Wastewater treatment plant online monitoring and control

Patent metadata

Field	Value
Publication number	US-10046995-B2
Application number	US-201214234955-A
Country	US
Kind code	B2
Filing date	Jul 25, 2012
Priority date	Jul 26, 2011
Publication date	Aug 14, 2018
Grant date	Aug 14, 2018

Abstract

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.

First claim

What is claimed is:

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; and
providing 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 (CO 2 ), 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 (CH 4 ) concentration, AD Biogas CO 2 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 CO 2 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

Assignees

• Kumar Aditya
• Murray Anthony John
• Shi Ruijie
• Wan Zhaoyang
• Dokucu Mustafa Tekin
• Prasad Vijaysai
• Gen Electric

Inventors

• Kumar Aditya
• Murray Anthony John
• Shi Ruijie
• Wan Zhaoyang
• Dokucu Mustafa Tekin
• Prasad Vijaysai

Classifications

• C02F2209/40

  Liquid flow rate · CPC title

• C02F3/1268

  Membrane bioreactor systems · CPC title

• C02F2305/06

  Nutrients for stimulating the growth of microorganisms · CPC title

• C02F2209/04

  Oxidation reduction potential [ORP] · CPC title

• C02F3/006 Primary

  Regulation methods for biological treatment · CPC title