An online detection intelligent operation maintenance system and operation method

Abstract

The application provides an online detection intelligent operation maintenance system and an operation method. The water inflow amplitude prediction module is used for predicting water inflow quality amplitude Z. The detection decision module is used for judging whether the online detection system is operated based on real-time water inflow quality amplitude. The sampling detection module is a sampling detection device. The medicament monitoring module is used for monitoring the medicament usage amount of the online detection equipment in real time based on parameters of a medicament container and parameters of usage, and calculating the remaining detection times Nd and the remaining detection time Td of the medicament of the online detection equipment. According to the essential target of water quality detection, the online detection intelligent operation maintenance system creatively introduces water quality amplitude prediction, and applies the water quality amplitude prediction to the operation management of the online detection equipment. In combination with a decision analysis method, the operation frequency of the online detection equipment is reduced under the premise of guaranteeing process control stability and stable effluent quality, so that the operation cost is reduced.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment, and in particular relates to an online detection intelligent operation and maintenance system and its operation method. Background Technology

[0002] With increasingly stringent environmental regulations, online monitoring equipment has been widely used in wastewater treatment plant influent and effluent water quality monitoring to ensure that operators can promptly understand water quality changes and adjust process and equipment operating parameters in a timely manner to ensure compliant discharge. In large and complex wastewater treatment systems, a large number of online monitoring devices need to be deployed to achieve real-time water quality monitoring, resulting in a significant increase in operation and maintenance costs and workload.

[0003] The operating costs of online monitoring equipment include reagents, electricity consumption, parts replacement, waste disposal, and maintenance labor. Furthermore, these operating costs are almost linearly positively correlated with the number of online monitoring devices and the frequency of testing; that is, the more devices there are and the higher the testing frequency, the higher the operating and maintenance costs. To meet increasingly stringent environmental regulations and emission standards, ensuring a sufficient number of online monitoring devices and a sufficient testing frequency is essential.

[0004] Reagent supply is also an issue. For example, online testing of COD, TP, TN, and ammonia nitrogen based on national standards requires at least three reagents for each indicator, in addition to purified water and standard solutions. With numerous online testing devices, the number of reagent bottles placed in the testing cabinets is even greater. The dosage and stock of each reagent vary, making it difficult for maintenance personnel to ensure that each reagent for each indicator is replenished in a timely manner. Reagent shortages often lead to testing interruptions or inaccurate data, causing short-term operational control failures and hindering the stability of water plant operations.

[0005] In summary, reducing the operating costs of online detection equipment and improving the stable supply of its reagents are practical problems that need to be solved for the 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 propose an online detection intelligent operation and maintenance system and its operation method.

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

[0008] This invention provides an online detection intelligent operation and maintenance system, including an influent amplitude prediction module, a detection decision module, a sampling detection module, and a reagent monitoring module;

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

[0010] The aforementioned detection decision module determines whether the online detection system is operating based on the real-time influent water quality amplitude.

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

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

[0013] Furthermore, the influent water quality amplitude Z is the range of water quality change before and after one prediction period; the range of the influent water quality amplitude Z is shown in equation (I):

[0014] Z=±(D max -D min Formula (Ⅰ)

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

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

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

[0018]

[0019] Where x represents the input layer neuron, i is the ordinal number of the input layer neuron (ranging from 1 to m), y represents the calculated result of the hidden neuron, and j is the ordinal number of the hidden layer neuron (ranging from 1 to n, where 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 calculated hidden layer neuron, b2 is the bias of the calculated output neuron, w1 is the weight coefficient of the calculated hidden layer neuron, and w2 is the weight coefficient of the calculated output neuron. The bias and weight coefficients are automatically assigned and optimized by the algorithm model through data training.

[0020] Furthermore, if the real-time influent water quality amplitude Z0 of the detection decision module satisfies equation (Ⅳ), then the online detection equipment is activated to perform sampling and detection operations; if it does not satisfy the equation, then sampling and detection are not required. Equation (Ⅳ) is shown below:

[0021]

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

[0023] Furthermore, the amplitude of the influent water quality is transmitted to the subsequent monitoring section, and the sampling and detection time at the subsequent monitoring section is T1 = T0 + Hr, where T0 is the time when the sampling and detection is initiated at the influent end, and Hr is the hydraulic residence time of the influent to the subsequent process monitoring section.

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

[0025]

[0026] The remaining detection time Td is calculated as shown in equation (VI):

[0027]

[0028] Where count(t) is the proprietary frequency distribution function of the online detection device, with a range of 0 and 1; to is the initial time, set manually; V s V represents the volume of the reagent consumed in a single test. o The initial volume of the drug is set manually.

[0029] Furthermore, early warning rules are set for the remaining number of drug tests Nd and the remaining testing time Td. Based on the analysis results, early warning information is issued to remind operators to replenish the drug in a timely manner.

[0030] Furthermore, the reagent is stored in a graduated reagent bottle, which allows operators to easily monitor the reagent level and modify the program's operating parameters based on the dosage and shelf life of the reagent. The effective volume of the reagent bottle is 500-5000mL, with graduations accurate to 10-100mL. Operators decide to replenish the reagent based on the currently displayed remaining detection time or number of detections.

[0031] The sampling and detection module consists 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 with a filter screen having a pore size of 10-200 μm. Different pore sizes and materials (polypropylene, polyethylene, carbon steel, etc.) are selected based on the wastewater properties and the sampling cross-section. When the water sample (e.g., a total discharge outlet sample) contains suspended particulate matter (SS), filtration is unnecessary, and direct sampling and detection 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, a reagent warning module is not required.

[0032] This invention also provides a method for operating an online intelligent operation and maintenance system, characterized by the following steps:

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

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

[0035] (3) The sampling and detection module selects standby or running within the corresponding time range based on the received operating condition signal;

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

[0037] (5) The drug early warning module monitors the drug status of the online detection equipment, displays monitoring information or issues early warning information to remind operators to replenish or prepare drugs in a timely manner.

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

[0039] The online detection intelligent operation and maintenance system described in this invention creatively introduces water quality amplitude prediction based on the essential objectives of water quality detection, and applies it to the operation and management of online detection equipment. Combined with decision analysis methods, it reduces the operating frequency of online detection equipment while ensuring stable process control and stable effluent quality, thereby reducing its operating costs.

[0040] The online testing intelligent operation and maintenance system described in this invention has developed an online testing equipment reagent monitoring and early warning system, enabling operation and maintenance personnel to have a real-time and comprehensive grasp of the online equipment reagent status, and to replenish reagents in a timely manner according to early warning signals or actual conditions, so as to avoid the problem of insufficient reagents in the online testing equipment causing the testing process to be interrupted.

[0041] The online detection intelligent operation and maintenance system described in this invention realizes the informatization and intelligentization of the operation of online detection equipment, significantly improves its operation and maintenance efficiency, and reduces the operating cost and labor intensity of online detection equipment. Attached Figure Description

[0042] Figure 1 This is a flowchart of the intelligent operation and maintenance system for line detection according to an embodiment of the present invention. Detailed Implementation

[0043] Unless otherwise defined, the technical terms used in the following embodiments have the same meanings as commonly understood by those skilled in the art. Unless otherwise specified, the experimental reagents used in the following embodiments are conventional biochemical reagents; and the experimental methods described are conventional methods.

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

[0045] Example 1

[0046] The online monitoring system for a municipal wastewater treatment plant includes influent sampling and monitoring and total discharge outlet sampling and monitoring. The total discharge outlet sampling and monitoring (COD, TN, ammonia nitrogen, and TP) is conducted hourly according to environmental regulations, and is performed periodically; therefore, it does not include a sampling decision module, but reagent monitoring modules are still used. The influent sampling and monitoring (COD, TN, ammonia nitrogen, and TP) fully utilizes the intelligent operation and maintenance system for online monitoring equipment of this invention.

[0047] The water quality amplitude prediction module utilizes its influent water quality, influent flow rate, temperature, rainfall, and calendar data from the past three years (corresponding to x in Equation II). i For each indicator (COD, TN, TP and ammonia nitrogen), an influent water quality amplitude prediction model is established to predict the influent water quality amplitude from the current hour to the future HRT (hydraulic retention time of the wastewater treatment system).

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

[0049] Taking the prediction of influent TN amplitude as an example, x i The corresponding historical (last 3 years) data (i = 1, 2, 3, 4, 5, representing historical total nitrogen TN, influent flow rate Q, temperature T, rainfall R, and calendar D respectively) were organized and trained to obtain the 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 inlet to outlet). Substituting the current amplitude prediction value (-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) into equation (Ⅳ), the calculated result is 5.92. The absolute value of the current predicted amplitude -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 of 50μm). The filtered water sample is sent into the sample cup, and the sampling tube of the online detection instrument quantitatively extracts water sample from the sample cup for testing.

[0051] The reagent monitoring module statistically analyzes and calculates the monitoring and operation information (cumulative number of tests, inventory of various reagents) of the influent and effluent online monitoring equipment, and provides the remaining number of tests for each reagent. When the remaining number of tests N is less than 24, a Level 1 warning is issued, reminding operations personnel to replenish reagents in a timely manner; when the remaining number of tests N is less than 12, a Level 2 warning is issued, reminding maintenance personnel to replenish reagents as soon as possible, otherwise there is a risk of operational interruption of the online monitoring equipment.

[0052] As shown in Table 1, by implementing the intelligent operation and maintenance system for online monitoring equipment, compared to the conventional method of testing once per hour (24 times per day), the average frequency of online influent monitoring is reduced by approximately 50% after implementing this invention (blank data areas indicate standby without monitoring). Furthermore, the more stable the water quality indicators, the greater the reduction in monitoring frequency, i.e., the greater the cost savings. Simultaneously, through reagent monitoring and early warning, the reagents for the online monitoring equipment can be replenished in a timely manner, avoiding interruptions in the monitoring process due to insufficient reagents.

[0053] Table 1 Daily Report of Online Water Inlet Monitoring 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An online detection and intelligent operation and maintenance system, characterized in that: It includes an influent amplitude prediction module, a detection decision module, a sampling and detection module, and a reagent monitoring module; The aforementioned influent amplitude prediction module is used to predict the influent water quality amplitude Z; The aforementioned detection decision module determines whether the online detection system is operating based on the real-time influent water quality amplitude. The sampling and testing module is a sampling and testing device; The drug monitoring module monitors the amount of drug used by the online detection device in real time based on the parameters of the drug container and the parameters used, and calculates the remaining number of drug detections Nd and the remaining detection time Td of the online detection device; The influent water quality amplitude Z is the range of water quality change before and after one prediction period; the range of the influent water quality amplitude Z is shown in equation (I): Z=±(D max -D min Formula (Ⅰ) Among them, D max This represents the historical maximum value for this water quality, D min The predicted amplitude of the water quality reading is the historical minimum value. When the predicted amplitude of the water quality reading exceeds the above range, the system will issue an early warning. If the real-time influent water quality amplitude Z0 of the detection decision module satisfies equation (Ⅳ), then the online detection equipment is activated to perform sampling and detection operations; otherwise, sampling and detection are not required. Equation (Ⅳ) is shown below: Where h is the rounded-up value of the hydraulic residence time (HRT) of the treatment system or treatment unit, k is the ordinal number of the influent water quality amplitude, k = 0 is the real-time amplitude prediction value, k < 0 is the historical amplitude prediction value, and k > 0 is the future amplitude prediction value. Set early warning rules for the remaining number of drug tests Nd and the remaining testing time Td. Based on the analysis results, issue early warning information to remind operators to replenish the drug in a timely manner.

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

3. The online detection intelligent operation and maintenance system according to claim 1, characterized in that: The values ​​of the influent water quality amplitude Z are shown in equations (II) and (III): Where x is the input layer neuron, i is the ordinal number of the input layer neuron, with a value of 1-m, y is the calculated result of the hidden neuron, j is the ordinal number of the hidden layer neuron, with 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 calculated hidden layer neuron, b2 is the bias of the calculated output neuron, w1 is the weight coefficient of the calculated hidden layer neuron, and w2 is the weight coefficient of the calculated output neuron.

4. The online detection intelligent operation and maintenance system according to claim 1, characterized in that: The amplitude of the influent water quality is transmitted to the subsequent monitoring section. The sampling and detection time at the subsequent monitoring section is T1 = T0 + Hr, where T0 is the time when the sampling and detection is initiated at the influent end, and Hr is the hydraulic residence time of the influent to the subsequent process monitoring section.

5. The online detection intelligent operation and maintenance system according to claim 1, characterized in that: The remaining number of drug tests Nd is calculated as shown in equation (V): The remaining detection time Td is calculated as shown in equation (VI): Where count(t) is the proprietary frequency distribution function of the online detection device, with a range of 0 and 1; to is the initial time, set manually; V s V represents the volume of the reagent consumed in a single test. o The initial volume of the drug is set manually.

6. The online detection intelligent operation and maintenance system according to claim 1, characterized in that: The reagent is stored in graduated reagent bottles, allowing operators to easily monitor the reagent level and adjust program parameters based on dosage and shelf life. The effective volume of the reagent bottle is 500-5000mL, with graduations accurate to 10-100mL. Operators decide to replenish the reagent based on the currently displayed remaining detection time or number of detections.

7. The operation method of the online detection intelligent operation and maintenance system according to any one of claims 1-6, characterized in that: Includes the following steps: (1) The influent amplitude prediction module is used to predict the influent water quality amplitude in real time and sends the prediction results to the detection decision module; (2) The detection decision module determines whether to perform a detection operation based on 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 running within the corresponding time range based on the received operating condition signal; (4) During operation, the detection instrument performs detection operations on the water sample collected by the sampling device to obtain measurement results; (5) The drug early warning module monitors the drug status of the online detection equipment, displays monitoring information or issues early warning information to remind operators to replenish or prepare drugs in a timely manner.

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