Online detection intelligent operation maintenance system and operation method

Abstract

The invention provides an on-line detection intelligent operation maintenance system and an operation method. An inflow water amplitude prediction module is used for predicting inflow water quality amplitude Z; the detection decision module is used for judging whether the online detection system is operated or not based on the real-time inflow water quality amplitude; the sampling detection module is a sampling detection device; and the medicament monitoring module monitors the medicament usage amount of the online detection equipment in real time based on the parameters of the medicament container and the used parameters, and calculates the medicament residual detection times Nd and residual detection time Td of the online detection equipment. The on-line detection intelligent operation maintenance system creatively introduces water quality amplitude prediction according to the essential target of water quality detection, applies the water quality amplitude prediction to operation management of on-line detection equipment, and combines a decision analysis method to ensure stable process control and stable discharged water quality on the premise of ensuring stable process control and stable discharged water quality. And the operation frequency of the online detection equipment is reduced, so that the operation cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of sewage treatment, and in particular relates to an online detection intelligent operation and maintenance system and an operation method. Background Art

[0002] As environmental protection supervision becomes increasingly stringent, online detection equipment has been widely used in sewage treatment plant inlet and outlet water quality monitoring to ensure that operators can timely understand water quality changes, so as to timely adjust the process and equipment operating parameters to ensure that emissions meet standards. In large and complex sewage treatment systems, in order to achieve real-time monitoring of water quality, a large number of online detection equipment needs to be deployed, resulting in a significant increase in operating and maintenance costs and maintenance workload.

[0003] The operating costs of online testing equipment include reagents, power consumption, parts replacement, waste liquid disposal and maintenance labor, etc., and the above operating costs are almost linearly positively correlated with the number of online testing equipment invested and the frequency of testing, that is, the more equipment there are and the higher the frequency of testing, the higher the operating and maintenance costs. In order to meet increasingly stringent environmental supervision and emission standards, it is necessary to ensure the number and frequency of online testing equipment.

[0004] The supply of reagents is also a problem. For example, based on the national standard method, online COD, TP, TN and ammonia nitrogen require at least three reagents for each indicator test, in addition to purified water, standard solution, etc. There are many online testing devices, and the number of reagent bottles placed in the testing cabinet is even greater. The dosage and inventory of each reagent are different. It is difficult for operation and maintenance personnel to ensure that each reagent for each indicator is replenished in time. Insufficient reagents often lead to interruption of testing or inaccurate test data, resulting in short-term failure in operation control, which is not conducive to the stability of water plant operation.

[0005] In summary, reducing the operating costs of online detection equipment and improving its stable supply of reagents are practical problems that need to be solved for the current large-scale and widespread application of online detection equipment. Summary of the invention

[0006] In view of this, the present invention aims to overcome the defects in the prior art and proposes an online detection intelligent operation and maintenance system and an operation method.

[0007] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0008] The present invention provides an online detection intelligent operation and maintenance system, including a water inlet amplitude prediction module, a detection decision module, a sampling detection module and a medicine monitoring module;

[0009] The inlet water amplitude prediction module is used to predict the inlet water quality amplitude Z;

[0010] The detection decision module determines whether the online detection system is running based on the real-time inlet water quality amplitude;

[0011] The sampling and detection module is a sampling and detection device;

[0012] The medicine monitoring module monitors the medicine usage of the online detection device in real time based on the parameters of the medicine container and the parameters used, and calculates the remaining number of medicine detection times Nd and the remaining detection time Td of the online detection device.

[0013] Furthermore, the inlet water quality amplitude Z is the amplitude of the water quality change before and after a prediction cycle; the range of the inlet water quality amplitude Z is shown in formula (I):

[0014] Z=±(D max -D min ) Formula (I)

[0015] Among them, D max is the historical maximum value of the water quality, D min This is the historical minimum value of the water quality. When the predicted amplitude of the water quality exceeds the above range, the system will issue an early warning.

[0016] Furthermore, the prediction period of the inlet water quality amplitude Z is 30 minutes, and an inlet water quality amplitude is generated every 30 minutes.

[0017] Furthermore, the value of the inlet water quality amplitude Z is as shown in formula (II) and formula (III):

[0018]

[0019] Among them, x is the input layer neuron, i is the ordinal number of the input layer neuron, which takes a value of 1-m, y is the calculation result of the hidden neuron, j is the ordinal number of the hidden layer neuron, which takes a value of 1-n, 2≤n≤m. f(·) is the activation function from the input layer to the hidden layer, g(·) is the activation function from the hidden layer to the output layer, b1 is the bias of the hidden layer neuron, b2 is the bias of the output neuron, w1 is the weight coefficient of the hidden layer neuron, and W2 is the weight coefficient of the output neuron. The bias and weight coefficient are automatically assigned and optimized by the algorithm model through data training.

[0020] Furthermore, if the real-time inlet water quality amplitude Z0 of the detection decision module satisfies formula (IV), the online detection equipment is started to implement sampling and detection operations. If it does not meet the requirements, there is no need to perform sampling and detection. Formula (IV) is as follows:

[0021]

[0022] Wherein, h is the rounded-up value of the hydraulic retention time (HRT) of the treatment system or treatment unit, k is the ordinal number of the influent water quality amplitude, k=0 is recorded as the real-time amplitude prediction value, k<0 is recorded as the historical amplitude prediction value, and k greater than 0 is recorded as the future amplitude prediction value.

[0023] Furthermore, the inlet water quality amplitude is transmitted to the subsequent monitoring section, and the time for sampling and detection in the subsequent monitoring section is T1=T0+Hr, wherein T0 is the time when the sampling detection is issued at the inlet end, and Hr is the hydraulic retention time from the inlet to the subsequent process monitoring section.

[0024] Furthermore, the calculation of the remaining number of drug detections Nd is shown in formula (V):

[0025]

[0026] The calculation of the remaining detection time Td is shown in formula (VI):

[0027]

[0028] Where count(t) is the proprietary frequency distribution function of the online detection equipment, and its value range is 0 and 1; to is the initial time, which is set by manual triggering; V s is the volume consumed by a single test of the drug, V o The initial volume of the medicine, set by manual triggering.

[0029] Furthermore, the warning rules for the remaining number of reagent detections Nd and the remaining detection time Td are set, and according to the analysis results, warning information is issued to remind the operating personnel to replenish the reagents in time.

[0030] Furthermore, the medicine is stored in a medicine bottle with a scale, which makes it easy for operators to observe the inventory of the medicine and modify the operating parameters of the program according to the dosage and shelf life of the medicine; the effective volume of the medicine bottle is 500-5000mL, and the scale is accurate to 10-100mL; the operator decides to add medicine according to the remaining detection time or the remaining number of detections of the medicine currently displayed.

[0031] The sampling and detection module is composed of a sampling pump, a filter, a sample cup and a detection instrument. The sampling pump is a peristaltic pump and / or a submersible pump. The filter is a self-cleaning filter, and the filter medium is a filter screen with a pore size of 10-200μm. According to the properties of the wastewater and the sampling section, filter screens with different pore sizes and materials (polypropylene, polyethylene, carbon steel, etc.) are selected. When the test index of the water sample (such as the total outlet water sample) contains SS (suspended particles), no filtration is required, and direct sampling and testing are performed. The detection instrument is an online chemical detection instrument or a probe-type detection sensor based on spectral or ion analysis. When a probe-type sensor is used, no drug warning module is required.

[0032] The present invention also provides an operation method of an online detection intelligent operation and maintenance system, which is characterized by comprising the following steps:

[0033] (1) The inlet water amplitude prediction module is used to predict the inlet water quality amplitude in real time and send the prediction results to the detection decision module;

[0034] (2) The detection decision module determines whether to perform the detection operation based on the calculation and analysis, and sends the decision result and execution time to the sampling detection module;

[0035] (3) The sampling detection module selects standby or operation within a corresponding time range according to the received working condition signal;

[0036] (4) During operation, the detection instrument performs detection operations on the water samples collected by the sampling device to obtain measurement results;

[0037] (5) The reagent warning module monitors the reagent status of the online detection equipment, displays monitoring information or issues warning information, and reminds operators to replenish or prepare reagents in time.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] The online detection intelligent operation and maintenance system described in the present invention creatively introduces water quality amplitude prediction based on the essential goal of water quality detection, and applies it to the operation management of online detection equipment. It is combined with decision analysis methods to reduce the operation frequency of online detection equipment while ensuring stable process control and stable outflow water quality, thereby reducing its operating costs.

[0040] The online detection intelligent operation and maintenance system described in the present invention has developed an online detection equipment reagent monitoring and early warning system, which enables operation and maintenance personnel to fully understand the status of online equipment reagents in real time and replenish reagents in time according to early warning signals or actual conditions, thereby avoiding the problem of insufficient online detection equipment reagents leading to interruption of the detection process.

[0041] The online detection intelligent operation and maintenance system described in the present invention realizes the informatization and intelligence of the operation of online detection equipment, significantly improves its operation and maintenance efficiency, and reduces the operation cost and operation labor intensity of the online detection equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The figure is a flow chart of the line detection intelligent operation and maintenance system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] Unless otherwise defined, the technical terms used in the following examples have the same meanings as those generally understood by those skilled in the art to which the present invention belongs. The test reagents used in the following examples, unless otherwise specified, are all conventional biochemical reagents; the experimental methods, unless otherwise specified, are all conventional methods.

[0044] The present invention will be described in detail below with reference to the embodiments.

[0045] Example 1

[0046] The online detection equipment system of a municipal sewage treatment plant includes inlet sampling detection and total outlet sampling detection. The total outlet sampling detection (COD, TN, ammonia nitrogen and TP) is sampled and tested once an hour according to environmental protection supervision requirements, and sampling is performed regularly. Therefore, it does not contain a sampling decision module, but the reagent monitoring module is still used in it. The inlet sampling detection (COD, TN, ammonia nitrogen and TP) fully applies the online detection equipment intelligent operation and maintenance system of the present invention.

[0047] The water quality amplitude prediction module uses the inflow water quality, inflow flow, temperature, rainfall, and calendar data of the past three years (corresponding to x in Formula II). i ), for each indicator (COD, TN, TP and ammonia nitrogen), establish its influent water quality amplitude prediction model, and predict its influent water quality amplitude from the current hour to the future HRT (hydraulic retention time of sewage treatment system).

[0048] The sampling decision module analyzes the predicted value of the inlet water quality amplitude according to the analysis method of formula (IV). When it meets the requirements of formula (IV), the online equipment is started for sampling and detection. Otherwise, the online equipment is in standby state.

[0049] Taking the prediction of water inlet TN amplitude as an example, x i The corresponding historical (last three years) data (i=1, 2, 3, 4, 5, representing historical total nitrogen TN, water inlet flow Q, temperature T, rainfall R, and calendar D, respectively) were sorted and trained to obtain its prediction model Z=(x1, x2, x3, x4, x5). Taking the sampling decision at a certain moment as an example, the overall HRT of the water treatment system is 7 hours (the fastest time from water inlet to water outlet). The amplitude prediction value at the current moment (-4.64), the historical amplitude prediction values ​​generated in the last 7 hours (-2.25, 5.26, -4.63, 4.17, 7.99, -6.6, 5.08), and the amplitude values ​​generated in the next 7 hours (3.62, 1.99, -0.16, 6.69, 4.99, 0.97, -1.83) are substituted into formula (IV) to obtain a calculated result of 5.92. The absolute value of the current predicted amplitude of -4.64 is less than 5.92, so there is no need to start the online detection equipment.

[0050] The sampling device consists of a submersible pump, a sampling tube, and a self-cleaning filter (filtration accuracy is 50μm). The filtered water sample is sent to the sample cup, and the sampling tube of the online detection instrument quantitatively draws water samples from the sample cup for detection.

[0051] The reagent monitoring module performs statistics and calculations on the detection and operation information (cumulative detection times, inventory of various reagents) of the inlet and outlet online detection equipment, and gives the remaining detection times of various reagents. When the remaining detection times of the reagent N is less than 24 times, a first-level warning message is issued to remind the operation personnel to replenish the reagent in time; when the remaining detection times of the reagent N is less than 12 times, a second-level warning message is issued to remind the operation and maintenance personnel to replenish the reagent as soon as possible, otherwise there is a risk of interruption of the operation of the online detection equipment.

[0052] As shown in Table 1, through the intelligent operation and maintenance system of online detection equipment, compared with the conventional detection once per hour and 24 times a day, after the implementation of the present invention, the frequency of online water inlet detection is reduced by about 50% on average (the blank space in the data is standby without detection), and the more stable the water quality index, the more the detection frequency is reduced, that is, the more cost is saved. At the same time, through the monitoring and early warning of the reagents, the reagents of the online detection equipment can be replenished in time, avoiding the interruption of the detection process due to insufficient reagents.

[0053] Table 1 Daily report of water inlet online detection data

[0054]

[0055]

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An online detection intelligent operation and maintenance system, characterized by: It includes water inlet amplitude prediction module, detection decision module, sampling detection module and reagent monitoring module; The inlet water amplitude prediction module is used to predict the inlet water quality amplitude Z; The detection decision module determines whether the online detection system is running based on the real-time inlet water quality amplitude; The sampling and detection module is a sampling and detection device; The medicine monitoring module monitors the medicine usage of the online detection device in real time based on the parameters of the medicine container and the parameters used, and calculates the remaining number of medicine detection times Nd and the remaining detection time Td of the online detection device.

2. The online detection intelligent operation and maintenance system according to claim 1 is characterized in that: The inlet water quality amplitude Z is the water quality change amplitude before and after a prediction cycle; the range of the inlet water quality amplitude Z is shown in formula (I): Z=±(D max -D min ) Formula (I) Among them, D max is the historical maximum value of the water quality, D min This is the historical minimum value of the water quality. When the predicted amplitude of the water quality exceeds the above range, the system will issue an early warning.

3. The online detection intelligent operation and maintenance system according to claim 1 is characterized in that: The prediction period of the inlet water quality amplitude Z is 30 minutes, and an inlet water quality amplitude is generated every 30 minutes.

4. The online detection intelligent operation and maintenance system according to claim 1 is characterized in that: The value of the inlet water quality amplitude Z is shown in formula (II) and formula (III): Where x is the input layer neuron, i is the ordinal number of the input layer neuron, which takes a value of 1-m, y is the calculation result of the hidden neuron, j is the ordinal number of the hidden layer neuron, which takes a value of 1-n, 2≤n≤m. f(·) is the activation function from the input layer to the hidden layer, g(·) is the activation function from the hidden layer to the output layer, b1 is the bias of the hidden layer neuron, b2 is the bias of the output neuron, w1 is the weight coefficient of the hidden layer neuron, and W2 is the weight coefficient of the output neuron.

5. The online detection intelligent operation and maintenance system according to claim 1 is characterized in that: If the real-time inlet water quality amplitude Z0 of the detection decision module satisfies formula (IV), the online detection equipment is started to implement sampling and detection operations. If it does not meet the requirements, there is no need to perform sampling and detection. Formula (IV) is as follows: Wherein, h is the rounded-up value of the hydraulic retention time (HRT) of the treatment system or treatment unit, k is the ordinal number of the influent water quality amplitude, k=0 is recorded as the real-time amplitude prediction value, k<0 is recorded as the historical amplitude prediction value, and k greater than 0 is recorded as the future amplitude prediction value.

6. The online detection intelligent operation and maintenance system according to claim 5 is characterized in that: The inlet water quality amplitude is transmitted to the subsequent monitoring section, and the time for sampling and detection in the subsequent monitoring section is T1=T0+Hr, wherein T0 is the time when the sampling detection is issued at the inlet end, and Hr is the hydraulic retention time from the inlet to the subsequent process monitoring section.

7. The online detection intelligent operation and maintenance system according to claim 1 is characterized in that: The calculation of the remaining number of drug detection times Nd is shown in formula (V): The calculation of the remaining detection time Td is shown in formula (VI): Where count(t) is the proprietary frequency distribution function of the online detection equipment, and its value range is 0 and 1; to is the initial time, which is set by manual triggering; V s is the volume consumed by a single test of the drug, V o The initial volume of the medicine, set by manual triggering.

8. The online detection intelligent operation and maintenance system according to claim 7 is characterized in that: Set the warning rules for the remaining number of reagent detections Nd and the remaining detection time Td, and issue warning information based on the analysis results to remind operators to replenish reagents in time.

9. The online detection intelligent operation and maintenance system according to claim 8, characterized in that: The medicine is stored in a medicine bottle with a scale, which is convenient for the operator to observe the inventory of the medicine and modify the operating parameters of the program according to the dosage and shelf life of the medicine; the effective volume of the medicine bottle is 500-5000mL, and the scale is accurate to 10-100mL; the operator decides to add medicine according to the remaining detection time or the remaining number of detections of the medicine currently displayed.

10. The operating method of the online detection intelligent operation and maintenance system according to any one of claims 1 to 9, characterized in that: The steps include: (1) The inlet water amplitude prediction module is used to predict the inlet water quality amplitude in real time and send the prediction results to the detection decision module; (2) The detection decision module determines whether to perform the detection operation based on the calculation and analysis, and sends the decision result and execution time to the sampling detection module; (3) The sampling and detection module selects standby or operation within a corresponding time range according to the received working condition signal; (4) During operation, the detection instrument performs detection operations on the water samples collected by the sampling device to obtain measurement results; (5) The reagent warning module monitors the reagent status of the online detection equipment, displays monitoring information or issues warning information, and reminds operators to replenish or prepare reagents in time.

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