Wastewater treatment plant online monitoring and control
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
C02F-003/00
C02F-003/12
C02F-003/28
C02F-003/30
C02F-001/66
G01N-033/18
G01N-021/00
G01N-033/00
G01N-031/00
출원번호
US-0234955
(2012-07-25)
등록번호
US-10046995
(2018-08-14)
국제출원번호
PCT/US2012/048163
(2012-07-25)
§371/§102 date
20140422
(20140422)
국제공개번호
WO2013/016438
(2013-01-31)
발명자
/ 주소
Kumar, Aditya
Murray, Anthony John
Shi, Ruijie
Wan, Zhaoyang
Dokucu, Mustafa Tekin
Prasad, Vijaysai
출원인 / 주소
General Electric Company
대리인 / 주소
Wegman, Hessler & Vanderburg
인용정보
피인용 횟수 :
0인용 특허 :
12
초록▼
A method of operating a waste water treatment plant (WWTP) having at least one of an aerobic digester (AD) and a membrane bioreactor (MBR) is described. The method of operating AD is comprised of monitoring and controlling AD in real-time using an online extended Kalman filter (EKF) having a online
A method of operating a waste water treatment plant (WWTP) having at least one of an aerobic digester (AD) and a membrane bioreactor (MBR) is described. The method of operating AD is comprised of monitoring and controlling AD in real-time using an online extended Kalman filter (EKF) having a online dynamic model of AD. The EKF uses real-time AD measured data, and online dynamic model of AD to update adapted model parameters and estimate model based inferred variables for AD, which are used for AD control by AD control system having supervisory and low-level control layers. The method of operating MBR is similar to that of AD. The supervisory control ensures the WWTP satisfying the effluent quality requirement while minimize the operation cost. A WWTP having at least one of AD or MBR is disclosed. The method of operating a WWTP can be implemented using a computer.
대표청구항▼
1. A method of monitoring and controlling the operating conditions of an anaerobic digester (AD), comprising: providing an AD;monitoring said AD, wherein said monitoring comprises: providing an AD offline extended Kalman filter (EKF) having an offline dynamic model of said AD, providing an AD online
1. A method of monitoring and controlling the operating conditions of an anaerobic digester (AD), comprising: providing an AD;monitoring said AD, wherein said monitoring comprises: providing an AD offline extended Kalman filter (EKF) having an offline dynamic model of said AD, providing an AD online EKF having an online dynamic model of said AD; wherein said offline and said online dynamic models of said AD are comprised of states, process material balances, energy balances, bio-chemical reaction kinetics, estimated parameters, and adapted model parameters; wherein said adapted model parameters are a subset of said estimated parameters;providing historical operation data for said AD, wherein said historical operation data is comprised of historical measured input data, historical measured output data, and historical laboratory analysis data;identifying said estimated parameters of said offline dynamic model of said AD using said AD offline EKF and said historical operation data for said AD;importing said estimated parameters from said offline dynamic model of said AD into said online dynamic model of said AD;providing real time operation data for said AD to said AD online EKF, wherein said real time operation data is comprised of real time measured input data and real time measured output data of said AD;updating said adapted model parameters of said online dynamic model of said AD and estimating one or more model based inferred variables of said AD using said AD online EKF, said online dynamic model of said AD, said real time measured input data of said AD, and said real time measured output data of said AD; andproviding one or more of said adapted model parameters of said online dynamic model of said AD and said model based inferred variables of said AD to an operator of said AD;wherein limits are applied to one or more of said estimated parameters and said adapted model parameters; wherein constraints are applied to one or more of said model based inferred variables;controlling said AD, wherein said controlling comprises: providing an AD control system;wherein said AD is comprised of an AD reactor and optionally a PA reactor; wherein said AD control system uses one or more of said real time measured input data of said AD, said real time measured output data of said AD, said estimated parameters of said online dynamic model of said AD, or said model based inferred variables of said AD to control at least one of a nutritional additive concentration of said AD reactor, a nutritional additive concentration of said PA reactor, AD reactor pH, PA reactor pH, biomass concentration of said AD reactor, fluid level of said PA reactor, or a recycle flow rate of said AD;wherein said AD control system is comprised of an AD supervisory control system and an AD low-level control system. 2. The method of claim 1, wherein said AD is comprised of an AD reactor. 3. The method of claim 2, wherein said AD reactor is a continuously stirred tank reactor (CSTR), upflow anaerobic sludge blanket reactor (UASB), expanded granular sludge bed reactor (EGSB), mixed bed, moving bed, low-rate, or high-rate reactor. 4. The method of claim 2, wherein said AD is further comprised of a pre-acidification (PA) reactor, wherein said AD reactor and said pre-acidification reactor are modeled separately in both of said online and offline dynamic models of said AD. 5. The method of claim 2, wherein said AD is comprised of a mixing stage and at least one recycle line. 6. The method of claim 5, wherein said at least one recycle line of said AD is a pre-acidification reactor recycle line or an AD reactor recycle line. 7. The method of claim 1, wherein materials for said material balances in said online and offline dynamic models of said AD are comprised of insoluble organics, soluble substrates, volatile fatty acids, biomass, inorganic carbon and alkalinity. 8. The method of claim 7, wherein said insoluble organics is comprised of carbohydrates, protein and fat; wherein said soluble substrate and VFA include at least one of sugars, long chain fatty acids (LCFA), amino acids, acetate acid, or propionate acid; wherein said biomass includes biomass for acedogenesis, acetogenesis, acetoclastic methanogenesis and hydrogen methanogenesis bio-chemical processes. 9. The method of claim 7, wherein said inorganic carbon is comprised of at least one of carbon dioxide (CO2), carbonate, or bicarbonate. 10. The method of claim 7, wherein said alkalinity is comprised of alkalinity associated with bicarbonate, VFA, added alkali, and generation of ammonia and hydrogen sulfide. 11. The method of claim 1, wherein said bio-chemical reaction kinetics in said online and offline dynamic models of said AD are comprised of at least one of insoluble organics hydrolysis, acedogenesis, acetogenesis, acetoclastic methanogenesis, or a hydrogen methanogenesis process. 12. The method of claim 1, wherein said AD is further comprised of a PA reactor, wherein said historical operation data of said AD and said real time operation data of said AD are comprised of at least one of raw influent pH, raw influent temperature, raw influent flow rate, raw influent total organic carbon (TOC), raw influent total inorganic carbon (TIC), added alkali flow rate, PA reactor fluid level, AD feed flow rate, raw influent soluble chemical oxygen demand (SCOD), raw influent total chemical oxygen demand (TCOD), raw influent soluble bio-chemical oxygen demand (SBOD), raw influent volatile suspended solids (VSS), raw influent total suspended solids (TSS), raw influent soluble inorganic nitrogen, raw influent VFA, added alkali concentration, PA reactor pH, PA effluent TOC, PA effluent TIC, AD biogas flow rate, AD biogas methane (CH4) concentration, AD Biogas CO2 concentration, AD reactor pH, AD effluent TOC, AD effluent TIC, AD effluent VFA, AD effluent alkalinity, AD reactor mixed liquor volatile suspended solids (MLVSS), AD effluent TCOD, AD effluent SCOD, AD effluent VSS, or AD effluent TSS. 13. The method of claim 1, wherein said AD is further comprised of a PA reactor, wherein said estimated parameters and said adapted model parameters of said offline dynamic model of said AD and said online dynamic model of said AD are comprised of at least one of PA reactor composite fraction of carbohydrate, PA reactor composite fraction of fat, PA reactor composite fraction of protein, PA reactor fraction of insoluble convertible to SBOD, PA reactor acedogenthese reaction coefficient, PA reactor biomass decay rate, PA reactor insoluble hydrolysis reaction coefficient, PA reactor insoluble flow out coefficient, PA reactor CO2 escape coefficient, AD reactor composite fraction of carbohydrate, AD reactor composite fraction of fat, AD reactor composite fraction of protein, AD reactor fraction of insoluble convertible to SBOD, AD reactor acedogenthese reaction coefficient, AD reactor acetogenesis reaction coefficient, AD reactor acetoclastic methanogenesis reaction coefficient, AD reactor hydrogen methanogenesis reaction coefficient, AD reactor biomass decay rate, PA reactor insoluble hydrolysis reaction coefficient, or PA reactor insoluble flow out coefficient. 14. The method of claim 1, wherein at least one of said estimated parameters of said offline dynamic model of said AD and said model based inferred variables of said online dynamic model of said AD are estimated with confidence intervals. 15. The method of claim 1, wherein said AD is further comprised of a PA reactor, wherein said model based inferred variables of said online dynamic model of said AD are comprised of at least one of the following unmeasured inputs or outputs of said AD: raw influent insoluble COD, raw influent insoluble inert COD, raw influent soluble inert COD, raw influent SBOD saccharide, raw influent SBOD LCFA, raw influent SBOD amino acid, raw influent propionate acid, raw influent acetate acid, raw influent inorganic carbon content, raw influent alkalinity, raw influent inorganic nitrogen, raw influent SCOD, raw influent TCOD, raw influent SBOD, PA reactor alkalinity, PA reactor VFA, PA reactor temperature, PA reactor SCOD, PA reactor TCOD, PA reactor SBOD, AD reactor alkalinity, AD reactor VFA, AD reactor temperature, AD reactor SCOD, AD reactor SBOD, AD reactor acedogenthese biomass, AD reactor acetogenesis biomass, AD reactor acetoclastic methanogenesis biomass, AD reactor hydrogen methanogenesis biomass, AD reactor insoluble COD, AD reactor insoluble inert COD, AD reactor soluble inert COD, AD reactor SBOD saccharide, AD reactor SBOD LCFA, AD reactor SBOD amino acid, AD reactor propionate acid, AD reactor acetate acid, AD reactor inorganic carbon content, AD reactor alkalinity, AD reactor inorganic nitrogen, AD reactor SCOD, AD reactor TCOD, AD reactor SBOD, SCOD conversion rate, CH4 conversion efficiency, or recycle flow rate. 16. The method of claim 1, further comprising tuning said adapted model parameters of said online dynamic model of said AD using different weights for said real time operation data and a prior knowledge of measurement accuracy of said real time operation data. 17. The method of claim 1, further comprising adjusting said adapted model parameters of said online dynamic model of said AD by one or both of: calculating model predicted outputs of said AD using said AD online EKF, said online dynamic model of said AD, said real time measured input data of said AD, and said real time measured output data of said AD, comparing said measured output data of said AD and said model predicted outputs of said AD, and updating said adapted model parameters of said online dynamic model of said AD such that said real time measured output data of said AD substantially correspond with said model predicted outputs of said AD; orperiodically re-identifying said estimated parameters of said offline dynamic model of said AD using said AD offline EKF and said historical operation data for said AD, and importing said estimated parameters from said offline dynamic model of said AD into said online dynamic model of said AD. 18. The method of claim 1, wherein at least one of said monitoring said AD or said controlling said AD is performed using a computer. 19. The method of claim 1, wherein controlling said nutritional additive concentration of said AD prevents biomass overfeeding and starvation, wherein controlling said nutritional additive concentration of said PA reactor prevents biomass overfeeding and starvation, wherein controlling said AD reactor pH minimizes alkali dosing, wherein controlling said PA reactor pH minimizes alkali dosing, wherein controlling said biomass concentration of said AD reactor offsets biomass inhibition and saves alkali, wherein controlling a recycle flow rate of said PA reactor minimizes alkali dosing and maintains fluid level of said PA reactor, and wherein controlling a recycle flow rate of said AD reactor maximizes COD conversion and biogas generation. 20. The method of claim 1, wherein said AD supervisory control system is comprised of at least one of an AD reactor pH supervisory controller, a PA reactor pH supervisory controller, or an PA:AD overall recycle flow ratio supervisory controller. 21. The method of claim 20, wherein said AD reactor pH supervisory controller is comprised of an AD reactor nonlinear Proportion-Integration (PI) pH controller and an AD reactor Proportion (P) alkalinity controller in a cascaded configuration. 22. The method of claim 20, wherein said PA reactor pH supervisory controller is comprised of a PA reactor nonlinear PI pH controller and a PA reactor P alkalinity controller in a cascaded configuration. 23. The method of claim 20, wherein said PA:AD overall recycle flow ratio supervisory controller is comprised of a PA:AD recycle ratio controller, and a PA reactor and AD reactor recycle flow rate controller. 24. The method of claim 1, wherein said AD low-level control system is comprised of at least one of an AD reactor biomass concentration controller, a PA reactor fluid level controller, a PA reactor nutritional additive concentration controller, or an AD reactor nutritional additive concentration controller. 25. The method of claim 20, wherein at least one of said AD reactor pH supervisory controller or said PA reactor pH supervisory controller uses one or more of said model based inferred variables of said AD. 26. The method of claim 25, wherein said model based inferred variables of said AD include PA alkalinity and/or AD alkalinity. 27. The method of claim 20, wherein at least one of said AD reactor pH supervisory controller or said PA reactor pH supervisory controller has a feedforward control action; wherein said feedforward control action uses one or more of said a model based inferred variables of said AD. 28. The method of claim 27, wherein said model based inferred variables of said AD includes is raw influent alkalinity. 29. The method of claim 24, wherein at least one of said AD reactor biomass concentration controller, said PA reactor nutritional additive concentration controller, and said AD reactor nutritional additive concentration controller uses at least one of said estimated parameters of said online dynamic model of said AD or said model based inferred variables of said AD. 30. The method of claim 29, wherein said estimated parameters of said online dynamic model of said AD or said model based inferred variables of said AD is at least one of reaction coefficients and biomass concentrations for hydrolysis, acedogenthese, acetogenesis, acetoclastic methanogenesis, or hydrogen methanogenesis processes. 31. The method of claim 20, wherein said AD reactor pH supervisory controller is comprised of an AD reactor nonlinear PI pH controller and a PA reactor P alkalinity controller in a cascaded configuration. 32. The method of claim 7, wherein said insoluble organics is comprised of carbohydrates, protein and fat; wherein said soluble substrate and VFA includes sugars, long chain fatty acids (LCFA), amino acids, acetate acid, or propionate acid; wherein said biomass includes biomass for acedogenesis, acetogenesis, acetoclastic methanogenesis, and hydrogen methanogenesis bio-chemical processes. 33. The method of claim 7, wherein said inorganic carbon is comprised of CO2, carbonate, or bicarbonate. 34. The method of claim 1, wherein said bio-chemical reaction kinetics in said online and offline dynamic models of said AD are comprised of insoluble organics hydrolysis, acedogenesis, acetogenesis, acetoclastic methanogenesis, and a hydrogen methanogenesis process. 35. The method of claim 1, wherein said AD is further comprised of a PA reactor, wherein said historical operation data of said AD and said real time operation data of said AD are comprised of raw influent pH, raw influent temperature, raw influent flow rate, raw influent TOC, raw influent TIC, added alkali flow rate, PA reactor fluid level, AD feed flow rate, raw influent SCOD, raw influent TCOD, raw influent SBOD, raw influent VSS, raw influent TSS, raw influent soluble inorganic nitrogen, raw influent VFA, added alkali concentration, PA reactor pH, PA effluent TOC, PA effluent TIC, AD biogas flow rate, AD biogas CH4 concentration, AD Biogas CO2 concentration, AD reactor pH, AD effluent TOC, AD effluent TIC, AD effluent VFA, AD effluent alkalinity, AD reactor MLVSS, AD effluent TCOD, AD effluent SCOD, AD effluent VSS, or AD effluent TSS. 36. The method of claim 1, wherein said AD is further comprised of a PA reactor, wherein said estimated parameters and said adapted model parameters of said offline dynamic model of said AD and said online dynamic model of said AD are comprised of PA reactor composite fraction of carbohydrate, PA reactor composite fraction of fat, PA reactor composite fraction of protein, PA reactor fraction of insoluble convertible to SBOD, PA reactor acedogenthese reaction coefficient, PA reactor biomass decay rate, PA reactor insoluble hydrolysis reaction coefficient, PA reactor insoluble flow out coefficient, PA reactor CO2 escape coefficient, AD reactor composite fraction of carbohydrate, AD reactor composite fraction of fat, AD reactor composite fraction of protein, AD reactor fraction of insoluble convertible to SBOD, AD reactor acedogenthese reaction coefficient, AD reactor acetogenesis reaction coefficient, AD reactor acetoclastic methanogenesis reaction coefficient, AD reactor hydrogen methanogenesis reaction coefficient, AD reactor biomass decay rate, PA reactor insoluble hydrolysis reaction coefficient, and PA reactor insoluble flow out coefficient. 37. The method of claim 1, wherein said AD is further comprised of a PA reactor, wherein said model based inferred variables of said online dynamic model of said AD are comprised of the following unmeasured inputs or outputs of said AD: raw influent insoluble COD, raw influent insoluble inert COD, raw influent soluble inert COD, raw influent SBOD saccharide, raw influent SBOD LCFA, raw influent SBOD amino acid, raw influent propionate acid, raw influent acetate acid, raw influent inorganic carbon content, raw influent alkalinity, raw influent inorganic nitrogen, raw influent SCOD, raw influent TCOD, raw influent SBOD, PA reactor alkalinity, PA reactor VFA, PA reactor temperature, PA reactor SCOD, PA reactor TCOD, PA reactor SBOD, AD reactor alkalinity, AD reactor VFA, AD reactor temperature, AD reactor SCOD, AD reactor SBOD, AD reactor acedogenthese biomass, AD reactor acetogenesis biomass, AD reactor acetoclastic methanogenesis biomass, AD reactor hydrogen methanogenesis biomass, AD reactor insoluble COD, AD reactor insoluble inert COD, AD reactor soluble inert COD, AD reactor SBOD saccharide, AD reactor SBOD LCFA, AD reactor SBOD amino acid, AD reactor propionate acid, AD reactor acetate acid, AD reactor inorganic carbon content, AD reactor alkalinity, AD reactor inorganic nitrogen, AD reactor SCOD, AD reactor TCOD, AD reactor SBOD, SCOD conversion rate, CH4 conversion efficiency, and recycle flow rate. 38. The method of claim 1, further comprising adjusting said adapted model parameters of said online dynamic model of said AD by: calculating model predicted outputs of said AD using said AD online EKF, said online dynamic model of said AD, said real time measured input data of said AD, and said real time measured output data of said AD, comparing said measured output data of said AD and said model predicted outputs of said AD, and updating said adapted model parameters of said online dynamic model of said AD such that said real time measured output data of said AD substantially correspond with said model predicted outputs of said AD; andperiodically re-identifying said estimated parameters of said offline dynamic model of said AD using said AD offline EKF and said historical operation data for said AD, and importing said estimated parameters from said offline dynamic model of said AD into said online dynamic model of said AD. 39. The method of claim 1, further comprising controlling said AD, wherein said controlling comprises: providing an AD control system;wherein said AD is comprised of an AD reactor and a PA reactor; wherein said AD control system uses said real time measured input data of said AD, said real time measured output data of said AD, said estimated parameters of said online dynamic model of said AD, and said model based inferred variables of said AD to control at least one of a nutritional additive concentration of said AD reactor, a nutritional additive concentration of said PA reactor, AD reactor pH, PA reactor pH, biomass concentration of said AD reactor, fluid level of said PA reactor, and a recycle flow rate of said AD. 40. The method of claim 1, wherein said AD supervisory control system is comprised of an AD reactor pH supervisory controller, a PA reactor pH supervisory controller, and an PA:AD overall recycle flow ratio supervisory controller. 41. The method of claim 1, wherein said AD low-level control system is comprised of an AD reactor biomass concentration controller, a PA reactor fluid level controller, a PA reactor nutritional additive concentration controller, and an AD reactor nutritional additive concentration controller. 42. The method of claim 40, wherein said AD reactor pH supervisory controller and said PA reactor pH supervisory controller uses one or more of said model based inferred variables of said AD. 43. The method of claim 40, wherein said AD reactor pH supervisory controller and said PA reactor pH supervisory controller has a feedforward control action; wherein said feedforward control action uses one or more of said model based inferred variables of said AD. 44. The method of claim 20, wherein said AD reactor pH supervisory controller and said PA reactor pH supervisory controller uses one or more of said model based inferred variables of said AD. 45. The method of claim 20, wherein said AD reactor pH supervisory controller and said PA reactor pH supervisory controller has a feedforward control action; wherein said feedforward control action uses one or more of said model based inferred variables of said AD.
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