Integrated real-time SRT and aeration control in the city of Grand Rapids

Transcription

1 2016 MWEA 92 st Annual Conference Sunday, June 18 th - Wednesday, June 21 st Boyne Mountain Resort Integrated real-time SRT and aeration control in the city of Grand Rapids M. Lunn, City of Grand Rapids E. Belia, Primodal US Inc. Oliver Schraa, inctrl Solutions Leiv Rieger, inctrl Solutions John Copp, Primodal Inc

2 Grand Rapids, MI Design flow 61 MGD Average flow 38 MGD Peak wet weather 90 MGD Population 270,000 Serve 11 communities Enhanced biological phosphorus removal Aeration control Sludge Retention Time (SRT) control Disinfection (UV) Wet weather operation Grit aeration Future high strength waste equalization Future digestion

3 Monitoring & Control Platform Real-time data quality of sensors Optimization of sensor maintenance Control system design and testing Dynamic process modeling Process monitoring Decision support: data to information (KPIs, benchmarking) Process KPI1 KPI2 Nitrification kwh / lb NH x removed % TKN removal Phosphorus removal Mole metal / mole TP removed % TP removal Sludge production Lb sludge removed from the lb dry sludge per kg influent secondary per lb of BOD going BOD to the primaries Power consumption kw / MG treated kw / lb BOD or N Peak factors (annual) BOD, P, Ammonia Annual maximum hourly load rate / annual average hourly load rate $ / hourly average load

4 Real time plant-wide control Real-time control loops Wet weather operation Plant process model Aeration control Grit aeration RAS SRT / WAS Disinfection (UV) Enhanced biological phosphorus removal Future digestion Future high strength waste equalization

5 Commissioning the Ammonia-DO Controller

6 DO and Ammonia-DO control DO control Ammonia and DO control Rieger, L., Jones, R. M., Dold, P. L., & Bott, C. B. (2014). Ammonia-Based Feedforward and Feedback Aeration Control in Activated Sludge Processes. Water Environment Research, 86(1),

7 Ammonia based aeration control Grand Rapids North AT 10 Q air Setpoint 2.5 scfm high PI 7,800 scfm 0.5 scfm low DO Setpoint NH x -N Setpoint 2.5 mg DO high /L PI 1.2 mg DO/L 2.0 mg N/L 0.5 mg DO low /L PID Q air 7,897 scfm 1.35 mg DO/L 1.75 mg N/L M valve Valve Position AT10 75 % VP AT7 VP AT8 55 % 45 % MOV VP AT9 45 % Pressure Setpoint 7.5 psig 300 % high 125 % PI 50 % low Blower Capacity Setpoint PT 7.35 psig

8 North ABAC Early implementation Final tuning

9 Blower power draw

10 Concentration (mg/l) Airflow per tank (scfm) Concentation (mg/l) Airflow per tank (scfm) ABAC full-scale and model comparison Time (d) Effluent ammonia DO control probe NHx Setpoint North DO Setpoint North Total airflow NHx_Setpoint NHx T09 DO_Setpoint Air_Flow (scfm) T9 15 per. Mov. Avg. (DO T09)

11 Ammonia based aeration control

12 Airflow [Nm3/d] Model estimated airflow savings 400,000 DO (1,5) 350, , , ,000 DO (1,5) 16 % DO (0.5,3) 23 % 150,000 NO ABAC DW ABAC DW ABAC DW

13 JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN kw blowers ammonia removed (kg/d) kw/kg ammonia removed kw blowers kw/kg kw/kg ammonia ammonia removed ammonia removed (kg/d) Temperature Temperature (oc) (oc) Development of success metrics % 12% SEP '15 SEP '16 OCT '15 OCT ' JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN without ABAC with ABAC Ammonia removed no ABAC Ammonia removed with ABAC T no ABAC T with ABAC kw without ABAC kw with ABAC kw/kg ammonia removed without ABAC kw/kg ammonia removed with ABAC

14 Monitoring and Control Savings South Plant ammonia based aeration control: $60,800 /year North Plant ammonia based aeration control (estimate): $62,500 /year

15 Commissioning the Ammonia-SRT Controller

16 Combined DO-SRT control Measured NH x NH x set point Ammonia Controller Measured DO DO set point DO Controller Airflow Desired Average DO Concentration SRT set point Optimizer SRT set point Calculated Dynamic SRT SRT Controller Selects optimal SRT in context of desired DO set point WAS Flow Rate Schraa, O., Rieger, L. and Alex, J. (2016). Coupling SRT Control with Aeration Control Strategies. Proceedings of WEFTEC.16, New Orleans, LA, USA.

17 Ammonia - SRT control Grand Rapids North ABAC-SRT Q WAS Setpoint MLSS Setpoint SRT Setpoint DO setpoint target 1 mgd high 3500 mg TSS high /L 15 d SRT high Q WAS 0.55 mgd PI 2500 mg TSS/L PI 5.0 d SRT 1.0 mg DO/L PI 0 mgd low 1000 mg TSS low /L 3 d SRT low 3480 mg TSS/L SRT estim 4.85 d DO average 1.05 mg DO/L

18 SRT (d) MLSS (mg/l) Ammonia (mg/l) DO (mg/l) MLSS (mg/l) Q was (gpm) DO (mg/l) SRT (d) Model-based controller evaluation ABAC and independent MLSS control MLSS-WAS controller Days DO controller - SRT Days MLSS_setpoint MLSS_filtered Qwas Qwas bound max DO setpoint SRT_setpoint SRT-MLSS controller Ammonia-DO controller Days Days SRT_setpoint SRT_estimated MLSS_setpoint Ammonia setpoint DO setpoint NH4 exit

19 SRT (d) MLSS (mg/l) Ammonia (mg/l) DO (mg/l) MLSS (mg/l) Q was (gpm) DO (mg/l) SRT (d) Model-based controller evaluation DO-SRT integrated control MLSS-WAS controller DO controller - SRT Days Days MLSS measurement MLSS_setpoint MLSS_filtered Qwas DO setpoint DO_filtered SRT_setpoint SRT-MLSS controller Ammonia-DO controller Days Days SRT_setpoint SRT_estimated MLSS_setpoint Ammonia Ammonia setpoint DO setpoint

20 Commissioning the MLSS-Qwas PI

21 MLSS - Qwas

22 SRT - MLSS

23 Controller In-Out

24 Full-scale implementation challenges Controller performance - slow response time Plant planned maintenance Hydraulic issues Internet security - remote access Plant upgrades - SCADA

25 DO SRT control Tank 7 Tank 8 Tank 9 Tank 10 Primary influent RAS

26 SCADA upgrade

27 Steps to successful ICA Design: Include ICA as part of the design process Instrumentation: make use of new sensors and instruments implement adequate maintenance plans on-site develop Standard Operation Procedures for these sensors similar to laboratory measurements Computers: Take advantage of computing and storage capacity G. Olsson, B. Carlsson, J. Comas, J. Copp, K. V. Gernaey, P. Ingildsen, U. Jeppsson, C. Kim, L. Rieger, I. Rodríguez-Roda, J.-P. Steyer, I. Takács, P. A. Vanrolleghem, A. Vargas, Z. Yuan and L. Åmand (2014) Instrumentation, control and automation in wastewater from London 1973 to Narbonne 2013, Water Science & Technology April 2014

28 Steps to successful ICA Signal treatment and monitoring: Develop data validation tools and monitoring, fault detection and diagnosis methods integrate and re-use the data in operator support tool Process control: apply control technology test already-developed process control ideas in full scale System-wide: from unit process to whole plant perspective to wider system understand how to manage disparate objectives and performance criteria G. Olsson, B. Carlsson, J. Comas, J. Copp, K. V. Gernaey, P. Ingildsen, U. Jeppsson, C. Kim, L. Rieger, I. Rodríguez-Roda, J.-P. Steyer, I. Takács, P. A. Vanrolleghem, A. Vargas, Z. Yuan and L. Åmand (2014) Instrumentation, control and automation in wastewater from London 1973 to Narbonne 2013, Water Science & Technology April 2014

29 Staff Changes Environmental Resource Technician 1 part Laboratory Technician 1 part Air Pollution Control Inspector 1 part Operator Responsible for Laboratory Analysis, Online Sensors (Air, Water, Wastewater, Stormwater, River) and Data Quality

30 Decade of Progress

31 Summary Decades of work Data Quality Installation Controls Staff New Jobs Reduced Energy Improved Operation