WO2013125737A1

WO2013125737A1 - Rule-based control apparatus and rule-based control method based on diagnosis of process state of sewage treatment plant

Info

Publication number: WO2013125737A1
Application number: PCT/KR2012/001271
Authority: WO
Inventors: 김창원; 김예진; 김효수
Original assignee: 부산대학교 산학협력단
Priority date: 2012-02-20
Filing date: 2012-02-20
Publication date: 2013-08-29
Also published as: KR20130095405A

Abstract

According to the present invention, a rule-based control apparatus based on a diagnosis of a process state of a sewage treatment plant is provided, the apparatus comprising: a data collecting unit for taking, as an input, inflow/outflow water quality data required for the diagnosis of the process state of the sewage treatment plant and process operation data, or taking, as an input, the historical data set stored in a database; a data pre-processing unit for removing the range of the numerical absolute value of the data input into the data collecting unit so as to pre-process the data; a data reduction unit for receiving the data pre-processed by the data pre-processing unit and reducing the pre-processed data so as to extract information on the process state from the historical data set of the sewage treatment plant by applying a multi-variable analysis technique to the pre-processed data; a process diagnosis unit for grouping the reduced data so as to derive a diagnosis result on the state of the current treatment performance from the reduced data, and deriving a discriminant function for discriminating the state of the grouped data so as to derive the diagnosis result on the discriminant function; and a control strategy call unit for calling the rule on the variation of the moderating variable of the process operation data by a simulation test preset for the group that indicates the diagnosis result derived by the process diagnosis unit.

Description

Rule-based control device and method according to the diagnosis of process status of sewage treatment plant

The present invention relates to a rule-based control device and method according to the sewage treatment plant process status diagnosis. In more detail, in order to improve the treatment performance of sewage treatment plant, it is necessary to derive qualitative diagnostic information on the treatment performance state of the sewage treatment plant, and provide a change amount of specific control variables according to the diagnosis result. 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.

In spite of the continuous research on ICA (Instrumentation Control and Automation) technology for optimizing the treatment performance and operation of sewage treatment plant for the past 40 years, it has been applied to the actual site, but it still diagnoses the process performance and condition of sewage treatment plant. And the application of appropriate control strategies have been relied upon by the sewage treatment plant operator's empirical knowledge.

Since the driver's empirical knowledge is many cases based on the operational know-how obtained by working for a long time in the sewage treatment plant, the process often becomes more unstable because it is not an accurate solution to the process operation problem.

In addition, when an experienced operator changes or retires to a new sewage treatment plant, there are frequent cases where the empirical knowledge is not properly transferred, and the condition of the process is diagnosed, and the appropriate control is performed to improve the treatment performance of the process. There is no perpetuity of operational knowledge to improve.

Recently, a diagnostic methodology that utilizes expert knowledge has been reported to overcome the limitations of process status diagnosis and control of sewage treatment plants based on such experiences and knowledge of drivers.

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.In recent years, various solutions for extracting useful information from a large amount of data are available. Through the application of data mining techniques, various techniques for extracting information on the process operation status in real time and suggesting to the operator have been proposed.

However, the disadvantage of these technologies is that even though it is possible to detect abnormal operating conditions of the sewage treatment plant through process status diagnosis, it is necessary to quantitatively change the operating conditions of a series of adjustable devices such as aeration, sludge return flow, etc. It does not involve the presentation of the operating conditions.

After diagnosing the process condition and confirming that the process problem is caused by some cause, in order to solve this problem, the control action to change the process operation condition by changing the control variable must be accompanied. Various control methodologies have been developed to apply to the actual field.

According to traditional control methodologies, the traditional P, PI and PID (Proportional Integral), which present a setpoint of a control variable and reach a new setpoint by changing the value of a control variable, as in a chemical process that can be easily illustrated. 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.

In particular, 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.

Recently, various methodologies have been proposed to derive the setpoints, which are based on mass balances or set the target value or present the setpoint using mathematical and statistical models and report the case of control through the PID controller. However, the interval at which new set- tings are derived is limited to time units, resulting in equipment wear caused by frequent fluctuations in equipment operating conditions and lack of situational awareness of how the sewage treatment plant process is operating. It has not been evaluated.

In addition, the 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.

Therefore, it is urgent to develop an appropriate control strategy method to keep the process stable based on comprehensive diagnosis result information on the current process state by recognizing that the existing diagnosis and control have been separately performed.

In order to solve this 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.

According to the present invention, 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; Receiving the preprocessed data by the data preprocessing unit and applying the multivariate analysis method to the preprocessed data to reduce the preprocessed data to extract information on the process state inherent in past data sets of the sewage treatment plant. 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.

Meanwhile, 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).

On the other hand, 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. .

On the other hand, 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.

Meanwhile, 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.

On the other hand, the 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.

According to the present invention, the data collection step of 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 preprocessing step of preprocessing the respective data by removing a range of numerical absolute values of each data from the received data; A data reduction step of receiving the preprocessed data and applying a principal component analysis to analyze the variances of the variables constituting the preprocessed data to derive a principal component that can be reduced for each common variance; 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. A process diagnosis step of determining which group among the grouped groups corresponds to the main components generated by the input data, and deriving a diagnosis result regarding treatment performance and process operation of the sewage treatment plant using the determined groups; Derived by simulation of the control rule by calling a control rule for 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 the group for the derived diagnostic result A control strategy call step of calling up a change amount of a control variable of the process operation data; And a control strategy applying step of applying a control action determined according to the called control rule to a sewage treatment plant.

On the other hand, 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.

Existing process status diagnostics provide only qualitative information on the current process status (e.g. high runoff BOD / high effluent TN / low effluent TP, etc.) so that the actual operator can perform some control actions to solve the problem. Although no solution has been proposed, the present invention provides comprehensive information on the current process state through process state diagnosis (eg high influent load, low DO concentration, and therefore high effluent NH ₄ -N concentration). Etc.), and by providing the quantitative figures on how to maintain the new operating conditions according to the diagnosis result, it is possible to present problems and solutions for process safety by systemizing them, and systemize this to prevent the operator from being present. In addition, there is an advantage that the process can be automatically maintained stable.

In addition, although the existing inventions have been limited to diagnosis or control and could not be applied to the actual process by interlocking diagnosis and control, 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.

In addition, 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.

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.

2 is a configuration diagram showing in detail the operating state of the rule-based control device of 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.

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.

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.

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.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. First, in adding reference numerals to the components of each drawing, it should be noted that the same reference numerals are used as much as possible even if displayed on different drawings. In describing the present invention, when it is determined that the detailed description of the related well-known configuration or function may obscure the gist of the present invention, the detailed description thereof will be omitted.

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 7 is a control strategy call of Figure 6 8 is a flow chart showing the steps in detail, Figure 8 is a graph showing the simulation results for the control strategy call of the two groups in Figure 7, Figure 9 is a control strategy call for the three groups in Figure 7 10 is a graph showing simulation results, and 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.

1 and 2, 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. 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. In addition, it is possible to check the operating status of the currently operated sewage treatment plant process equipment. 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. After the input is converted into the principal component, 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.

Referring to FIG. 3, a detailed screen of the process diagnosis unit 500 is shown. Looking at the process status diagnosis screen, the upper part is a schematic diagram of the sewage treatment plant process, and the middle part is a table showing the corresponding group for each hour as time passes, and 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.

4, 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. Looking at the detailed screen thereof, 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. In order to maintain this set value, 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.

Referring to FIG. 5, a detailed screen of the external carbon source flow rate injection in the control strategy by the control strategy call unit 600 is shown. 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.

6, there is shown a flow chart of the rule-based control method according to the sewage treatment plant process status diagnosis according to the present invention.

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. It includes a control strategy application step (S160) to automatically apply the control strategy to the process.

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.

First, 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.

For this purpose, 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.

Equation 1

At this time,

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.

Therefore, principal component analysis is performed using preprocessed data (inflow and outflow NH ₄ -N, NOx-N, PO ₄ -P, DO, MLSS, ORP total 9 data items) As a result of selecting one or more components, three main components (PC ₁ , PC ₂ , PC ₃ ) were selected. This means that the 9-dimensional dataset was transformed into a new 3-dimensional dataset. Principal component coefficients obtained through the principal component analysis are shown in [Table 1].

Table 1

Therefore, in the data abbreviation step S130, when new data is input, the principal component value is finally calculated based on each preprocessed item value and principal component coefficient value.

For example, in the case of PC ₁ , 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.

Subsequently, in the process diagnosis step S140, 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. To derive the results. 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.

In more detail, through the K-means cluster analysis, 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.

TABLE 2

Diagnosis results through the process characteristics of each group can be presented to the operator with the following qualitative information.

Group 1: Under normal conditions, the influent load is below normal and the nitrogen and phosphorus removal is smooth.

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.

4 Group: Nitrification and phosphorus removal not as smooth due to low DO at moderate influent loads.

Then, in the 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.

In the 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. In this case, each rule implies a change amount of a control variable by a mathematical simulation performed periodically. Doing.

7, the control strategy call step (S150) of the control method is shown in detail. First, 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.

Which control actions are performed according to the diagnosis results derived in advance for each group is different, and 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. In the case of Figure 7, 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.

After the control action of each group is performed, 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.

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.

1 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.

3 Group Control Behavior: Unsafe process conditions require the control of DO and injection of external carbon sources as needed.

4 Group Control Behavior: Unsafe process conditions require the control of DO and injection of external carbon sources as needed.

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. When comparing the initial value and the simulation result of DO 50% increase in FIG. 9, it can be seen that the effluent NH ₄ -N concentration average decreases by about 15.4%, and 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.

When constructing the strategy used in the control strategy call step (S150), to perform the pre-simulation using a mathematical model to derive the appropriate set value for each of the above-mentioned group, the skilled driver according to the characteristics of the sewage treatment plant process The empirical knowledge of the system, and calculations based on the balance of mass balance between input and output, are also applicable.

In addition, 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.

In the 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.

(1) As an example, when the process status diagnosis result is maintained at least four times in three groups, 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. In this case, 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.

(2) After the control is performed, it is checked whether the effluent NH ₄ -N concentration exceeds the target value of 10ppm, and if it is exceeded, the group DO is checked while maintaining the current DO set value. The target value at this time is also preferably applied differently to reflect the process characteristics of the sewage treatment plant.

(3) If the effluent NH ₄ -N concentration does not exceed the target value of 10ppm, an external carbon source is injected if the effluent NOx-N concentration exceeds the target value. At this time, 10 ppm, which is a target value of NOx-N, is also preferably applied differently to reflect the process characteristics of the sewage treatment plant.

(4) In this way, the 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.

And a control strategy applying step of applying the control action determined according to the called control rule to the sewage treatment plant.

In the control strategy application step (S160), the control action determined according to the called control rule is applied to the sewage treatment plant. In this case, in the case of DO control, the PID control mounted in the control device 10 is applied to the external carbon source. In this case, an appropriate external carbon source flow rate is injected according to a series of calculation methods mounted inside the control device 10.

In the application of the control strategy (S160), it is preferable to use an appropriate controller by identifying the type of the sewage treatment plant process and the driveable equipment.

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.

Referring to FIG. 11, it 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. In general, it was confirmed that 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. Through this, if the status is maintained for a long time, it was determined that the process operation may be gradually worsened and the process abnormality may be long, and the rule-based control method according to the present invention diagnosis of the sewage treatment plant should be applied.

Referring to Figure 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.

First, four groups were generated four times, and control was performed using the DO setting value of 1.5 ppm as the first control strategy for the group. After the control was carried out, the group variation was checked, and the three groups were generated three times and one group was generated twice, so that the control behavior was maintained without the group variation, and when the effluent NH ₄ -N concentration was lower than 15 ppm according to the flowchart, DO The set value was changed to 1.2ppm, and this control action was performed in one period of two occurrences of one group.

Thereafter, three groups were generated four times, and the control behavior of four groups was automatically stopped, and the DO setting value was changed to 1.5 ppm according to the three group control flowchart, and the set value of 1.5 ppm was set because the effluent NH ₄ -N concentration was higher than 10 ppm. The change in the group was checked while maintaining.

Thereafter, one group occurred four times, and accordingly, the three group control actions were stopped, and the process was operated at a fixed amount of air flow without DO control according to the one group control flowchart.

After the first group, two groups were maintained four times, and 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. At this time, 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.

According to the four-group control flowchart, 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.

Thereafter, three groups occurred once, one group was maintained four times, and four group control was stopped, and DO control was not applied.

After 40 hours, 2 groups 2, 4 groups 1 side, 2 groups 2 times were derived continuously, 4 groups were maintained 4 times, and 4 groups control was performed, and DO was set according to the effluent NH ₄ -N concentration comparison. The value was applied at 1.2 ppm.

After 4 groups, 3 groups occurred 4 times, and the 3 group control strategy was applied again. The DO setting value of 1.5 ppm was maintained, and the effluent NH ₄ -N concentration appeared to be lower than 10 ppm. It was injected at a flow rate of about 32.1 mL / min by a series of calculations inside the controller 10.

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.

Thereafter, 4 groups occurred 3 times and 2 groups occurred 2 times, and 4 groups occurred 4 times, and control was performed at a DO set value of 1.2 ppm according to the 4 group control flowchart.

Eight control actions were performed in about 90 hours, with mean influent concentrations of about 26.5 mg / L for NH ₄ -N and 0.91 mg / L for NOx-N, with mean concentrations of NH _4- About 9.6 mg / L for N and 8.85 mg / L for NOx-N.

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.

In addition, according to the comprehensive process status diagnosis, appropriate control actions are applied based on a series of rules, so that the process control actions are maintained for at least 4 hours and 20 hours or more, thereby reducing the frequent fluctuations of the process compared to the real time control of the hour. It was concluded that this could lead to the effect of reducing the load on equipment such as process equipment and pumps.

The above description is merely illustrative of the present invention, and those skilled in the art to which the present invention pertains may various modifications without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed herein are not intended to limit the present invention but to describe the present invention, and the spirit and scope of the present invention are not limited by these embodiments. It is intended that the scope of the invention be interpreted by the following claims, and that all descriptions within the scope equivalent thereto shall be construed as being included in the scope of the present invention.

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.

Claims

A data collection unit for receiving inflow / outflow water quality data and process operation data necessary for diagnosing a process state of a sewage treatment plant or receiving a historical data set stored in a 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;

Receiving the preprocessed data by the data preprocessing unit and applying the multivariate analysis method to the preprocessed data to reduce the preprocessed data to extract information on the process state inherent in past data sets of the sewage treatment plant. 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 on the amount of change of the control variable of the process operation data according to a preset simulation according to a group representing a diagnosis result derived from the process diagnosis unit; Rule-based control device according to.
The method of claim 1,

The rule-based control apparatus according to the process state diagnosis of the sewage treatment plant further includes a control strategy application unit for applying the control action determined by the control strategy calling unit to the control strategy call unit. Rule-based control device according to the diagnosis of the process state of the sewage treatment plant, characterized in that for transmitting an electrical signal by the control action determined by the actuator (actuator).
The method according to claim 1 or 2,

The data pre-processing unit standardizes the received data by the following formula 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. Rule-based control device according to the diagnosis of process status of plant.

At this time,
Means the measured value of each data,
Is the mean of that data item,
Means the standard deviation of the data item.
The method according to claim 1 or 2,

The data abbreviation unit performs dimensional reduction of a plurality of inflow / outflow water quality data and process operation data with fewer abbreviated principal components by principal component analysis, which is one of the multivariate analysis methods. Rule-based control device based on condition diagnosis.
The method of claim 4, wherein

The process diagnosis unit performs a 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. After that, a discriminant function to be used as a means for determining to which group the new inflow / outflow water quality data and process operation data can be allocated is derived by using Fisher's linear discriminant analysis, and the sewage treatment plant using the assigned group is used. Rule-based control device according to the diagnosis of the process status of the sewage treatment plant, characterized in that for performing the diagnosis of treatment performance and process operation.
The method of claim 5,

The 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, and the process diagnosis unit Calling the control rule stored in advance in the control strategy call unit according to the process state derived from the call to the amount of change of the control variable of the process operation data derived by the simulation of the control rule Rule-based control device according to the diagnosis of process status of sewage treatment plant.
A data collection step of receiving inflow / outflow water quality data and process operation data necessary for diagnosing a process state of a sewage treatment plant or a historical data set stored in a database unit;

A data preprocessing step of preprocessing the respective data by removing a range of numerical absolute values of each data from the received data;

A data reduction step of receiving the preprocessed data and applying a principal component analysis to analyze the variances of the variables constituting the preprocessed data to derive a principal component that can be reduced for each common variance;

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. A process diagnosis step of determining which group among the grouped groups corresponds to the main components generated by the input data, and deriving a diagnosis result regarding treatment performance and process operation of the sewage treatment plant using the determined groups;

Derived by simulation of the control rule by calling a control rule for 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 the group for the derived diagnostic result A control strategy call step of calling a change amount of a control variable of the process operation data that has been performed; And

And a control strategy applying step of applying the control action determined according to the called control rule to the sewage treatment plant.
The method of claim 7, wherein

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. Rule-based control method according to the diagnosis of the process status of the sewage treatment plant, characterized in that the main components generated by the input to compare the magnitudes of the values calculated by the discriminant function of each group and assign them to the group representing the largest value.