Predictive control of ferrous chloride dosing to minimise corrosion & odour in Bellambi system. 15 June 2015

Transcription

1 Predictive control of ferrous chloride dosing to minimise corrosion & odour in Bellambi system 15 June 2015

2 Agenda! Background! Objectives of study! Auto-regressive model! Modelling assessment to determine control constants! Results and findings! Conclusions

3 Background! The Sewer Corrosion and Odour Research (SCORe) program was a five-year, $20 million major initiative funded by the Australian Research Council (ARC) and many Australian water utilities along with partners from overseas.! A sub project of this program, SP5, focused on the automatically controlled dosing of chemicals into sewers for corrosion and odour control.! Presently Sydney Water uses approximately 20,000 tonnes per year of ferrous chloride to remove hydrogen sulphides in wastewater during its transport to wastewater treatment plants.

4 Background (cont)! Previous work had established that ferrous dosing was effective in controlling sulphides in solution by precipitating them out.! The ARC project had also established a relationship between sulphide generation and the HRT. Total sulphides are therefore related to the sewer flow rate.

5 Background (cont)! Current practice for ferrous chloride dosing is to characterise the wastewater flow pattern and then determine the appropriate flow-paced dosing rate.! This project targets long pressure mains

6 Objectives! Develop a dosing control algorithm Predict flow rate, HRT, total dissolved sulphide (TDS), dosing rate! Identify a site where the control algorithm could be tested Bellambi rising main is over 9 km long with a HRT of 3 to 8 hours for average dry weather flow Existing ferrous chloride dosing at the Bellambi pumping station Accessible for monitoring! Apply the dosing control and verify the effectiveness of that control Monitoring before and after implementation of the doing control

7 Auto-regressive Model Step 1 Calculate Inflow from one year data Wet Well Volume (V) Measured Inflow (Q I ) Outflow (Q O ) Q I = Q O + ΔV 15 minutes time interval

8 Auto-regressive Model Step 2 Calculate typical flow as the average dry weather flow

9 Auto-regressive Model Step 3 Calculate constants a, b, c using auto-regression equation below: Q t I = μ t +a ( Q t 1 I μ t 1 )+b ( Q t 2 I μ t 2 )+c ( Q t 3 I Where μ t is the typical flow at time t. a= b= c=0.1126

10 Historical and Predicted Inflow

11 Modelling Assessment 20% chemical reduction TDS= α HRT e ( b 1 24 HRT)/ b 2 ) Dose Rate= β (TDS TDS Target ) Q I SRB activity rate

12 Monitoring Programs! Background monitoring undertaken in September 2011 Two sites: (1) wet well; (2) Wollongong WRP Total Dissolved Sulphides (TDS), ph and Temperature Grab samples to measure total and dissolved sulphide, total and dissolved iron! Monitoring repeated in September 2013 after the implementation of the online control dosing

13 Post Implementation Assessment Comparison of average dosing rate 25% chemical reduction

14 Post Implementation Assessment Statistic results Parameters No-dosing (2009) Profiled dosing (2011) Feed-forward dosing (2013) Sewage flow (L/sec) 292 ± ± ± 25 ph 7.4 ± ± ± 0.2 Average TDS (mgs/l) % TDS (mgs/l) Iron dosage (L/day)

15 Conclusions! The Feed Forward Ferrous Dosing Trial at Bellambi successfully demonstrated the benefits of feed forward control, using the autoregressive model for predicting the wastewater flow rate.! Predictive ferrous chloride dosing control not only improved the efficiency of the dosing, it also improved the effectiveness by being adaptive to actual flow rates. The result was a tighter control on TDS with reduced chemical usage.! It is recommended that feed-forward ferrous dosing be implemented at all locations where TDS control is achieved using iron salt dosing.

16 Acknowledgements The authors acknowledge the Sewer Corrosion and Odour Research (SCORe) Project LP funded by an Australian Research Council Industry Linkage Project Grant and by many key members of the Australian water industry and acknowledge our Research Partners on this Project. We further acknowledge the work of all those involved in the project at Bellambi, including the programmers, the sampling crews and the plant and lab personnel.