By Cordell Samuels, Toronto Works Dept.
The activated sludge and sludge thickening processes at Toronto's main treatment plant have been greatly improved by a sludge retention time control system. This is a 818,000 m3/day (180 MGD) secondary facility that consistently meets discharge limits on BOD, TSS and phosphate.
Despite excellent water quality produced by an activated sludge system, the plant experienced high variability of MLSS concentration, SRT, and mass of wasted sludge. High variability of the SRT reduces the ability of the activated sludge system to withstand the stress caused by changes in flow, temperature, or toxic spills. Significant changes in MLSS and in mass of wasted sludge cause overloading of the sludge thickening DAF units and the necessity to bypass them. In order to overcome these challenges, an SRT control system was installed at Toronto Main at the end of the summer of 1999.
SRT Control System
The automatic SRT control system manufactured by Royce Instrument Corporation consists of two suspended solids meters and a waste flow controller. (Figure 1). Royce is represented in Canada by Cancoppas Ltd.
The control algorithm employed takes into account the dynamic characteristics of the individual activated sludge system. One of the suspended solids meters was installed in the mixed liquor channel; another TSS meter was installed in the sampling sink to measure suspended solids concentration of RAS that is pumped there.
Operation of SRT Control System
The suspended solids meters were initially calibrated and then checked daily with a portable TSS meter. If the difference between the portable and permanent TSS readings exceeded 5%, the permanent TSS meters were recalibrated. Laboratory data was used only once per week to calibrate the portable TSS meter. During the first part of October, 1999, the SRT control system was utilized in monitoring rather than control mode; during this period, plant operators controlled the waste flow using conventional strategies. Starting in the second part of October, the waste flow was controlled by the SRT control system. Initially, the SRT target was 1.2 days, since it was equal to operational data at the time. It was recommended, though, to slowly raise the SRT target to reduce observed earlier dispersed growth, commonly associated with SRT below two days. By the end of November the SRT target was 2.2 days.
Results
The number of required laboratory TSS tests was reduced by more than 80%. Suspended solids meters provided accurate data with reliability. The meters did not require maintenance and rarely needed recalibration. The treatment process was improved considerably. Data presented covers 16 days of manual control (September 28 through October 12, 1999) and 16 days of automatic control (December 11-26). The data was received from the SRT control system via modem. As can be seen from Table 1 and Figure 2, stability of the waste stream from treatment train #10 has significantly improved.
Variation of waste (RASSS) and MLSS concentrations was reduced by almost a factor of three, while the variation of mass waste stream was reduced by a factor of seven.
Significant reduction of variation in the waste stream mass improved the operation of the DAF facility, and the earlier practice of bypassing the DAF facility became unnecessary. Significant (up to 50%) reduction of polymer usage was also observed. A reduction of variability of MLSS (Figure 3) and an increase of its value improves the capability of the activated sludge system to withstand any unexpected stress. An increase of MLSS did not cause a noticeable increase in sludge blanket depth.
The control system was very robust; during the testing period, there were no occasions when a faulty control signal was generated by the controller. On several occasions, RAS flow to the sampling sink was interrupted, and as a result, the RASSS meters produced invalid readings. The control system automatically detected problems with RASSS readings and not only initiated alarms but also changed the control algorithm to disregard the faulty readings. Once conditions returned to normal, the controller automatically changed the control algorithm back to the one used prior to the problems encountered.
Conclusions
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