Water Treatment Plants (WTPs) supply potable water which is transferred by pumps to various end users. WTPs and other sub-systems are energy intensive with pump installed capacities varying between 75 kW – 6 000 kW. It has therefore become important to optimise the utilisation of WTPs. Cost savings can be achieved and the load on the national grid can be reduced. The aim of this study is to develop and implement load management strategies on a municipal WTP.
In this investigation the high lift pumps are deemed to be the largest consumers of electricity. Strategies to safely implement load management on a WTP were researched. By optimising the operations of the pumps, significant cost savings can be achieved. Comparisons between different electricity tariff structures were done. It was found plausible to save R 990 000 annually, on a pumping station with four 1 000 kW pumps installed, when switching to a time-of-use dependent tariff structure.
Strategies to optimise plant utilisation while attempting a load management study include the optimisation of filter washing methods and raw water operations. An increase of 34% in efficiency for a filter backwash cycle was achieved. To accommodate the effects of the load management on the WTP, the operation of valves that allow water to distribute within the plant was also optimised.
The implemented control strategies aimed to accomplish the full utilisation of the WTP and sub-systems to achieve savings. An average evening peak period load shift impact of 2.21 MW was achieved. Due to filter modifications the plant is able to supply 5% more water daily. A conclusion is drawn regarding the success of the strategies implemented. Recommendations are made for further research. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nwu/oai:dspace.nwu.ac.za:10394/15212 |
| Date | January 2015 |
| Creators | Els, Lötter Adriaan |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0018 seconds