WO2013125737A1 - Appareil de commande à base de règles et procédé de commande à base de règles en se basant sur le diagnostic de procédé état de station de traitement des eaux usées - Google Patents

Appareil de commande à base de règles et procédé de commande à base de règles en se basant sur le diagnostic de procédé état de station de traitement des eaux usées Download PDF

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WO2013125737A1
WO2013125737A1 PCT/KR2012/001271 KR2012001271W WO2013125737A1 WO 2013125737 A1 WO2013125737 A1 WO 2013125737A1 KR 2012001271 W KR2012001271 W KR 2012001271W WO 2013125737 A1 WO2013125737 A1 WO 2013125737A1
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data
control
diagnosis
unit
rule
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PCT/KR2012/001271
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Korean (ko)
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김창원
김예진
김효수
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부산대학교 산학협력단
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
    • Y02A20/152—Water filtration

Definitions

  • the present invention relates to a rule-based control device and method according to the sewage treatment plant process status diagnosis.
  • a rule-based control device and method according to the sewage treatment plant process status diagnosis.
  • the present invention relates to a rule-based control device and method according to the diagnosis of process status of sewage treatment plant for the development of control method.
  • the solution for process diagnosis and control at the time of the initial application was mostly an off-line expert system based on the expert's operation experience to help the inexperienced operator's decision-making.
  • various solutions for extracting useful information from a large amount of data are available.
  • the traditional P, PI and PID Proportional Integral
  • Differential control methods have been applied to sewage treatment processes, but they have only relied on the experience of experienced operators without any criteria to select new setpoints of variables to be controlled.
  • the sewage treatment plant process is the main one, and it is distinguished from other industrial processes such as chemical processes due to various variables such as microbe behavior, operation status of the device, current water temperature, and rainfall. It is still too much to follow traditional control methodologies.
  • control is not performed based on the result of the process status diagnosis.
  • the result of the process diagnosis is reported to the operator as a result of the diagnosis.
  • the control is carried out separately, and the control is performed without clear information on the current process status. There was a long time problem.
  • the present invention performs qualitative diagnostic information on the treatment performance state of the sewage treatment plant being operated to improve the treatment performance of the sewage treatment plant, and provides the amount of change of specific control parameters according to the diagnosis result.
  • the purpose is to provide a rule-based control device and method according to the diagnosis of process status of sewage treatment plant for developing rule-based control method.
  • the data collection unit for receiving the inflow / outflow water quality data and process operation data required for the diagnosis of the process state of the sewage treatment plant or the past data set stored in the database unit;
  • a data preprocessor for preprocessing the respective data by removing a range of numerical absolute values of each data from the data received by the data collector;
  • a data abbreviation unit In order to derive a diagnosis result regarding the state of the current processing performance using the reduced data, the reduced data is grouped, and a determination function for determining the state of the grouped data is derived, and the diagnosis result by the determination function. Deriving process diagnosis unit; And a control strategy call unit for calling a rule regarding an amount of change of the control variable of the process operation data by a preset simulation according to a group representing a diagnosis result derived from the process diagnosis unit. Provide rule-based control device based on diagnosis.
  • the rule-based control device according to the sewage treatment plant process state diagnosis further includes a control strategy applying unit applying the control action determined by the control strategy calling unit to the sewage treatment plant, wherein the control strategy applying unit includes the control strategy calling unit. It is characterized in that for transmitting an electrical signal by the control action determined by the actuator (actuator).
  • the data preprocessing unit standardizes the input data by Equation 1 by using the average and standard deviation which are automatically calculated and derived for each data of the data input by the data collection unit. .
  • the data reduction unit is characterized in that by performing the principal component analysis method of one of the multivariate analysis method to reduce the plurality of inflow / outflow water quality data and process operation data with a reduced number of reduced main components than several.
  • the process diagnosis unit performs K-average cluster analysis on the principal components abbreviated by the data abbreviation unit to group the processing performance and operation state of the process, and new inflow / outflow water quality data and process operation data are input and converted into the principal components. And then use Fisher's linear discriminant analysis to determine which group the new inflow / outflow water quality data and process operation data can be assigned to. It is characterized by performing a diagnosis on the treatment performance and process operation of the treatment plant.
  • control strategy call unit stores control rules regarding DO (dissolved oxygen) control or an external carbon source control operation that can further improve the processing performance of the process according to the characteristics of each group derived from the process diagnosis unit, the process diagnosis Calling the control rule stored in advance in the control strategy call unit according to the process state derived from the unit to call the amount of change of the control variable of the process operation data derived by the simulation of the control rule do.
  • DO dissolved oxygen
  • Process states are grouped by K-means cluster analysis using the principal components as input data, and a discriminant function for determining the state of the grouped data is derived using discriminant analysis, and new inflow / outflow water quality data and process operation data.
  • DO dissolved oxygen
  • the discriminant analysis uses the group classified by the K-means cluster analysis as an external standard, and through Fisher's linear discriminant analysis, derives a discriminant function for each group, and introduces new inflow / outflow water quality data and process operation. It is characterized in that the principal components generated by the data are input and compared to the magnitudes of the values calculated by the discriminant function of each group and assigned to the group representing the largest value.
  • the present invention combines process state diagnosis and rule-based control based on this to inform the operator about the current process state. And by suggesting the optimized control method for this, it can help the operator to understand the process more quickly, and it has the effect of helping to operate the efficient sewage treatment plant for optimal operation and maintenance.
  • the existing control strategies have the disadvantage of requiring frequent failure and repair of the equipment by changing the operating conditions of the equipment in a short time
  • the present invention by setting the minimum maintenance period that the process can be stabilized by the changed operating conditions It is effective to prevent the frequent breakdown of the equipment due to the long period of changing operating conditions.
  • FIG. 1 is a block diagram showing a rule-based control device according to the sewage treatment plant process status diagnosis according to an embodiment of the present invention.
  • FIG. 2 is a configuration diagram showing in detail the operating state of the rule-based control device of FIG.
  • FIG. 3 is a diagram illustrating a detailed screen of the process diagnosis unit of FIG. 1.
  • FIG. 4 is a diagram illustrating a detailed screen of DO control in a control strategy by the control strategy calling unit in FIG. 1.
  • FIG. 5 is a view showing a detailed screen of the external carbon source flow rate injection of the control strategy by the call control unit in FIG.
  • FIG. 6 is a flow chart showing a rule-based control method according to the sewage treatment plant process status diagnosis according to an embodiment of the present invention.
  • FIG. 7 is a flowchart illustrating a control strategy call step of FIG. 6 in detail.
  • FIG. 8 is a graph illustrating a simulation result for calling a control strategy of two groups in FIG. 7.
  • FIG. 9 is a graph illustrating a simulation result for calling a control strategy of three groups in FIG. 7.
  • FIG. 10 is a graph illustrating a simulation result for calling a control strategy of four groups in FIG. 7.
  • 11 is a graph showing the results of field operation to which the rule-based control method according to the diagnosis of the process state of the sewage treatment plant according to the present invention is not applied.
  • FIG. 12 is a graph showing the results of field operation to which the rule-based control method according to the sewage treatment plant process condition diagnosis according to the present invention is applied.
  • the data collection unit for receiving the inflow / outflow water quality data and process operation data required for the diagnosis of the process state of the sewage treatment plant or the past data set stored in the database unit;
  • a data preprocessor for preprocessing the respective data by removing a range of numerical absolute values of each data from the data received by the data collector;
  • a data abbreviation unit In order to derive a diagnosis result regarding the state of the current processing performance using the reduced data, the reduced data is grouped, and a determination function for determining the state of the grouped data is derived, and the diagnosis result by the determination function. Deriving process diagnosis unit; And a control strategy call unit for calling a rule regarding an amount of change of the control variable of the process operation data by a preset simulation according to a group representing a diagnosis result derived from the process diagnosis unit. Provide rule-based control device based on diagnosis.
  • FIG. 1 is a block diagram showing a rule-based control device according to the sewage treatment plant process condition diagnosis according to an embodiment of the present invention
  • Figure 2 is a configuration diagram showing the operating state of the rule-based control device of Figure 1 in detail
  • 1 is a view showing a detailed screen of the process diagnostic unit
  • Figure 4 is a view showing a detailed screen of the DO control of the control strategy by the control strategy caller in Figure 1
  • Figure 5 is a control strategy caller in Figure 1
  • Figure 6 is a view showing a detailed screen of the external carbon source flow rate injection of the control strategy
  • Figure 6 is a flow chart showing a rule-based control method according to the process state diagnosis of sewage treatment plant according to an embodiment of the present invention
  • Figure 8 is a graph showing the simulation results for the control strategy call of the two groups in Figure 7
  • FIG. 10 is a graph showing simulation results for calling a control strategy of four groups in FIG. 7, and FIG. 11 is not applied to a rule-based control method according to a process status diagnosis of a sewage treatment plant according to the present invention.
  • 12 is a graph showing the results of the field operation, Figure 12 is a graph showing the results of the field operation to which the rule-based control method according to the diagnosis of the process state of the sewage treatment plant according to the present invention.
  • the rule-based control device 10 includes a data collection unit 100, a database unit 200, a data preprocessor 300, a data abbreviation unit 400, and a process diagnosis unit 500. ), The control strategy call unit 600 and the control strategy application unit 700.
  • the data collection unit 100 receives process operation data such as inflow / outflow water quality data and a flow rate value, a sensor value, a blowing amount, and a return flow rate required for the diagnosis and control of the process state of the sewage treatment plant from the sewage treatment plant.
  • the data collection unit 100 may typically be a programmable logic controller (PLC) and a data collection device.
  • PLC programmable logic controller
  • the input process operation data can be confirmed in numerical values and graphs of the change of the cumulative data and the current data values on the monitoring screen of the rule-based control device 10.
  • the monitoring screen is adjustable according to the number and type of devices constituting the process and the type of data to be input.
  • the database unit 200 separately stores inflow / outflow water quality data and process operation data such as a flow rate value, a sensor value, a blowing amount, and a return flow rate. That is, the data collected by the data collection unit 100 is automatically stored in the database unit 200 by the electrical signal conversion. The stored data is composed of past data sets necessary for diagnosing the process state of the sewage treatment plant, and if necessary, data stored in the database unit 200 may be called and used.
  • the database unit 200 may be configured as a separate device, or may be incorporated in the data collector 100 or the data preprocessor 300 to be described later.
  • the data preprocessor 300 performs data preprocessing on the data collected by the data collector 100 or the past data sets stored by the database unit 200. Accordingly, the data preprocessor 300 removes the range of the numerical absolute value of each data from the data input by the data collector 100 to facilitate subsequent data processing. Preprocess
  • the data preprocessing unit 300 calculates the input data by Equation 1 using an average and a standard deviation which are automatically calculated and derived for each of the data input by the data collection unit 100. Standardize The standardized data are the data required for performing multivariate analyses, in particular principal component analysis.
  • the data abbreviation unit 400 receives data preprocessed by the data preprocessing unit 300 and applies a multivariate analysis method to the preprocessed data to relate to a process state inherent in past data sets of a sewage treatment plant.
  • the preprocessed data is abbreviated to extract information.
  • the data reduction unit 400 may perform a dimensional reduction on a plurality of inflow / outflow water quality data and process operation data with fewer abbreviated principal components by principal component analysis, which is typically one of multivariate statistical techniques. . For example, 10 or more inflow / outflow water quality data and process operation data can be reduced to three to five abbreviated principal components.
  • the process diagnosis unit 500 uses the reduced data to group (group) the reduced data to derive a diagnosis result regarding a state of current processing performance, and to determine the state of the grouped data.
  • a function is derived to derive a diagnosis result by the discrimination function.
  • the process diagnosis unit 500 performs a K-average cluster analysis on the principal components abbreviated by the data abbreviation unit 400 to group the processing performance and operation state of the process, and new inflow / outflow water quality data and process operation data.
  • a discriminant function to be used as a means for determining which group the new inflow / outflow water quality data and the process operation data can be assigned to is derived using Fisher's linear discriminant analysis, and the allocation This group is used to carry out diagnostics on the treatment performance and process operation of sewage treatment plants.
  • the process diagnosis unit 500 first performs a process state diagnosis through a past data set stored in the database unit 200, and the result of the diagnosis may be checked on a process state diagnosis screen.
  • the process diagnosis checks which process group belongs to the pre-classified group, and the process characteristics are derived in advance for each group and the result is displayed on the screen. Although varying from process to process, three to five groups are most preferred.
  • FIG. 3 a detailed screen of the process diagnosis unit 500 is shown.
  • the upper part is a schematic diagram of the sewage treatment plant process
  • the middle part is a table showing the corresponding group for each hour as time passes
  • the lower right shows the diagnosis result for the current group. You can check the current process status either hourly or daily, depending on the cycle of the data being stored.
  • the diagnosis result of the current process is displayed on the screen through the process state diagnosis, which can be checked by the operator, which is automatically stored in the database unit 200.
  • Rule-based control according to the derived diagnosis result is performed according to the control flowchart stored in the control device 10, and the result is displayed on the rule-based control screen.
  • the set values of devices and equipment derived by a series of rules can be checked on the screen, and these set values are automatically stored in the database unit 200, and the devices and equipment constituting the sewage treatment plant process are set according to the set values. Controlled. At this time, since the coefficient values necessary for the P, PI, and PID control are input in the control device 10, all control actions are driven by the actual device through the control device 10 under changed operating conditions.
  • the control strategy call unit 600 calls a rule regarding the amount of change of the control variable of the process operation data by a simulation set in advance according to a group representing a diagnosis result derived from the process diagnosis unit 500.
  • the control strategy call unit 600 controls the control rules related to DO (dissolved oxygen) control or external carbon source control operation that can further improve the processing performance of the process according to the characteristics of each group derived from the process diagnosis unit 500. Storing and calling a control rule stored in the control strategy caller 600 in advance according to the process state derived from the process diagnosis unit 500, and a process operation derived by simulation of the control rule. Call up the amount of change in the control variable in the data.
  • DO dissolved oxygen
  • the detailed screen of the DO control of the control strategy by the control strategy call unit 600 is shown.
  • the control strategy call unit 600 performs DO control and external carbon source flow control.
  • the DO setting value derived through rule-based control in the middle of the process diagram of the sewage treatment plant at the top is Is shown.
  • the actual DO concentration is adjusted through PID control in the controller 10. It is appropriate to apply P, PI or PID control to control the setting value.
  • the new DO setpoint is performed to increase or decrease the nitrification reaction, resulting in a change in the NH 4 -N concentration of the effluent.
  • the bottom part shows the DO concentration in the current process.
  • the two graphs in the middle of the screen show the inlet and outlet NH 4 -N concentrations over time.
  • the first graph on the bottom right shows the set DO value and the current DO concentration.
  • the second graph to the right shows the aeration of the blower that is changing to match the set DO concentration.
  • the control strategy call unit 600 determines whether to inject the external carbon source flow rate through rule-based control, and if the external carbon source flow rate is to be injected, calculates an appropriate external carbon source flow rate using the current effluent NOx-N concentration. To be injected into the process. Looking at a more detailed screen of the external carbon source flow control performed in the control device 10, the middle part of the screen shows a series of calculation process for deriving the external carbon source flow rate, which is currently being injected into the lower middle part of the screen The external carbon source flow rate will appear.
  • the first and second graphs on the right side of the screen show the incoming and outgoing NOx-N concentrations, and the graph on the lower right shows the flow of external carbon source flow.
  • the operator can check whether the external carbon source is being injected according to the rule-based control through the screen, and can also check the change of the process status according to the change of operating conditions in each diagnosis and control screen.
  • the control strategy application unit 700 applies the control action determined by the control strategy call unit 600 to the sewage treatment plant.
  • the control strategy application unit 700 transmits an electrical signal based on the control action determined by the control strategy call unit 600 to an actuator.
  • the control method includes a data collection step of receiving data collected in a sewage treatment plant process (S110), a data preprocessing step (S120) of performing preprocessing on necessary data, and a principal component coefficient previously analyzed through the preprocessed data.
  • Data abbreviation step of deriving the principal component value through the combination and calculation of (S130) process diagnostic step of analyzing which group the current process state is applied by applying to the discrimination function also analyzed in advance through the derived principal component value ( S140), a specific group corresponding to the diagnosis result determined in the process diagnosis step is confirmed, and according to the control strategy call step (S150) for calling a rule-based control strategy for each group according to the identified diagnosis result (S150) and the derived result.
  • the control strategy preferably includes only changes in DO setpoints in the process and therefore changes in air flow, internal conveying flow changes, sludge conveying flow changes and, if necessary, additional external carbon source flow changes, depending on the sewage treatment plant process characteristics. It is desirable to exclude some of the statements mentioned.
  • an inflow / outflow water quality data and process operation data necessary for diagnosing a process state of a sewage treatment plant or a data collection step of receiving a historical data set stored in the database unit 200 is performed.
  • inflow / outflow water quality data and process operation data from November 2010 to September 2011 were collected from an A 2 / O process with an average inflow of 18 m 3 / day.
  • the data preprocessing step (S120) is performed on the collected data to preprocess the respective data by removing a range of numerical absolute values of each data. That is, data normalization is performed to perform principal component analysis, which is calculated as shown in Equation 1 through the average and standard deviation that can be calculated from each individual data and the data set in which the data is accumulated. 0, converted to data with a standard deviation of 1.
  • Means the measured value of each data Is the mean of that data item, Means the standard deviation of the data item.
  • Principal component analysis is performed by receiving the preprocessed data through a data reduction step (S130). In other words, by analyzing the variance of the variables constituting the pre-processed data to derive a principal component that can be reduced by a common variance.
  • the principal component value is finally calculated based on each preprocessed item value and principal component coefficient value.
  • the principal component value is calculated as 0.384 * In NH 4 -N + 0.082 * In NOx-N + 0.381 * In PO 4 -P + ... + 0.131 * AERO 3_MLSS-0.147 * ANOX_ORP.
  • process states are grouped by K-means cluster analysis using the principal components as input data, and a discriminant function for determining the state of the grouped data is derived using discriminant analysis.
  • the main components generated by the inflow / outflow water quality data and the process operation data are used as input data to determine which group is among the grouped groups, and the diagnosis is performed on the treatment performance and process operation of the sewage treatment plant using the determined groups.
  • the discriminant analysis sets the group classified by the K-means cluster analysis as an external standard, and derives a discriminant function which is a functional formula for each group through Fisher's linear discriminant analysis, and introduces new inflow / outflow water quality data and process operation data.
  • the main components generated by the input are compared with the magnitudes of the values calculated by the discriminant function of each group, and assigned to the group representing the largest value.
  • the process operation status can be classified into four groups according to their characteristics as shown in [Table 2] below.
  • the linear discriminant analysis yielded the following.
  • Group 1 -4.377PC 1 + 2.172PC 2 + 3.238PC 3 - 6.179
  • Group 2 2.322PC 1 + 3.076PC 2 - 2.504PC 3 - 4.878
  • Group 3 1.724PC 1 - 1.604PC 2 + 1.172PC 3 - 2.864
  • Group 4 -0.229PC 1 - 2.403PC 2 - 1.589PC 3 -3.010
  • the value of the function of each group is calculated by substituting the principal component values first derived into the discrimination function, and the group represented by the function having the largest value is interpreted to mean the current process state.
  • Diagnosis results through the process characteristics of each group can be presented to the operator with the following qualitative information.
  • Group 2 High incoming ammonia loads and moderate loads but not denitrification (DO concentrations are adequate but external carbon source control is required).
  • Group 3 High nitrogen / phosphorus loading, low nitrification and phosphorus removal due to low DO and low MLSS.
  • control strategy call step (S150) by calling the control rules for the DO (dissolved oxygen) control or external carbon source control operation, etc., which can further improve the processing performance of the process according to the characteristics of the group for the derived diagnostic results Call the amount of change in the control variable of the process operating data derived by the simulation of the control rule.
  • DO dissolved oxygen
  • control strategy call step (S150) a rule-based control flow chart as shown in FIG. 7 is embedded, and a control strategy is called for each group in question.
  • each rule implies a change amount of a control variable by a mathematical simulation performed periodically. Doing.
  • control strategy call step (S150) of the control method is shown in detail.
  • the relevant group is checked through the process status diagnosis, and the same group is repeated four times or more. If the process status diagnosis is carried out hourly, it is desirable to be more than 4 hours, and if the process status diagnosis is performed daily, it is desirable to be more than 4 days. This is to prevent frequent equipment changes, and also to change the operating conditions. We believe this is a minimum maintenance period for the process to stabilize.
  • FIG. 7 shows control actions for a total of four groups.
  • the classification of groups will depend on the nature of the sewage treatment plant process and groups 4 to 5 are appropriate.
  • group 1 is a steady state
  • group 2 is a state that requires an additional external carbon source injection due to the lack of denitrification reaction
  • group 3 is a state in which the DO set value is increased and additional external carbon source injection is necessary, due to lack of both nitrification and denitrification reaction
  • Group 4 was classified as requiring a DO increase due to lack of nitrification.
  • the process status is checked again. If a group other than the corresponding group is maintained at least four times, the control action is stopped. Otherwise, the control action is maintained.
  • the operating conditions that can stabilize the continuity are continuously applied.
  • control variables For example, the NH 4 -N and NOx-N concentrations were selected as control variables, and the DO concentration and the external carbon source injection flow rate were selected as control variables.
  • the control actions according to the process status diagnosis result of each group are as follows.
  • Group Control Behavior Do not perform separate control behavior as normal process. Switch to fixed aeration when DO is under control.
  • Group 2 Control Action Requires injection of external carbon sources due to high NOx-N concentrations occurring despite appropriate DO concentrations. Therefore, DO concentration is converted to fixed aeration volume and injected with external carbon source.
  • Group 1 does not need pre-simulation because no additional control is required.
  • Group 2 has a high influent NH 4 -N load but can be removed well and the denitrification reaction is not smooth. Therefore, the denitrification reaction should be activated by external carbon source injection without adjusting the DO concentration.
  • Preliminary simulation results regarding the amount of change in the control variable that can be achieved by setting L as a reference are shown in FIG. 8. As can be seen in Figure 8, the concentration of effluent NOx-N was reduced by about 21.1% through the injection of external carbon source, it can be said that it is reasonable to inject the external carbon source flow rate targeting the effluent NOx-N concentration of 5mg / L through this have.
  • Group 3 has high inflow NH 4 -N load and overall runoff is unstable, so simultaneous control of DO concentration and external carbon source is required, so pre-simulation was carried out under the strategy of DO 30% increase, DO 50% increase, external carbon source injection.
  • the effluent NH 4 -N concentration average decreases by about 15.4%
  • the NOx-N concentration is the DO concentration when an external carbon source is injected. It was confirmed that the increase was about 28.4% compared with the 30% increase.
  • Group 4 has a moderate influent load but is not well nitrified by low DO concentration, so DO control is required, and simulations were performed for DO 20% increase and DO 50% increase, and in FIG. As can be seen, it was confirmed that the initial value is reduced by increasing the DO concentration. Therefore, when the concentration of effluent NH 4 -N is 15 ppm or more, the DO concentration is increased by 50%, and when the concentration is less than 15 ppm, the DO increase of 20% is derived as a control strategy to prevent the problem of overexploitation.
  • the mathematical model used is not included in the control device 10, but may be configured by separately driving the controller at a predetermined period outside the control device 10 and inputting the quantified value into the control logic inside the control device 10. have.
  • control strategy call step (S150) the control strategy according to the rules of each group is called according to a series of flowcharts based on such quantitative set values, which will be described in detail as follows.
  • NH 4 -N and NOx-N control are performed according to the flowchart of FIG. 7, and the DO set value is increased by 50%. DO control is performed.
  • the DO concentration which is the reference for increasing the set value, was set to 1 ppm, and it is preferable to apply the reference value differently according to the process characteristics of the sewage treatment plant.
  • control action is continuously applied to check whether the group changes. If another group is repeated four or more times, it is determined that the control effect has occurred, and the control action of the group is stopped. The flowchart will be repeated. If the other group is not repeated four or more times, it is determined that a sufficient control effect has not occurred and the control action of the corresponding group is repeatedly repeated to check the group variation.
  • control strategy application step (S160) the control action determined according to the called control rule is applied to the sewage treatment plant.
  • the PID control mounted in the control device 10 is applied to the external carbon source.
  • an appropriate external carbon source flow rate is injected according to a series of calculation methods mounted inside the control device 10.
  • control strategy S160
  • the on-site verification was performed by applying the rule-based control device 10 and the control method according to the present invention to the sewage treatment plant process state diagnosis in an A 2 / O process having an average inflow rate of 18 m 3 / day.
  • FIG. 11 shows a result of not applying a rule-based control method according to a diagnosis of a process state of a sewage treatment plant, which is an actual site.
  • a total of 123 data were collected with a fixed airflow, with an average influent concentration of about 23.3 mg / L for NH 4 -N and 1.01 mg / L for NOx-N, with an average outflow concentration of NH 4 -N. About 12.95 mg / L for N and 4.12 mg / L for NOx-N.
  • nitrification did not occur smoothly, and it was confirmed that four groups lacking nitrification occurred predominantly in the state of hourly group derived from the collected data.
  • diagnosis of the sewage treatment plant should be applied.
  • FIG 12 it shows the result of applying the rule-based control method according to the diagnosis of the process state according to the present invention on the actual site.
  • the external carbon source flow was injected until the control strategy of the two groups, DO control and effluent NOx-N concentration reached 5 ppm.
  • the injected external carbon source flow rate was injected at a flow rate of about 19.7 mL / min by a series of calculations inside the controller 10.
  • the two groups lasted for 14 hours, after which the effluent NOx-N concentration reached 5 ppm and four groups occurred four times.
  • the DO setting value was first set to 1.5 ppm, but the effluent NH 4 -N concentration was less than 15 ppm and was changed to 1.2 ppm again.
  • the two groups were then maintained four times, injecting the same external carbon source flow rate, and the two groups were maintained for a total of 21 hours.
  • Such data are concentration values satisfying the effluent NH 4 -N 10mg / L and NOx-N 10mg / L, which were set as targets in the field verification, and through this, the rule-based control method according to the present condition diagnosis of sewage treatment plant It was confirmed that even if it is applied to the actual site, it can be sufficiently effective.
  • the present invention conducts qualitative diagnostic information on the treatment performance of the sewage treatment plant in order to improve the treatment performance of the sewage treatment plant, and provides a change amount of specific control parameters according to the diagnosis result to determine the process state of the sewage treatment plant. It can be widely used in the field of diagnosis and control.

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Abstract

Selon la présente invention , un appareil de commande à base de règles sur la base d'un diagnostic d'un état de processus dans une installation de traitement des eaux usées est prévu, l' appareil comprenant : une unité de collecte de données pour prendre , en tant qu' entrée , les données de qualité de l' eau entrant /sortante requises pour le diagnostic de l'état du processus de l'installation de traitement des eaux usées et données de fonctionnement du processus, ou en prenant en tant qu'entrée , les données historiques stockées dans une base de données ; une unité de prétraitement de données pour retirer la plage de la valeur absolue numérique de l' entrée de données dans l'unité de collecte de données de façon à pré - traiter les données ; une unité de unité de pré-traitement des données de manière à extraire des informations sur l'état du processus à partir de l' ensemble de données historiques de l' installation de traitement des eaux usées par application d'une technique d' analyse multi-variables pour les données pré-traitées; une unité de groupement de diagnostic de processus sur l' état de la performance de traitement actuel depuis les données réduites , et la dérivation d' une fonction discriminante pour discriminer l'état des données regroupées de manière à dériver le résultat de diagnostic sur la fonction discriminante et une unité stratégie de commande d'appel pour appeler la règle sur la variation de la variable de modération du processus données de fonctionnement pour le groupe qui indique le résultat de diagnostic obtenu par l' unité de diagnostic de processus.
PCT/KR2012/001271 2012-02-20 2012-02-20 Appareil de commande à base de règles et procédé de commande à base de règles en se basant sur le diagnostic de procédé état de station de traitement des eaux usées WO2013125737A1 (fr)

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CN105804187A (zh) * 2016-03-16 2016-07-27 宁波市江东精诚自动化设备有限公司 一种太阳能环保厕所
CN112085081A (zh) * 2020-09-02 2020-12-15 董萍 一种污水成分检测方法及系统
CN115140786A (zh) * 2022-07-08 2022-10-04 日照职业技术学院 一种智能化调整污水处理设备参数方法及系统
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CN108615115A (zh) * 2018-05-02 2018-10-02 山东汇贸电子口岸有限公司 一种政务数据采集流程的实现方法
KR101987897B1 (ko) * 2018-11-28 2019-06-11 부산가톨릭대학교 산학협력단 하수처리 시설의 공정 성능 모니터링 시스템
WO2020241959A1 (fr) * 2019-05-31 2020-12-03 주식회사 포스코아이씨티 Système de détection de données de commande anormales
KR102282847B1 (ko) * 2019-05-31 2021-07-27 주식회사 포스코아이씨티 제어데이터 이상 검출을 위한 시스템
CN111583058B (zh) * 2020-03-17 2023-09-05 国网浙江省电力有限公司杭州供电公司 一种配电网安全分析系统生成方法及装置
CN114967623B (zh) * 2022-06-07 2023-06-16 中国人民解放军陆军工程大学 城市地下污水处理厂规模优化与工艺选择方法

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