CN102156406B - Expert system and method for preventing and controlling sludge bulking under the diagnosis based on operation condition of sewage disposal plant - Google Patents
Expert system and method for preventing and controlling sludge bulking under the diagnosis based on operation condition of sewage disposal plant Download PDFInfo
- Publication number
- CN102156406B CN102156406B CN2011100477905A CN201110047790A CN102156406B CN 102156406 B CN102156406 B CN 102156406B CN 2011100477905 A CN2011100477905 A CN 2011100477905A CN 201110047790 A CN201110047790 A CN 201110047790A CN 102156406 B CN102156406 B CN 102156406B
- Authority
- CN
- China
- Prior art keywords
- sludge
- sludge bulking
- bulking
- diagnosis
- prevention
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Activated Sludge Processes (AREA)
Abstract
The invention relates to an expert system and method for preventing and controlling sludge bulking under the diagnosis based on operation condition of a sewage disposal plant, belonging to the crossing fields of an environmental engineering technology, a computer software technology and an automatic control technology, and overcoming the defects that the diagnosis speed is slow and man-made erroneous judgment is easy to make because the sludge bulking is analyzed, diagnosed and controlled manually only in the traditional sewage disposal technology. A knowledge base module is used for storing diagnosis methods and control methods, wherein rules are mainly established based on process running parameters and sludge sedimentation performances, and a man-machine interaction unit can be used for timely updating the methods stored in the knowledge base module and adding successful and unsuccessful cases during diagnosis and control. A database module is used for storing original data, offline detection data and secondary data acquired by an online monitoring instrument. An inference engine module is used for carrying out forward reasoning. An explanation engine module is used for explaining. A man-machine interaction unit is used for finishing information communication between users and the system.
Description
Technical field
The invention belongs to the crossing domain of environment project, computer software technology and automatic control technology; Relate to sewage disposal system and intelligence control system; Especially the expert system of exploitation is carried out fault diagnosis based on operating condition and sludge settling property etc. for indicative character, can be applied to the prevention and the control of sludge bulking in the biological wastewater treatment process.
Background technology
Activated sludge process is the present both at home and abroad extensive biologic process for treating sewage of employing in municipal effluent and the Industrial Wastewater Treatment, and it mainly comprises two stages, promptly the biological chemistry of pollutant the processing stage and the physical stage of mud-water separation.Contained pollutants such as organism, nitrogen and phosphorus get in the biological reaction pool and will be removed through biological chemistry action by active sludge in the sewage.Microorganism in the active sludge mainly comprises various bacteriums, and primary and metazoa, and bacterium mainly is divided into zoogloea bacterium and der Pilz according to differentiating forms.Secondly, the good mud-water separation performance and the compression performance of active sludge are assurance effluent quality key points up to standard in the second pond.But sludge bulking problem (being often referred to by hyphomycetic excess growth caused) of often running in service that is activated sludge process is stubborn problem very; Filamentous Bulking can cause the active sludge subsidence rate slack-off; The compression performance variation; And then cause that effluent quality descends, the adverse consequences of fluctuation of service, and can make the collapse of entire sewage disposal system when serious, be considered to " cancer " of activated sludge process.
Lot of domestic and international experts and scholars have carried out number of research projects to the sludge bulking problem, focus mostly at the aspects such as the origin cause of formation, mechanism, mathematical model and control method of sludge bulking.But because sludge bulking the complex nature of the problem does not still have unified theoretic knowledge and lasting effectively control method at present.In production practices, generally adopt two kinds of methods to control sludge bulking, a kind of is through adding the sanitizer that some have strong oxidizing property to aeration tank or returned sluge pipeline, such as hypochlorous acid, H
2O
2, ozone waits and suppresses or the deactivation der Pilz; Another kind method mainly is the situation of change of influent quality before and after the investigation sludge bulking, operational factor; Find out the concrete origin cause of formation of bringing out the excessive breeding of der Pilz; Provide the condition of suitable zoogloea bacterium dominant growth to control sludge bulking through changing operational factor; The method that generally adopts has biological selector, as head end is provided with aerobic, anoxic or anaerobic organism selector switch in the aeration tank.Although having dropped into a large amount of research fundings studies the sludge bulking phenomenon both at home and abroad, the sludge bulking problem still generally takes place at present.According to investigations, China has the sewage treatment plant more than 60% all can meet with the sludge bulking phenomenon every year.
Summary of the invention
The purpose of this invention is to provide a kind of prevention and control method based on sewage treatment plant's operating condition diagnosis sludge bulking expert system; Can only carry out analyzing and diagnosing and control to solve in the existing sewage disposal technology by the people to the problem of sludge bulking; Diagnosis speed is slow, also occurs the defective of artificial origin's erroneous judgement easily.
Sludge bulking prevention based on sewage treatment plant's operating condition diagnosis comprises base module, DBM, inference machine module, explanation engine module and man-machine interaction unit with the control expert system;
Base module; Be used to store based on sewage treatment plant's operational factor and diagnose the method for sludge bulking and the method for control sludge bulking; Rule sets up the variation of emphasis based on technology operational factor and sludge settling property in the base module; And can be, and add successful case and the unsuccessful case in diagnosis and the control procedure through the man-machine interaction unit method in the base module that upgrades in time;
DBM is used for storing raw data, the data of offline inspection and the secondary data that diagnostic procedure produces that online measuring instrument obtains;
The inference machine module is used for the reasoning process that programming is formulated by CLIPS and utilizes the method for base module to carry out forward reasoning;
The explanation engine module is used for the The reasoning results that the inference machine module provides is made an explanation, and makes the user understand reasoning process and knowledge of being used and data;
Man-machine interaction unit is accomplished the information interchange between user and the expert system, and is following based on the step of the prevention of sewage treatment plant's operating condition diagnosis sludge bulking expert system and control method:
Step 1: the required data of man-machine interaction unit input diagnosis sludge bulking, data comprise the raw data that the on-line monitoring instrument obtains, the data and the technology operational factor of offline inspection;
Step 2: integral data in DBM writes DBM to the technology operational factor in the step 1 and the data of sludge settling property, and obtains secondary data in internal calculation;
Step 3: the secondary data that data that the diagnosis sludge bulking that obtains step 1 is required and step 2 obtain imports the inference machine module;
Step 4: whether the inference machine module through the base module diagnosis sludge bulking takes place; Be that then execution in step five; Not, then execution in step seven;
Step 5: sludge bulking takes place, and inference machine module then diagnosis causes the concrete reason of sludge bulking generation, and provides to the solution that produces sludge bulking under this situation;
Step 6: select feasible solution according to actual conditions; And follow the tracks of the situation of change take sludge settling property after this solution, if sludge settling can recover, sludge bulking is resolved; Preserve this successful case to base module, log off; If sludge settling property does not recover, sludge bulking can not get solving, and preserves unsuccessful case to base module, continues the situation of change of collection technology operational factor, returns step 5, until completing steps six;
Step 7: sludge bulking does not take place, and whether diagnosis exists the possibility that sludge bulking takes place; Be that then execution in step eight; Not, then execution in step ten;
Step 8: have the possibility that the sludge bulking phenomenon occurs, the diagnosis of inference machine module possibly cause taking place the reason of sludge bulking in ensuing operational process, and the prevention method of this possibility of prevention is provided;
Step 9: the method for selecting feasible prevention sludge bulking according to actual conditions; And follow the tracks of the situation of change take sludge settling property after this measure, if sludge settling property is improved, sludge bulking obtains prevention; Preserve this successful case to base module, log off; If sludge settling property does not improve, prevention method can not solve contingent sludge bulking, preserves unsuccessful case to base module, continues the situation of change of collection technology operational factor, returns step 8, until completing steps nine;
Step 10: do not have the possibility that sludge bulking takes place, then diagnose settling basin sludge settling effect, if the settling basin sedimentation effect is poor, whether unimpeded whether inspection sludge return pipe or second pond receive hydraulic load is impacted, and gives solution; If sludge settling is effective, then the sludge bulking problem does not take place in illustrative system, logs off.
The invention provides based on sewage treatment plant's operating condition and sludge settling property is diagnosis, the prevention and intelligence control system and the method controlled that indicative character realizes sludge bulking.Various operating index in the activated sludge process of this invention with Treating Municipal Sewage and industrial waste water (comprising Inlet and outlet water index and operational parameter control) and sludge settling property (comprising sludge settling index SVI, sludge blanket height, der Pilz index) be changed to foundation; Set up prevention and the regulation and control rule of controlling sludge bulking in the conventional sewage treatment process; Make up prevention and the expert system of controlling sludge bulking; Internal reasoning through the in-house experts system; Provide the sludge bulking that can supply operations staff's reference to bring out the origin cause of formation and effective solution, realize prediction and expert diagnosis, reliable solution is provided or helps it to accomplish the auto-control function to the operations staff to Filamentous Bulking; The final efficient stable operation that realizes sewage treatment plant, and improve the automatic control level of existing sewage treatment plant.
The invention has the advantages that: the main foundation that is changed to that the invention reside in operating condition and sludge settling property through sewage treatment process; Judge the reason of the generation and the generation thereof of sludge bulking; And provide effectively and solution targetedly; And through the management to history library, increase accident sample, the prevention and the warning function of realization sludge bulking.Having solved in the existing sewage disposal technology problem to sludge bulking can only carry out analyzing and diagnosing and control by the people, and diagnosis speed is slow, also occurs the defective of artificial origin's erroneous judgement easily.
Description of drawings
Fig. 1 is a method flow diagram of the present invention, and Fig. 2 is common sludge bulking fault diagnosis decision tree, and Fig. 3 is a sludge settling index SVI curve map over time in the case study on implementation in the embodiment five.
Embodiment
Embodiment one: combine Fig. 1 and Fig. 2 that this embodiment is described, this embodiment comprises base module 1, DBM 2, inference machine module 3, explanation engine module 4 and man-machine interaction unit 5;
Base module 1; Be used to store based on sewage treatment plant's operational factor and diagnose the method for sludge bulking and the method for control sludge bulking; Rule sets up the variation of emphasis based on technology operational factor and sludge settling property in the base module 1; And can be, and add successful case and the unsuccessful case in diagnosis and the control procedure through man-machine interaction unit 5 method in the base module 1 that upgrades in time;
DBM 2 is used for storing raw data, the data of offline inspection and the secondary data that diagnostic procedure produces that online measuring instrument obtains;
Inference machine module 3 is used for the reasoning process that programming is formulated by CLIPS and utilizes the method for base module 1 to carry out forward reasoning;
Explanation engine module 4 is used for the The reasoning results that inference machine module 3 provides is made an explanation, and makes the user understand reasoning process and knowledge of being used and data;
Man-machine interaction unit 5 adopts VC++ visual programming language development, accomplishes the information interchange between user and the expert system.
Step 1: the required data of man-machine interaction unit 5 input diagnosis sludge bulkings, data comprise the raw data that the on-line monitoring instrument obtains, the data and the technology operational factor of offline inspection;
The raw data that the on-line monitoring instrument is obtained comprises aerobic zone DO concentration, flow of inlet water, pH value, temperature and second pond sludge blanket; The data of offline inspection comprise influent quality index, effluent quality index, MLSS value, SVI value and der Pilz index; The technology operational factor comprises return sludge ratio, sludge volume and sludge age;
Step 2: integral data in DBM 2 writes DBM 2 to the data of technology operational factor in the step 1 and sludge settling property, and obtains secondary data in internal calculation;
Sludge settling property comprises sludge settling index SVI, sludge blanket height, der Pilz index; Secondary data comprises COD-sludge loading, COD/N/P ratio, aeration tank hydraulic detention time, second pond hydraulic detention time and hydraulic load;
Step 3: the data importing inference machine module 3 that obtains step 1 and step 2;
Step 4: inference machine module 3 through the method for diagnosing sludge bulking based on sewage treatment plant's operational factor in the base module 1 and the method for control sludge bulking and the method for updating that constantly obtains from man-machine interaction unit 5 with add the case diagnosis whether sludge bulking take place; Be that then execution in step five; Not, then execution in step seven;
Step 5: sludge bulking takes place, and inference machine module 3 then diagnosis causes the concrete reason of sludge bulking generation, and provides to the solution that produces sludge bulking under this situation;
Step 6: select feasible solution according to actual conditions; And follow the tracks of the situation of change take sludge settling property after this solution, if sludge settling can recover, sludge bulking is resolved; Preserve this successful case to base module 1, log off; If sludge settling property does not recover, sludge bulking can not get solving, and preserves unsuccessful case to base module 1, continues the situation of change of collection technology operational factor, returns step 5, until completing steps six;
Step 7: sludge bulking does not take place, and whether diagnosis exists the possibility that sludge bulking takes place; Be that then execution in step eight; Not, then execution in step ten;
Step 8: have the possibility that the sludge bulking phenomenon occurs, 3 diagnosis of inference machine module possibly cause taking place the reason of sludge bulking in ensuing operational process, and the prevention method of this possibility of prevention is provided;
Step 9: the method for selecting feasible prevention sludge bulking according to actual conditions; And follow the tracks of the situation of change take sludge settling property after this measure, if sludge settling property is improved, sludge bulking obtains prevention; Preserve this successful case to base module 1, log off; If sludge settling property does not improve, prevention method can not solve contingent sludge bulking, preserves unsuccessful case to base module 1, continues the situation of change of collection technology operational factor, returns step 8, until completing steps nine;
Step 10: do not have the possibility that sludge bulking takes place, then analyze settling basin sludge settling effect, if the settling basin sedimentation effect is poor, whether unimpeded whether inspection sludge return pipe or second pond receive hydraulic load is impacted, and gives solution; If sludge settling is effective, then the sludge bulking problem does not take place in illustrative system, logs off.
After the result releases; Expert system can be extracted relevant information also and output of The reasoning results through explanation engine module 4 from base module 1; Be presented on the man-machine interaction unit 5; Interior in the self study correction base module 1 perhaps preserved the case daily record, and the The reasoning results that provides through 4 pairs of inference machine modules 3 of explanation engine module simultaneously makes an explanation, and makes the user understand reasoning process and knowledge of being used and data.
Embodiment two: this embodiment and embodiment one difference be storage in the said base module 1 diagnose the method for sludge bulking to comprise according to sludge settling index SVI, der Pilz index, sludge blanket height, water outlet concentration of suspension and the sludge loss phenomenon whether occurs based on sewage treatment plant's operational factor to judge whether to take place the sludge bulking method; Diagnose the condition whether sludge bulking takes place to be in the step 4: find to exist under the hyphomycetic condition at microscopy, when one or more symptom that following symptom takes place took place simultaneously, then sludge bulking took place; Described symptom comprises: a, sludge settling index SVI are higher than 250mL/g; B, observe to find the der Pilz index when microscopy and be higher than 3; C, sludge blanket height are near the warning position; Obvious sludge loss phenomenon appears in d, second pond; Otherwise sludge bulking does not take place.Other composition is identical with embodiment one with connected mode.
Embodiment three: this embodiment and embodiment one or two differences are in the step 5 when generation sludge bulking; According to the technology operational factor in the sewage disposal system: dissolved oxygen concentration, sludge loading scope, influent quality index, effluent quality index, water temperature, return sludge ratio, sludge age; Analyze and provide the generation reason of sludge bulking, and the confidence level of relative counter measures and this The reasoning results is provided.Described concrete reason comprises the sludge bulking that sludge bulking that LDO causes, sludge bulking that underload causes, sludge bulking that water inlet C/N/P causes than the unbalance sludge bulking that causes, low temperature, sludge bulking, the water inlet too high sludge bulking that causes of sulfide concentration and low pH that high load capacity causes cause etc.The solution of sludge bulking comprises the raising aeration rate, improves sludge loading, suitably adds nutriment, surmounts preliminary sedimentation tank, noxious material is introduced accident pool, adds chlorination, increases return sludge ratio and biological selector is set.Other composition is identical with embodiment one or two with connected mode.
Embodiment four: this embodiment is with embodiment three differences whether diagnosis exists the condition of the possibility that sludge bulking takes place to be in the step 7: when one or more symptom that following symptom takes place takes place simultaneously; Then there is the possibility that the sludge bulking phenomenon occurs; Symptom comprises: A, sludge settling index SVI circle and up-trend occurs in continuous 7 days between 150-200mL/g; B, microscopy are observed and are found the der Pilz index between 1-3, and up-trend occurs in continuous 7; Ascendant trend appears in C, sludge blanket height, approaches the warning position gradually; It is limpid that D, effluent quality obviously become; Otherwise there is not the possibility that sludge bulking takes place.Other composition is identical with embodiment three with connected mode.
Embodiment five: combine Fig. 1, Fig. 2 and Fig. 3 that this embodiment is described; This embodiment has been to give specific embodiment with embodiment one or four differences: certain sewage biological treatment system adopts the actual sanitary sewage of anaerobic-aerobic (A/O) PROCESS FOR TREATMENT; Find that in operational process sludge settling property progressively descends; The SVI value once had been higher than 350mL/g, and microscopy finds that der Pilz breeds in a large number, and the der Pilz index is 4-5; The der Pilz that grows has had a strong impact on the separation of muddy water in second pond, and part mud can run off in second pond along with water outlet.Adopt the present invention that the sludge bulking that takes place in this sewage biological treatment system is diagnosed to this phenomenon.Operational factor to this sewage biological treatment system detects and calculates; And in the information that system is the required input human-computer interaction interface; Find through internal reasoning; This sewage biological treatment system COD-sludge loading is lower than 0.18kgCOD/kdMLSS/d; The average low DO concentration of aerobic zone is 0.6mg/L, is the Filamentous Bulking that is caused by the low DO concentration of underload combination after diagnosing, and the solution that system provides is provided with biological selector, increase inflow, reduces hydraulic detention time, increases measures such as sludge volume raising load, increase aeration rate.According to these schemes, at first choose the increase aeration rate, aerobic zone DO concentration is promoted to 2.5mg/L, to move after 7 days, discovery SVI value is reduced to 270mL/g from 350mL/g before, but microscopy finds to still have more der Pilz to exist.In the situation that does not reduce DO concentration, increased sludge volume afterwards, sludge loading has been increased to more than the 0.24kgCOD/kdMLSS/d; After operation 10 days; The SVI of active sludge reduces to about 140mL/g in this sewage biological treatment system, and the der Pilz index is reduced to 1, and sludge bulking has obtained solution.That is to say if suspect or sludge bulking appears in definite sewage disposal system, can trigger sludge bulking prevention and controlling decision tree, and reason and solution thereof through the decision tree analysis generation.Decision tree is at first analyzed sludge settling property (SVI value, der Pilz exponential sum sludge blanket height); If the SVI value is normal or lowlyer (be lower than 150mL/g; This value can artificially be revised), continue to analyze settling basin sludge settling effect, if sludge settling is effective; Then the sludge bulking problem does not take place in illustrative system, withdraws from the decision tree inference system.If the settling basin sedimentation effect is poor, can know that then the sludge bulking phenomenon does not appear in system, impact but need the whether unimpeded or second pond of inspection sludge return pipe whether to receive hydraulic load.If the SVI value is between 150-200mL/g, and sludge settling property begins to descend the possibility of then deducibility sewage disposal system generation sludge bulking within a certain period of time gradually.If the SVI value is higher than 250mL/g, and the der Pilz index is greater than 3, and then deducibility sewage disposal system generation sludge bulking starts fault diagnosis and solves module.If the aerobic zone dissolved oxygen DO (dissolved oxygen, DO) concentration is lower, can strengthen aeration rate, and raising aerobic zone DO concentration can also take to reduce sludge loading (F/M) and sludge concentration means such as (MLSS) improve DO concentration indirectly; If sludge loading F/M value is lower, the control mode of taking has: strengthen sludge volume and improve load, biological selector is set; If high F/M value then can adopt returned sluge regeneration, strengthens return sludge ratio or reduce sludge discharge, reduce the F/M of system, and increase sludge age (SRT); If concentration of suspension is low excessively in the water inlet, can surmounts preliminary sedimentation tank or aerated grit chamber and improve aeration tank ss suspended solid concentration; If temperature is low excessively, can temperature low excessively, words with good conditionsi can feed Boiler Steam, or minimizing sludge volume that can be indirect improves the low temperature tolerance characteristics of mud; If water inlet pH value changes greatly or water inlet pH value is lower, then need confirm wastewater source, the control emission source adds alkaline matter simultaneously, improves water inlet pH value; If nutrients is deficient in the water inlet, when water inlet BOD/N＞100/3, add the nitrogen element, when water inlet BOD/P＞100: 1, add P elements, and confirm required throwing amount according to water inlet N/P concentration; If the denitrification zone denitrification that is provided with is incomplete, also possibly bring out sludge bulking, can suitably add carbon source or reduce the nitrification liquid reflux ratio; If the water inlet corruption contains sulfide, sulfide is carried out oxidation, carry out preaeration simultaneously; If the water inlet in or contain a large amount of der Pilzs in the bypass, then need confirm its source, need chlorination, pre-oxidation; Can adopt following mode to control if can't confirm reason: to add biocide or biological selector is set.Other composition is identical with embodiment one or four with connected mode.
Content of the present invention is not limited only to the content of above-mentioned each embodiment, and the combination of one of them or several embodiments equally also can realize the purpose of inventing.
Claims (6)
1. based on the prevention and the control method of sewage treatment plant's operating condition diagnosis sludge bulking expert system, it is characterized in that comprising base module (1), DBM (2), inference machine module (3), explanation engine module (4) and man-machine interaction unit (5) with the control expert system based on the sludge bulking prevention of sewage treatment plant's operating condition diagnosis;
Base module (1); Be used to store based on sewage treatment plant's operational factor and diagnose the method for sludge bulking and the method for control sludge bulking; Rule sets up the variation of emphasis based on technology operational factor and sludge settling property in the base module (1); And can be, and add successful case and the unsuccessful case in diagnosis and the control procedure through man-machine interaction unit (5) method in the base module (1) that upgrades in time; Storage diagnoses the method for sludge bulking to comprise according to sludge settling index SVI, der Pilz index, sludge blanket height, water outlet concentration of suspension and the method that the sludge loss phenomenon judges whether to take place sludge bulking whether occurs based on sewage treatment plant's operational factor in the said base module (1);
DBM (2) is used for storing raw data, the data of offline inspection and the secondary data that diagnostic procedure produces that online measuring instrument obtains;
Inference machine module (3) is used for the reasoning process that programming is formulated by CLIPS and utilizes the method for base module (1) to carry out forward reasoning;
Explanation engine module (4) is used for the The reasoning results that inference machine module (3) provides is made an explanation, and makes the user understand reasoning process and knowledge of being used and data;
Man-machine interaction unit (5) is accomplished the information interchange between user and the expert system;
It is characterized in that step is following:
Step 1: the required data of man-machine interaction unit (5) input diagnosis sludge bulking, data comprise the raw data that the on-line monitoring instrument obtains, the data and the technology operational factor of offline inspection;
Step 2: integral data in DBM (2) writes DBM (2) to the data of technology operational factor in the step 1 and sludge settling property, and obtains secondary data in internal calculation;
Step 3: the secondary data that data that the diagnosis sludge bulking that obtains step 1 is required and step 2 obtain imports inference machine module (3);
Step 4: whether inference machine module (3) through base module (1) diagnosis sludge bulking takes place; Be that then execution in step five; Not, then execution in step seven;
Step 5: sludge bulking takes place, and inference machine module (3) then diagnosis causes the concrete reason of sludge bulking generation, and provides to the solution that produces sludge bulking under this situation;
Step 6: select feasible solution according to actual conditions; And follow the tracks of the situation of change take sludge settling property after this solution, if sludge settling can recover, sludge bulking is resolved; Preserve this successful case to base module (1), log off; If sludge settling property does not recover, sludge bulking can not get solving, and preserves unsuccessful case to base module (1), continues the situation of change of collection technology operational factor, returns step 5, until completing steps six;
Step 7: sludge bulking does not take place, and whether diagnosis exists the possibility that sludge bulking takes place; Be that then execution in step eight; Not, then execution in step ten;
Step 8: have the possibility that the sludge bulking phenomenon occurs, inference machine module (3) diagnosis possibly cause taking place the reason of sludge bulking in ensuing operational process, and the prevention method of this possibility of prevention is provided;
Step 9: the method for selecting feasible prevention sludge bulking according to actual conditions; And follow the tracks of the situation of change take sludge settling property after this measure, if sludge settling property is improved, sludge bulking obtains prevention; Preserve this successful case to base module (1), log off; If sludge settling property does not improve, prevention method can not solve contingent sludge bulking, preserves unsuccessful case to base module (1), continues the situation of change of collection technology operational factor, returns step 8, until completing steps nine;
Step 10: do not have the possibility that sludge bulking takes place, then diagnose settling basin sludge settling effect, if the settling basin sedimentation effect is poor, whether unimpeded whether inspection sludge return pipe or second pond receive hydraulic load is impacted, and gives solution; If sludge settling is effective, then the sludge bulking problem does not take place in illustrative system, logs off.
2. prevention and control method based on sewage treatment plant's operating condition diagnosis sludge bulking expert system according to claim 1 is characterized in that the raw data that the on-line monitoring instrument in the step 1 is obtained comprises aerobic zone DO concentration, flow of inlet water, pH value, temperature and second pond sludge blanket; The data of offline inspection comprise influent quality index, effluent quality index, MLSS value, SVI value and der Pilz index; The technology operational factor comprises return sludge ratio, sludge volume and sludge age.
3. prevention and control method based on sewage treatment plant's operating condition diagnosis sludge bulking expert system according to claim 1 and 2 is characterized in that sludge settling property comprises sludge settling index SVI, sludge blanket height, der Pilz index in the step 2; Secondary data comprises COD-sludge loading, COD/N/P ratio, aeration tank hydraulic detention time, second pond hydraulic detention time and hydraulic load.
4. prevention and control method based on sewage treatment plant's operating condition diagnosis sludge bulking expert system according to claim 3; It is characterized in that diagnosing in the step 4 condition whether sludge bulking takes place to be: to find to exist under the hyphomycetic condition at microscopy; When one or more symptom that following symptom takes place takes place simultaneously; Then sludge bulking takes place, and described symptom comprises: a, sludge settling index SVI are higher than 250mL/g; B, observe to find the der Pilz index when microscopy and be higher than 3; C, sludge blanket height are near the warning position; Obvious sludge loss phenomenon appears in d, second pond.
5. according to claim 1 or 4 described prevention and control methods based on sewage treatment plant's operating condition diagnosis sludge bulking expert system; It is characterized in that in the step 5 when sludge bulking takes place; Inference machine module (3) is according to the technology operational factor in the sewage disposal system: dissolved oxygen concentration, sludge loading scope, influent quality index, effluent quality index, water temperature, return sludge ratio, sludge age, analyze and provide the concrete reason of the generation of sludge bulking; Described concrete reason comprises the sludge bulking that sludge bulking that LDO causes, sludge bulking that underload causes, sludge bulking that water inlet COD/N/P causes than the unbalance sludge bulking that causes, low temperature, sludge bulking, the water inlet too high sludge bulking that causes of sulfide concentration and low pH that high load capacity causes cause etc.
6. prevention and control method based on sewage treatment plant's operating condition diagnosis sludge bulking expert system according to claim 5 is characterized in that the solution of sludge bulking comprises the raising aeration rate in the step 5, improve sludge loading, suitably add nutriment, surmount preliminary sedimentation tank, noxious material are introduced accident pool, add chlorination, increase return sludge ratio and biological selector is set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100477905A CN102156406B (en) | 2011-02-28 | 2011-02-28 | Expert system and method for preventing and controlling sludge bulking under the diagnosis based on operation condition of sewage disposal plant |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100477905A CN102156406B (en) | 2011-02-28 | 2011-02-28 | Expert system and method for preventing and controlling sludge bulking under the diagnosis based on operation condition of sewage disposal plant |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102156406A CN102156406A (en) | 2011-08-17 |
| CN102156406B true CN102156406B (en) | 2012-11-07 |
Family
ID=44437948
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011100477905A Active CN102156406B (en) | 2011-02-28 | 2011-02-28 | Expert system and method for preventing and controlling sludge bulking under the diagnosis based on operation condition of sewage disposal plant |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102156406B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103241914A (en) * | 2013-05-23 | 2013-08-14 | 北京工业大学 | Method for effectively controlling sludge bulking caused by chlorine resistant filamentous bacteria |
| CN105045782A (en) * | 2014-11-14 | 2015-11-11 | 国家电网公司 | Ferroresonance fault knowledge base construction method |
| CN104914213B (en) * | 2015-05-13 | 2016-10-19 | 西安建筑科技大学 | A kind of method of early warning activated sludge filamentary discharges |
| CN106745707A (en) * | 2016-12-30 | 2017-05-31 | 天津创业环保集团股份有限公司 | A kind of control system and control method of sewage disposal sludge material balance |
| WO2020183576A1 (en) * | 2019-03-11 | 2020-09-17 | 株式会社 ゴーダ水処理技研 | Operation management system for wastewater treatment facility |
| CN111210599A (en) * | 2019-12-11 | 2020-05-29 | 南京连易智能科技有限公司 | Chemical industry index early warning method and system, electronic equipment and storage medium |
| CN111675336A (en) * | 2020-07-08 | 2020-09-18 | 北京信通碧水再生水有限公司 | Segmented water inlet multistage A/O process sludge expansion control system and method |
| CN112363391B (en) * | 2020-10-14 | 2022-03-11 | 北京工业大学 | Sludge bulking inhibition method based on self-adaptive segmented sliding mode control |
| CN113052718A (en) * | 2021-01-14 | 2021-06-29 | 北京朗新明环保科技有限公司 | Intelligent water affair platform |
| CN113204859B (en) * | 2021-03-17 | 2023-03-14 | 杭州杭开环境科技有限公司 | Sewage treatment AAO technology intelligent design back-calculation system |
| CN114573096A (en) * | 2022-03-25 | 2022-06-03 | 南京大学 | Sewage treatment aeration system control method based on microbial community |
| CN115583722A (en) * | 2022-08-29 | 2023-01-10 | 武汉华康世纪医疗股份有限公司 | Flocculation sludge poisoning treatment equipment suitable for medical waste water |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010069973A (en) * | 2001-05-23 | 2001-07-25 | 이용운 | Remote controling system of a processing plant |
| CN102063062A (en) * | 2011-02-18 | 2011-05-18 | 哈尔滨工业大学 | Sludge bulking control expert system for diagnosis based on filamentous bacteria population structure |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH02257302A (en) * | 1989-03-30 | 1990-10-18 | Akio Watanabe | Monitor and control equipment for rain water and sewage pump plant |
-
2011
- 2011-02-28 CN CN2011100477905A patent/CN102156406B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010069973A (en) * | 2001-05-23 | 2001-07-25 | 이용운 | Remote controling system of a processing plant |
| CN102063062A (en) * | 2011-02-18 | 2011-05-18 | 哈尔滨工业大学 | Sludge bulking control expert system for diagnosis based on filamentous bacteria population structure |
Non-Patent Citations (1)
| Title |
|---|
| JP平2-257302A 1990.10.18 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102156406A (en) | 2011-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102156406B (en) | Expert system and method for preventing and controlling sludge bulking under the diagnosis based on operation condition of sewage disposal plant | |
| Farhi et al. | Prediction of wastewater treatment quality using LSTM neural network | |
| Turkdogan-Aydınol et al. | A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater | |
| Santín et al. | Fuzzy logic for plant-wide control of biological wastewater treatment process including greenhouse gas emissions | |
| CN101555068B (en) | Culture method of aerobic granular sludge for simultaneous denitrification and dephosphorization of domestic sewage at low and normal temperature | |
| Oulebsir et al. | Optimization of the energy consumption in activated sludge process using deep learning selective modeling | |
| CN100569667C (en) | Modified four-section water-feeding A/O deep denitrogenation device and course control method for use | |
| CN110188946B (en) | Sewage parameter prediction method and sewage prediction system | |
| CN102063062B (en) | Sludge bulking control expert system for diagnosis based on filamentous bacteria population structure | |
| CN105836965A (en) | Intelligentized sewage treatment expert system | |
| CN103864194A (en) | Emergency control method for sewage treatment system for treating abnormal influent/effluent quality | |
| CN105938581A (en) | Industrial park terminal water treatment system assessment method | |
| CN108088974A (en) | A kind of flexible measurement method of anaerobism while denitrification methane phase process water outlet nitrate nitrogen | |
| Huang et al. | A predictive model for determining the nitrite concentration in the effluent of an anammox reactor using ensemble regression tree algorithm | |
| CN204848445U (en) | Carbon source is thrown with controlling means based on denitrification speed analysis appearance | |
| Nasr et al. | Sustainable management of wastewater treatment plants using artificial intelligence techniques | |
| Yu et al. | Application of a water-energy-carbon coupling index to evaluate the long-term operational stability of the anaerobic-anoxic-oxic-membrane bioreactor (A2/O-MBR) process under the influence of rainstorms | |
| CN103991962A (en) | ICAST (Intermittent or Cyclic Activated Sludge Technology) treatment method for recycling wastewater on sites and self-control and monitoring device thereof | |
| Pagilla | Operational optimization and control strategies for decarbonization in WRRFs | |
| CN206624748U (en) | A kind of municipal waste water treatment sludge minimizing intermittent motion apparatus for being interrupted dephosphorization | |
| CN118495692B (en) | Sewage biological purification method and system | |
| CN100475717C (en) | Alternate aerobic-anaerobic completely mixing active sludge process | |
| CN206447723U (en) | A kind of high concentration lint wastewater treatment equipment | |
| Igbodo | Implementation of standard methods for parameters routinely monitored in wastewater treatment plants and application in bioaugmentation with mycolicibacterium sp. In granular sludge reactors | |
| Gogina et al. | Comparison of methods for calculating biological wastewater treatment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |