Abstract
There is increasing demand on operators of small scale wastewater treatment plants (WWTPs) to
improve biological nutrient removal and energy efficiency. Automated monitoring and automation
of wastewater treatment plants can provide the necessary tools to improve plant performance.
However, online sensors for key parameters such as ammonium can require excessive
maintenance, are unreliable unless frequently maintained and are often not affordable. In addition,
control techniques such as machine learning are not currently financially or technically compatible
within small scale treatment facilities. This study presents a methodology to use low cost, reliable
surrogate sensors in association with inexpensive and robust programmable logistic controllers to
improve performance and energy efficiency of batch type WWTPs (e.g. sequencing batch
reactors). The paper presents simplified methodologies for control of batch WWTPs using pH and
oxidation reduction potential (ORP) trends. A purpose built SBR WWTP with a fixed time
treatment cycle was monitored for this study. Applying these methodologies to the gathered data
returned average reductions in cycle time of 50.9% and 50.3% for pH and ORP trends
respectively. Corresponding average energy reduction was 39.0% for both trends. Ammonium
removal was 74.2% and 78.4% of total removal in the fixed time cycle for pH and ORP
respectively. The developed control methodologies have significant potential to enhance the
performance of small scale and decentralised WWTPs.
| Original language | English (Ireland) |
|---|---|
| Title of host publication | 7th International Water Association (IWA) Young Water Professionals Conference |
| Place of Publication | Belgrade, Serbia |
| Publication status | Published - 1 Jan 2015 |
Authors (Note for portal: view the doc link for the full list of authors)
- Authors
- Fox, S., Clifford, E.
