with suggestions for the results below.

After the above process, press the left doctor support analysis button at the bottom to go to the next step, press the return to the main menu on the right to go to the previous step.

< Advanced oxidation ( UV Of H ₂ O ₂ ) Process)>

15 shows a flowchart of an advanced oxidation (UV / H ₂ O ₂ ) process according to an embodiment of the present invention.

As shown in FIG. 15, advanced oxidation (UV / H ₂ O ₂ ) process model calculation is performed by dividing process modeling and cost analysis.

In process modeling, the input module of the advanced oxidation (UV / H ₂ O ₂ ) process is classified into three groups of input data groups, and the MIB concentration, MIB generation cycle (F) The period (D) is input, and the user-defined effluent MIB treatment concentration (eg, 5 ng / L, 10 ng / L, 20 ng / L) is input to the treatment target setting in Group 2. TOC concentration, pH, alkalinity, UV254 absorbance, H ₂ O ₂ concentration are input.

Once the conditions of the advanced oxidation (UV / H ₂ O ₂ ) process are entered, the UV dose required for MIB control (UV _MIB ) is then calculated and at the same time the UV dose required for disinfection (UV _dis ) is determined. (UV _dis = 40 mJ / cm 2).

Next, it is determined whether the UV irradiation amount (UV _MIB ) is greater than (UV _dis ), and if the value is large, 'Yes', the next step is that the UV irradiation amount is the UV irradiation amount (UV _MIB ) required for MIB control, No 'if the UV irradiation amount is the irradiation amount UV (UV _dis) necessary for the disinfection.

After the process modeling process, the flow goes to the cost analysis step and the inflow flow rate is input.

If the UV irradiation amount in the above step (UV _MIB ), it is determined whether the existing water purification plant or a new water purification plant, if the new water purification plant, the installation cost is calculated according to the UV contact time (UVT) and the flow rate, the maintenance cost is taste smell It is calculated according to (UV _MIB ) during development, and according to (UV _dis ) during absence of taste. On the other hand, if the existing water purification plant in the step, the maintenance cost is calculated according to the UV irradiation amount (UV _MIB -UV _dis ) by the difference between (UV _MIB ) and (UV _dis ) only during the taste odor.

In the above step, if the UV irradiation amount is (UV _dis ), it is determined whether the existing water purification plant or a new water treatment plant. If it is a new water treatment plant, the installation cost is calculated according to the UV contact time (UVT) and the flow rate, and the maintenance cost is determined by UV _dis . Is calculated accordingly. On the other hand, if the existing water purification plant in the step, it is understood that there is no installation cost and maintenance costs.

FIG. 16 illustrates an example of an input screen of an advanced oxidation (UV / H ₂ O ₂ ) process modeling performed according to the advanced oxidation (UV / H ₂ O ₂ ) process flowchart of FIG. 15.

In the above-described initial screen of FIG. 3, the modeling button 10 for the process provided on the left side selects an advanced oxidation (UV / H ₂ O ₂ ) process, and the cost analysis / execution button for decision support provided in the middle. If (20) is selected, the advanced oxidation (UV / H ₂ O ₂ ) process input screen of FIG. 16 appears.

As shown in FIG. 16, three groups of input windows are activated on the input screen. The inflow MIB concentration, MIB generation cycle and duration are inputted into the taste-smelling characteristic input window on the upper left side, and the set target input window on the lower left side is input. The user-defined effluent MIB treatment concentration is selected from 5, 10, and 20 and entered. In addition, UV ₂₅₄ absorbance (1 / cm), influent TOC concentration (mg / L), alkalinity (mg / L), and H ₂ O ₂ concentration (mg / L) are entered in the water quality / design factor input window on the upper right. .

After the input process is finished, press the left process model operation button at the bottom to move to the next step, press the return to the main menu on the right to move to the previous step.

Figure 17 shows an example of the output screen of the advanced oxidation (UV / H ₂ O ₂ ) process modeling in accordance with an embodiment of the present invention.

As shown in FIG. 17, three output windows appear on the output screen of the advanced oxidation (UV / H ₂ O ₂ ) process modeling, and the modeling parameter values selected in FIG. 16 are displayed on the left side of the screen, and the right side of the screen is performed. Modeling results are presented.

Selected influent MIB concentration, effluent treatment target MIB concentration, H ₂ O ₂ injection volume are displayed at the top of the model factor group on the left side of the screen, and K _{C , OH} values _, which are used model factors _, are displayed at the bottom.

At the top of the modeling result group on the right side of the screen, the selected removal rate, the amount of UV required (mJ /) is presented, and the recommendation is presented below.

After the above process, press the left doctor support analysis button at the bottom to go to the next step, press the return to the main menu on the right to go to the previous step.

<Biofiltration Biofiltration ) Process>

18 shows a flowchart of a biofiltration process in accordance with an embodiment of the present invention.

As shown in FIG. 18, in the process modeling, the input module of the biofiltration process is classified into four groups of input data groups, and the concentration of MIBs and the frequency of MIB generation inflow into the taste and smell occurrence characteristic data in the first group. ) And duration (D) are input, the turret residence time and media type are input in water quality and design factors in group 2, and the biomass (nmol-P / g) is input in model factor in group 3, and 4 Other considerations in the group include temperature and MIB compliance periods.

Next, it is determined whether there is residual chlorine in the filter paper, and if there is residual chlorine, it is determined that the MIB cannot be removed in the next step, and if there is no residual chlorine, the next step is no. If it is determined as 'no', the removal rate follows the following equation according to the compliance period.

Remove = Remove by Model × 0.167t

If the removal rate is calculated or if the MIB compliance period is more than 6 months 'yes', in the next step to determine whether the water temperature is 10 ℃ or more in the case of 'yes' if the water temperature is 10 ℃ or more by model It is determined that there is no reduction in.

If the water temperature corresponds to 'no' below 10 ° C., it is determined whether the water temperature is above 5 ° C., and if it corresponds to 'Yes' above 5 ° C., the removal rate is determined to be reduced to 70%. If no, it is determined that it will not be removed.

FIG. 19 illustrates an example of an input screen of biofiltration process modeling performed according to the biofiltration process flow chart of FIG. 18.

In the above-described initial screen of FIG. 3, the biofiltration process is selected from the modeling button 10 for the process provided on the left side, and the cost analysis / execution button 20 for decision support provided in the middle is selected. 19 shows the biofiltration process input screen of FIG. 19.

As shown in FIG. 19, four groups of input windows are activated on the input screen. The input MIB concentration, MIB generation period and duration are inputted into the taste-smelling characteristic input window on the upper left, and the model factor input window on the lower left. The biomass (nmol P / g) is input into. In addition, the inflow TOC concentration and the tower contact time (EBCT) are input to the water quality / design factor input window on the upper right side, and one of sand and activated carbon is selected and selected from the media type. Other considerations in the lower right corner of the input window enter the temperature, the filter adaptation time during which the MIB occurs during the period of taste odor, and select whether yes or no is present in the filter influent.

After the input process is finished, press the left process model operation button at the bottom to move to the next step, press the return to the main menu on the right to move to the previous step.

20 shows an example of an output screen of the biofiltration process modeling according to an embodiment of the present invention.

As shown in FIG. 20, four output windows appear on the output screen of the biofiltration process modeling, and the modeling factor values selected in FIG. 19 are displayed on the left side of the screen, and the modeling results performed on the right side of the screen are presented. .

In the upper part of the model factor group on the left side of the screen, the influent MIB concentration, the selected tower contact time (EBCT), the selected media type, the MIB acclimation time, and the biomass concentration are shown, and at the bottom, the primary velocity constant (k ') is used. .

Selected removal rate, effluent MIB concentration (ng / L) is presented at the top of the modeling result group on the right side of the screen, and recommendations are presented below.

After the above process, press the left doctor support analysis button at the bottom to go to the next step, press the return to the main menu on the right to go to the previous step.

< Nanomembrane ( Nanomembrane ) Process>

21 is a flowchart of a nanomembrane (Nanomembrane) process according to an embodiment of the present invention.

As shown in FIG. 21, in the process modeling, the input module of the nanomembrane process is classified into three groups of input data groups, and the inflow MIB concentration is input to the taste and smell generation characteristic data in the first group, and 2 In the group, the user-defined effluent MIB set target concentration (eg, 5ng / L, 10ng / L, 20ng / L) is input to the set target input data, and the membrane molecular weight (MWCO) is input to the system parameter input data of the 3 groups. .

Next, it is determined whether the membrane molecular weight (MWCO) is in the range of 250 to 550, and if it is not in the range and it is 'no', the nanomembrane process is determined not to be applicable, and if it is in the range and the 'yes' is selected, It is determined whether the removal rate is greater than 90%.

If the removal rate selected in the above step is less than 90% and corresponds to 'no', the operation time is determined to be ± 15% per week, and if the selected removal rate is more than 90% to 'yes', the operation time is determined according to the selected removal rate. Calculate

Here, X = operating time and R = removal rate.

Next, the range of operation time from the output value is determined by the following equation.

Operation range = operation time ± 15%

FIG. 22 illustrates an example of an input screen of a nanomembrane process modeling performed according to the nanomembrane process flowchart of FIG. 21.

In the initial screen of FIG. 3, the nano-membrane process is selected from the modeling button 10 for the process provided on the left side, and the cost analysis / execution button 20 for decision support provided in the middle is selected. The nano-membrane process input screen of FIG. 21 appears.

As shown in FIG. 22, two groups of input windows are activated on the input screen. The inflow MIB concentration and the outflow water treatment target MIB concentration are inputted into the taste-smelling characteristic input window in the upper left corner, and the membrane is entered in the system parameter input window on the right. Molecular weight CUT OFF is input.

After the input process is finished, press the left process model operation button at the bottom to move to the next step, press the return to the main menu on the right to move to the previous step.

FIG. 23 shows an example of an output screen of a nanomembrane process modeling according to an embodiment of the present invention.

As shown in FIG. 23, three groups of output windows appear on the output screen of the nanomembrane process modeling, and the modeling factor values selected in FIG. 22 are displayed on the left side of the screen, and the modeling results performed on the right side of the screen are presented. .

The model factor group on the left side of the screen displays the influent MIB concentration, the effluent treatment target MIB concentration, and the membrane molecular weight fraction.The selected removal rate and operating time are presented at the top of the modeling result group on the right side of the screen. do.

After the above process, press the left doctor support analysis button at the bottom to go to the next step, press the return to the main menu on the right to go to the previous step.

< PAC Of UF Hybrid  Process>

24 illustrates an example of an input screen of PAC / UF Hybrid process modeling according to an embodiment of the present invention. Since the PAC / UF Hybrid process is a combination process of a powder activated carbon (PAC) process and an UF (ultrafiltration membrane) process, detailed descriptions of the process modeling and cost analysis processes will be omitted.

 In the above-described initial screen of FIG. 3, the PAC / UF Hybrid process is selected from the modeling button 10 for the process provided on the left side, and the cost analysis / execution button 20 for decision support provided in the middle is selected. The PAC / UF Hybrid process input screen of FIG. 24 appears.

As shown in FIG. 24, four groups of input windows are activated on the input screen. The inflow MIB concentration, MIB generation cycle and duration are inputted into the taste-smelling characteristic input window in the upper left corner, and the set target input window in the lower left corner. The user-defined effluent MIB set target concentrations are selected and input from 5, 10, and 20.

In addition, the influent TOC concentration is input in the water quality / design factor input window at the upper right, the PAC hydraulic retention time is input in minutes, and the PAC adsorption capacity is selected. In the lower right, 'Yes' and 'No' are selected depending on whether or not chlorination is carried out with the current PAC in the other considerations input window.

After the input process is finished, press the left process model operation button at the bottom to move to the next step, press the return to the main menu on the right to move to the previous step.

25 shows an example of an output screen of the PAC / UF Hybrid process modeling.

As shown in FIG. 25, three output windows appear in the output screen of the PAC / UF Hybrid process modeling. Modeling factor values are displayed on the left side of the screen, and the modeling results are performed on the right side of the screen.

The upper part of the model factor group on the left side of the screen shows the selected influent MIB concentration, the effluent set target concentration, the powdered activated carbon hydraulic retention time, and the lower part shows the distilled water Freundlich factor value, K value and 1 / n value in the used powdered carbon model. In the upper part of the modeling result group on the right side of the screen, the selected removal rate and the required amount of activated powdered carbon are presented, and in the lower part, the EBC K value, the EBC 1 / n value, and the EBC initial concentration value are used.

After the above process, press the left doctor support analysis button at the bottom to go to the next step, press the return to the main menu on the right to go to the previous step.

26 shows an example of a cost analysis result screen for decision making of an MDSS system according to an embodiment of the present invention.

As shown in FIG. 26, the cost analysis result screen is a screen that appears when the middle comparison / decision support button 20 is clicked on the initial screen of FIG. 3 and is composed of six groups as a summary of the entire process. .

In the upper left, the process name, relative installation cost, maintenance cost, and total cost are presented for the selected processes in the MDSS system, and finally the priority process for the total cost is presented sequentially. In the upper right corner, the influent MIB concentration, the set target MIB concentration, the generation period, the generation period, and the flow rate, which are input conditions in the primary priority process selection, are presented as input factor conditions and confirmation groups.

At the bottom, comments on cost analysis, comments on input factor conditions, comments on biofiltration processes, and comments on naming processes are presented.

Through the above process, process modeling and cost analysis are integrated and provided in a system to support the decision making for the user to select the optimal process.

In the embodiment of the present invention as a process for removing the smell smell granular activated carbon (GAC) process, powder activated carbon (PAC) process, ozone (O ₃ ) process, biofiltration process, advanced oxidation (UV / H ₂ O ₂ ) Process, PAC / UF Hybrid process, and nano-membrane process to select modeling and cost analysis to select the optimal process for removing odor.

However, the present invention is not limited to the seven processes described above, but new processes may be added or changed as necessary, and process modeling processes and cost analysis processes may also be modified to achieve the same or similar purposes. Of course. Therefore, the present invention is not limited to the above-described embodiment, and various modifications within the equivalent scope of the technical idea of the present invention and the claims to be described below by those skilled in the art to which the present invention pertains. And of course modifications can be made.

1 is an overall configuration diagram of a taste smell control decision support system according to the present invention,

2 is a detailed block diagram of a taste smell control decision support system according to the present invention,

Figure 3 is an example of the initial screen of the taste control decision support system according to the present invention,

4 is an overall processing flow chart of a taste control decision support system according to the present invention;

5 is a flowchart of a granular activated carbon (GAC) process according to the present invention,

6 is an example input screen of the granular activated carbon (GAC) process modeling according to the present invention,

Figure 7 is an example of the output screen of the granular activated carbon (GAC) process modeling according to the present invention,

8 is an example input screen of the granular activated carbon (GAC) process cost modeling according to the present invention,

9 is a flow chart of the powder activated carbon (PAC) process according to the present invention,

10 is an example input screen of powder activated carbon (PAC) process modeling according to the present invention,

11 is an example of an output screen of the powder activated carbon (PAC) process modeling according to the present invention,

12 is a flow chart of an ozone (O ₃ ) process according to the present invention,

13 is an example input screen of ozone (O ₃ ) process modeling according to the present invention,

14 is an example of an output screen of the ozone (O ₃ ) process modeling according to the present invention,

15 is a flow chart of the advanced oxidation (UV / H ₂ O ₂ ) process according to the present invention,

16 is an example input screen of advanced oxidation (UV / H ₂ O ₂ ) process modeling according to the present invention,

17 is an example of an output screen of the advanced oxidation (UV / H ₂ O ₂ ) process modeling according to the present invention,

18 is a flowchart of a biofiltration process according to the present invention;

19 is an example input screen of the biofiltration process modeling according to the present invention,

20 is an example of an output screen of the biofiltration process modeling according to the present invention,

21 is a flowchart of a nanomembrane (Nanomembrane) process according to the present invention;

22 is an example of an input screen of a nano-membrane (Nanomembrane) process modeling according to the present invention,

23 is an example output screen of the nano-membrane (Nanomembrane) process modeling according to the present invention,

24 is an example input screen of PAC / UF Hybrid process modeling according to the present invention,

25 is an example output screen of PAC / UF Hybrid process modeling according to the present invention;

Figure 26 shows an example of the cost analysis result screen for the decision of the taste control decision support system according to the present invention.

Claims (7)

delete A system for supporting decision-making of process selection for taste control, Taste odor generating characteristic input module 110, which inputs taste odor generating characteristic information including inflow MIB concentration, MIB generation cycle and duration, and setting target information including setting target information including effluent MIB treatment concentration defined by the user. Input module 120, the water quality / design factor input module 130 to which the water quality / design factor information, including TOC, pH, temperature is input, and the consideration input that the consideration information including the constraints for each process is entered A decision condition input unit (100) having a module (140) for inputting decision condition information necessary for a taste smell control process; According to the decision condition input unit 100 inputted through the decision condition input unit 100, modeling processing for each process for taste control is performed to calculate processing costs for each process, and the calculated processing cost is analyzed to optimize the process. An optimal process selection processing unit 200 for selecting a; And a result analysis output unit (300) which gives priority to each process selected by the optimum process selection processing unit (200) and outputs them. 3. The method of claim 2, The optimum process selection processing unit 200 A process modeling module 210 for calculating design factors and operating conditions required to satisfy a set target input through the decision condition input unit 100; A cost analysis module 220 for performing cost analysis based on the process result of the process modeling; Taste control control support system characterized in that it comprises an optimization module 230 for selecting the optimization process according to the conditions of the decision condition input unit 100 through the cost analysis processed through the cost analysis module 220. The method of claim 3, wherein The result analysis output unit 300 is A modeling result output module 310 displaying design factors and operating conditions processed through the process modeling module 210 of the optimal process selection processing unit 200 and cost analysis processed through the cost analysis module 220. The cost analysis result output module 320, the result is displayed, and the priority process selection output module 330 is displayed, the priority process selection result processed by the optimization module 230, characterized in that made Odor Control Physician Support System. The method according to claim 4, The priority process selection output module 330 is Taste control control support system, characterized in that the installation cost, maintenance cost and total cost for each process is displayed, and the priority process for the total cost is displayed sequentially. The method of claim 3, wherein Process modeling process is carried out through the process modeling module 210 granular activated carbon (GAC) process, powder activated carbon (PAC) process, ozone (O ₃ ) process characterized in that the taste control control support system. The method of claim 6, Process modeling process is performed through the process modeling module 210 includes a biofiltration process, advanced oxidation (UV / H ₂ O ₂ ) process, PAC / UF hybrid process, nano-membrane (Nanomembrane) process Taste control doctor support system, characterized in that.

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