CN116263850A - Online sewage water quality early warning method combining offline simulation data - Google Patents
Online sewage water quality early warning method combining offline simulation data Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent water management service, in particular to an online sewage water quality early warning method combining offline simulation data, which comprises the steps of acquiring water quality information of a designated sewage plant and operation attributes of water treatment equipment; writing the acquired water quality information data and the operation attribute into an ASM model to generate an offline model, and executing calibration on the offline model; sequentially simulating various scenes by using an offline model to obtain simulation conclusion information; writing the offline model into an intelligent water service platform to generate an online model; performing simulation debugging on the online model, and optimizing the online model into an online model with a prediction function; the method comprises the steps of writing simulation conclusion information of an offline model into a data platform, triggering comprehensive sewage early warning after synchronously calling the online model for calculation, and forming an online model by combining offline simulation data with online real-time monitoring information to realize accurate early warning of sewage quality.
Description
Technical Field
The invention relates to the technical field of intelligent water management service, in particular to an online sewage water quality early warning method combining offline simulation data.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
For the sewage biochemical treatment process, the popularization and application of a commercialized mechanism model begin to appear in Canada university at the beginning of 90 s, but the application of the mechanism model is mainly in an off-line modeling analysis mode, and the reason is that the requirement of moving the model to 'on-line' operation is not high in the whole foreign. However, due to the lack of corresponding abundant professional training of related operation technicians in domestic sewage treatment plants and the lack of professional modeling talents in the industry, commercial modeling tools have no good application in markets, but along with the development of intelligent water service, the development of informatization and data platform construction of the sewage treatment plants is greatly promoted in China, the models are embedded into a digital system platform to form an online model, and the technicians and managers of the sewage plants are assisted to better guide the actual operation of the sewage plants according to the quantitative analysis of the models, but the current attempt of finding the online model by related technicians in actual operation cannot guarantee the accuracy of online prediction of the model.
Based on this, it is highly desirable to provide a method for on-line prediction or early warning of sewage quality that can be accurately predicted.
Disclosure of Invention
The inventors found through research that: at present, the domestic sewage quality is researched and analyzed mostly by means of actual past experience, even when some operations of forecasting and early warning are performed, the operations are simply performed by means of artificial experience, thus the risk of inaccurate forecasting is potentially achieved, the experiences, the level and the like of different technicians are differentiated, and meanwhile, foreign related technical ideas are clear, so that if the foreign treatment ideas are effectively combined, the method is synchronously suitable for the domestic sewage current situation, and the method for accurately early warning the sewage quality dynamic information can be provided certainly.
The invention aims to provide an online sewage quality early warning method combined with offline simulation data, so as to solve the technical problem that the prior art cannot provide an online sewage quality accuracy early warning method.
According to one aspect of the disclosure, an online wastewater quality early warning method in combination with offline simulation data includes the steps of:
step 1, acquiring water quality information of a designated sewage plant and operation attributes of water treatment equipment;
step 2, writing the acquired water quality information data and the operation attribute into an ASM model to generate an offline model, and executing calibration on the offline model;
step 3, sequentially simulating various scenes by using an offline model to obtain simulation conclusion information, wherein the simulation conclusion information obtained by the various scenes at least comprises the water quality conditions of effluent of the sewage treatment plant under the following conditions: the average water inflow and the average pollution load, different sludge and mixed liquor backflow, different sludge residence time, different dissolved oxygen set values, peak water amount, heavy rain condition, peak load, low temperature condition, equipment maintenance and partial corridor shutdown overhaul one or more conditions, so that the influence of the factors on the water quality of the effluent in a sewage treatment plant is quantified;
step 4, writing the offline model into an intelligent water service platform to generate an online model, and then performing simulation debugging on the online model to optimize the online model into an online model with a prediction function;
step 5, writing the simulation conclusion information of the offline model into a data platform, and triggering comprehensive sewage early warning after synchronously calling the online model for calculation;
and 6, executing at least one step 1 to step 5.
The method obtained by the inventor after long-term actual work combines an offline model with an online water service platform, so that the monitoring of the dynamic sewage quality is realized, and the blank of an online and offline combined early warning technology in the domestic sewage early warning technology field is filled.
In some embodiments of the disclosure, the step 1 specifically includes: the water quality information at least comprises the chemical oxygen demand concentration, the ammonia nitrogen concentration, the total phosphorus concentration and the suspended particle solid concentration of the inlet water and the outlet water; the operation attribute of the water treatment equipment at least comprises equipment limit operation power and equipment operation limit rotating speed.
In some embodiments of the present disclosure, the water quality information and the operational attribute acquisition pathway of the water treatment apparatus include at least process drawings, experimental analysis, field investigation, historical data, and data results of supplemental experiments.
In some embodiments of the disclosure, the step 2 specifically includes: the ASM model comprises a public ASM model published by international water collaboration or a purchased business model; the off-line model calibration includes the steps of: firstly, determining the quality characteristics of inflow water, then, determining kinetic parameters, and finally, determining the properties of sludge, wherein when the offline model uses historical input data, the calculated result is consistent with the historical outflow water data.
In some embodiments of the disclosure, the step 4 specifically includes: the real-time online monitoring data obtained by automatic collection in the intelligent water platform is arranged, and the arranged data is written into a database for calling of an online model; meanwhile, manually uploading manual record data and laboratory test analysis data to an intelligent water platform by a technician to classify and sort, and integrating the manual record data and the laboratory test analysis data into an online model of the platform according to water inlet data and operation data to generate an online model with a prediction function for process operation state and water outlet quality, wherein the online model is calculated according to platform data updating frequency;
wherein the collated data includes at least an average value over an hour after the unreasonable data is removed.
In some embodiments of the present disclosure, the data sorting process combines the analog data conclusion with the state parameter monitored in real time or at regular time, and compares the differences between the analysis values, so as to obtain the compared conclusion data, which is the sorted data.
In some embodiments of the disclosure, the status parameters include at least a biochemical reactor status parameter mixed liquor volatile suspended solids concentration, an oxygen concentration dissolved in water, a secondary sedimentation tank status parameter surface overflow rate, a solids loading rate, and an underflow sludge concentration.
In some embodiments of the disclosure, the step 5 specifically includes writing one or more of maximum water volume and load that can be processed by the sewage treatment plant in the simulation conclusion information, limiting factors under different situations, critical factors that cause out-of-standard in different situations, core control elements of the sewage treatment plant, and possible countermeasures when the sewage treatment plant encounters different extreme situations into an online model, after synchronously calling the online model to calculate, the online model comprehensively determines out-of-standard risk according to the degree of the rise of the effluent water quality index, the occurrence time of the index rise, and comparison conclusion data of the state parameters, and when the value reaches the early warning threshold, triggering sewage early warning.
In some embodiments of the present disclosure, the types of early warning for the online model include model effectiveness early warning, model predictive early warning, high risk scenario early warning, and operational monitoring early warning.
Compared with the technology disclosed at present, the technology disclosed by the disclosure has the following advantages and beneficial effects: according to the method, the online model is formed by combining the offline simulation data with the online real-time monitoring information, so that the sewage quality condition is accurately predicted, meanwhile, the uncertainty of information of early warning conclusion depending on manual experience can be solved, and the accuracy of domestic sewage quality information early warning is substantially improved.
Drawings
Fig. 1 is a schematic flow chart of the early warning method of the present invention.
The arrows in fig. 1 of the present invention only represent the interconnections between the process steps of the method, and do not specifically refer to the processing logic of the program flow.
Detailed Description
Referring to fig. 1, the present embodiment provides an online sewage water quality early warning method combined with offline simulation data, which is already in a practical use stage, and the inventor has performed an actual sewage water quality early warning test of a sewage plant according to related public data and the method of the present disclosure, and has obtained a better effect.
The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which illustrative embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It will be further understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and "comprising," when used herein, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof.
Unless defined to the contrary, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
It should also be noted that in some alternative implementations, the functions/acts noted in the flowcharts may occur out of the order noted in the flowcharts. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Finally, the functionality of one or more of the blocks may be combined with the functionality of other blocks, alone or in combination.
For a better understanding of the present disclosure, the relevant content of the present disclosure will be described first: the models referred to in this disclosure are all models disclosed in the prior art, including but not limited to published ASM models published by international water communities or commercial models obtained by the inventor through purchase, and this disclosure provides a specific treatment method for wastewater quality treatment only.
Examples
The present example includes at least the following: the online sewage water quality early warning method combining the offline simulation data comprises the following steps of obtaining the water quality information of a designated sewage plant and the operation attribute of water treatment equipment.
Firstly, the acquisition way of acquiring the water quality information of the appointed sewage plant at least comprises a process drawing, experimental analysis, field investigation, historical data, a supplementary experiment plan and the like, wherein the appointed sewage plant at least comprises a large, medium and small municipal sewage plant which needs to carry out early warning of the water quality of sewage, and the embodiment is preferable for sewage treatment of the municipal sewage plant because the pollutants in the sewage of the municipal sewage plant mainly come from resident life, namely various saccharides, oil and fat, protein organic matters and various inorganic components, and the special matters are less; meanwhile, the water quality information collection content of the municipal sewage plant is relatively standard, the water quality information at least comprises the chemical oxygen demand concentration, the ammonia nitrogen concentration, the total phosphorus concentration, suspended solid particles and the like of the inlet water and the outlet water, and the inventor can select relevant pollutant measurement indexes according to the needs in actual operation, so that the method is not limited;
then, after knowing the sewage quality information, it is necessary to know and determine the operation attribute of the water treatment device, where the operation attribute of the sewage treatment plant at least includes the device limit operation power such as a pump and a blower, and the device operation limit rotation speed, so that the device operation attribute needs to be obtained, because in the sewage treatment field, the hardware factor of the device also has a substantial influence on the final result of the water treatment, and therefore, related consideration must be performed.
Further, the inventor needs to specify that the ASM model includes an published ASM model published by international water collaboration or a purchased business model, AND the selectable types of the ASM model may refer to articles named ACTIVATED SLUDGE MODELS ASM, ASM2d AND ASM3 published by IWA in its scientific AND technical report series, wherein the mentioned models can be used as long as the mentioned effects of the embodiment can be achieved or related problems can be solved, AND the embodiment is not particularly limited; the business model is a process model in the art that can be purchased through public channels.
After the offline model is obtained, a calibration operation is carried out on the offline model, and the calibration operation process is to sequentially finish the determination of the water quality characteristics, the dynamic parameters and the sludge characteristics of the inlet water. Wherein the characteristics of the water quality, the kinetic parameters and the sludge characteristics of the inlet water are as follows: the water quality characteristics of the inlet water comprise the proportion of organic matters capable of being rapidly biochemically degraded, organic matters capable of being biochemically degraded slowly, particulate organic matters not capable of being biochemically degraded, soluble organic matters not capable of being biochemically degraded in the inlet water, the proportion of ammonia nitrogen in the inlet water and the proportion of soluble orthophosphate in the inlet water; the kinetic parameters comprise related parameters of main biochemical reactions in the biochemical treatment process, such as the maximum growth specific rate, the concentration of half-saturated dissolved oxygen, the half-saturation coefficient of a substrate and the like of microorganisms such as nitrifying bacteria (ammonia nitrogen oxidizing bacteria and nitrite oxidizing bacteria), common heterotrophic bacteria and the like; sludge characteristics refer to parameters related to the sedimentation characteristics thereof, such as maximum sedimentation rate, maximum compressible concentration, a Vesline model obstructive sedimentation parameter describing the change of sedimentation rate with sludge concentration, and the like.
Still further, the off-line model after calibration is utilized to sequentially execute the operation of simulating multiple scenes to obtain simulation conclusion information, wherein the simulation conclusion information obtained by the multiple scenes at least comprises the water quality of effluent of the sewage treatment plant under the following conditions: in this embodiment, in order to obtain comprehensive data information, all the information data are preferable;
in the dynamic simulation of the offline model, the fitting degree of simulation data such as the volatile suspended solid concentration (MLSS), the Chemical Oxygen Demand (COD), the total nitrogen content (TN), the ammonia nitrogen (NH 4-N), the solid (SS) after sludge dewatering and drying and the like and the actually obtained water quality data are required to be more than 80%, and the trend of the actual water quality data is consistent with the change of the actual water quality data over time, the trend of the actual water quality data is that the actual water quality data is rising, the model prediction is also rising, but the rising degree is possibly different.
Furthermore, when the calculated result of the calibrated offline model after using the historical input data is consistent with the change trend of the historical effluent data, that is, after the model is calibrated, the model is integrated and written into the intelligent water service platform in a coding manner, so as to form an online model, after the online model is formed, the online model is simulated and debugged, and is optimized into an online model with a prediction function, wherein the writing method is a method which is disclosed in the prior art and can be used for data writing processing, and the method is not limited in the embodiment; meanwhile, the process specifically comprises the following steps: firstly, real-time online monitoring data automatically collected and obtained in an intelligent water platform are arranged, the arranged data are written into a database for line model call, then, manual recording data and laboratory test analysis data which are manually uploaded to the intelligent water platform by technicians are classified and arranged, the manual recording data and the laboratory test analysis data are integrated into an online model of the platform according to water inlet data and operation data, and an online model which is calculated according to platform data update frequency and has a prediction function on process operation state and water outlet quality is generated;
the data are arranged at least comprising average values in hours after unreasonable data are removed; the data finishing process combines the simulation data conclusion with the state parameters monitored in real time or at regular time, compares the differences among the analysis values, and further obtains conclusion data after comparison, wherein the conclusion data is the finished data, and further needs to be explained that the state parameters in the embodiment at least comprise the state parameters of the biochemical reactor, the volatile suspended solid concentration of the mixed solution, the oxygen concentration dissolved in water, the surface overflow rate of the secondary sedimentation tank state parameters, the solid load rate and the underflow sludge concentration.
And further, after optimizing the online model, the obtained data such as the water quality analysis of the target sewage plant in the model and the operation limit of the water treatment equipment are combined with the offline model calibrated by the utilization step to simulate the average inflow water quantity and pollution load, the reflux condition, the sludge residence time, the dissolved oxygen set value, the peak water quantity, the heavy rain condition, the peak load, the low temperature condition, the equipment maintenance and the water inflow condition and the operation scheme in the partial corridor shutdown maintenance one by one, the simulated offline model conclusion information is written into the optimized online model, after the online model calculation is synchronously executed, the online model comprehensively judges the out-of-standard risk according to the degree of the rising of the effluent water quality index, the occurrence time of the index rising and the comparison conclusion data of the state parameters, and when the value reaches the early warning threshold value, the sewage early warning is triggered.
It should be noted that, the type of partial sewage early warning in this embodiment specifically includes:
the model effectiveness early warning is to judge whether the online model needs to perform manual professional maintenance by judging whether the online model is consistent with the state parameters of the sewage treatment plant obtained by calculating the input data from the intelligent water service platform, the water quality of the effluent and the state parameters obtained by actual monitoring, and the water quality of the effluent, including the characteristic adjustment of the water quality of the influent, the dynamic parameter adjustment and the sludge state parameter adjustment. The water quality characteristics of the inlet water comprise the proportion of quick biochemical degradation organic matters, slow biochemical degradation organic matters, granular non-biochemical degradation organic matters, soluble non-biochemical degradation organic matters and soluble orthophosphate in the inlet water, wherein the proportion of ammonia nitrogen and the proportion of total phosphorus in the inlet water; the kinetic parameters comprise related parameters of main biochemical reactions in the biochemical treatment process, such as the maximum growth specific rate, the concentration of half-saturated dissolved oxygen, the half-saturation coefficient of a substrate and the like of microorganisms such as nitrifying bacteria (ammonia nitrogen oxidizing bacteria and nitrite oxidizing bacteria), common heterotrophic bacteria and the like; sludge characteristics refer to parameters related to the sedimentation characteristics thereof, such as maximum sedimentation rate, maximum compressible concentration, a Vesline model obstructive sedimentation parameter describing the change of sedimentation rate along with the sludge concentration, and the like; the model prediction early warning is to use real-time data, because after an offline model is moved to an online platform, two types of simulation calculation are needed, the steady state calculation uses manual measurement data (chemical detection and inspection data) which are uploaded to the platform every day, the dynamic calculation takes the result of the steady state model as a dynamic calculation initial value, the dynamic calculation is carried out every 2 hours according to the data which are monitored in real time, the effluent quality after 2 hours is predicted, whether the exceeding standard risk exists after 2 hours is predicted and judged according to the calculated effluent quality, wherein the triggering early warning process is that the index which is presented according to the calculation result of the online model is increased to exceed the effluent standard;
the high risk scene early warning is combined with the evaluation result of the off-line model, and is used for monitoring data of the quality, the quantity and the temperature of the water to be fed, and the like, and the data are found to exceed the range of guaranteeing the healthy operation of the biochemical treatment process in the off-line evaluation, so that the alarm is given. While this alarm is not necessarily a certain instantaneous value, such as temperature, an excessive temperature may not greatly affect the system, but for 1-3 consecutive days, a greater risk may be created;
the operation monitoring and early warning is divided into two types, namely a process variable and effluent quality, and is characterized in that the operation monitoring and early warning directly reads monitored real-time data and compares the monitored real-time data with a threshold value, and the operation monitoring and early warning specifically comprises the following steps:
1. process variable: during the process, some monitoring is carried out to remind operators of possible risks, such as dissolved oxygen DO, if the concentration is lower than 1.0mg/L, incomplete nitrification reaction and the like can be caused, and the risks can be classified into more traditional threshold early warning categories, including dissolved oxygen DO, oxidation-reduction electrode potential ORP, pH and the like;
2. effluent quality: this part is also a threshold warning; however, our early warning logic will be more comprehensive, and the triggering early warning process is the degree of index increase and the duration of the index increase. For example, other sewage plants can usually give an early warning when the concentration of the effluent exceeds the national standard. One more water quality concentration is set, for example, when the monitored water quality concentration reaches 80% of the concentration in the national effluent standard, an alarm is given, but if the water quality concentration is only reached instantaneously, the maintenance time is not long, and the risk is not high; however, if the national effluent standard concentration of 80% is reached for 1-3 days continuously, there is a risk of the effluent exceeding the standard.
And finally, executing at least one time according to the actual sewage treatment requirement and the actual environment condition.
Based on the above, the comprehensive sewage water quality early warning operation is completed by combining the traditional early warning mode, utilizing the model calculation result to predict early warning and utilizing the off-line evaluation result to set early warning.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (9)
1. The online sewage water quality early warning method combining the offline simulation data is characterized by comprising the following steps of:
step 1, acquiring water quality information of a designated sewage plant and operation attributes of water treatment equipment;
step 2, writing the acquired water quality information data and the operation attribute into an ASM model to generate an offline model, and executing calibration on the offline model;
step 3, sequentially simulating various scenes by using an offline model to obtain simulation conclusion information, wherein the simulation conclusion information obtained by the various scenes at least comprises the water quality conditions of effluent of the sewage treatment plant under the following conditions: the average water inflow and the average pollution load, different sludge and mixed liquor backflow, different sludge residence time, different dissolved oxygen set values, peak water amount, heavy rain condition, peak load, low temperature condition, equipment maintenance and partial corridor shutdown overhaul one or more conditions, so that the influence of the factors on the water quality of the effluent in a sewage treatment plant is quantified;
step 4, writing the offline model into an intelligent water service platform to generate an online model, and then performing simulation debugging on the online model to optimize the online model into an online model with a prediction function;
step 5, writing the simulation conclusion information of the offline model into a data platform, and triggering comprehensive sewage early warning after synchronously calling the online model for calculation;
and 6, executing at least one step 1 to step 5.
2. The early warning method according to claim 1, wherein the step 1 specifically includes: the water quality information at least comprises the chemical oxygen demand concentration, the ammonia nitrogen concentration, the total phosphorus concentration and the suspended particle solid concentration of the inlet water and the outlet water; the operation attribute of the water treatment equipment at least comprises equipment limit operation power and equipment operation limit rotating speed.
3. The method of claim 2, wherein the water quality information and the operation attribute of the water treatment device are obtained by a process drawing, an experimental analysis, an on-site investigation, historical data and a data result of a supplementary experiment.
4. The method according to claim 1, wherein the step 2 specifically includes: the ASM model comprises a public ASM model published by international water collaboration or a purchased business model; the off-line model calibration includes the steps of: firstly, determining the quality characteristics of inflow water, then, determining kinetic parameters, and finally, determining the properties of sludge, wherein when the offline model uses historical input data, the calculated result is consistent with the historical outflow water data.
5. The method according to claim 1, wherein the step 4 specifically includes: the real-time online monitoring data obtained by automatic collection in the intelligent water platform is arranged, and the arranged data is written into a database for calling of an online model; meanwhile, manually uploading manual record data and laboratory test analysis data to an intelligent water platform by a technician to classify and sort, and integrating the manual record data and the laboratory test analysis data into an online model of the platform according to water inlet data and operation data to generate an online model with a prediction function for process operation state and water outlet quality, wherein the online model is calculated according to platform data updating frequency;
wherein the collated data includes at least an average value over an hour after the unreasonable data is removed.
6. The method according to claim 5, wherein the data sorting process is to combine a conclusion of the analog data with a state parameter monitored in real time or periodically, compare and analyze differences between the values, and further obtain conclusion data after comparison, the conclusion data being sorted data.
7. The method according to claim 6, wherein the status parameters include at least a concentration of volatile suspended solids in the mixed liquid, a concentration of oxygen dissolved in water, a surface overflow rate of the secondary sedimentation tank status parameters, a solid load rate, and an underflow sludge concentration.
8. The early warning method according to claim 6, wherein the step 5 specifically includes writing one or more of maximum water volume and load that can be processed by the sewage treatment plant in the simulation conclusion information, limiting factors under different situations, critical factors that cause out-of-standard under different situations, core control elements of the sewage treatment plant, and possible countermeasures when the sewage treatment plant encounters different extreme situations into the online model, and after synchronously invoking the calculation of the online model, comprehensively judging out-of-standard risk by the online model according to the degree of out-water quality index rising, the occurrence time of index rising, and comparison conclusion data of state parameters, and triggering sewage early warning when the numerical value reaches an early warning threshold.
9. The method of claim 8, wherein the types of online model warnings include model effectiveness warnings, model predictive warnings, high risk scenario warnings, and operational monitoring warnings.
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