Power generation is geared towards maximum cost effectiveness; power stations compete for the right to supply the transmission network based on dollars/electricity unit delivered. In this regard, Scantech believes in working smarter, not harder, to get the most productive, innovative and efficient outcomes possible for our clients. While most of the focus has been on the reduction of CO2 emissions, the reduction of other emissions, such as sulphur dioxide and nitrogen oxide, has put further pressure on coal-fired power stations to monitor their fuel quality.
The major cost components in the power station are:
- purchase costs of fuel
- fuel handling and storage
- efficient combustion of fuel
- scheduled maintenance
- unscheduled failures, or plant outages
- disposal of waste products, including treatment of wastes to permit disposal.
Power stations operate in a variety of modes, ranging from “mine mouth” stations, which receive coal from a single nearby source, to remote stations located at load centres, which receive coal from multiple sources. These operating modes also influence stockpile management; a power station close to the mine may receive coal by truck or by overland conveyor and thereby hold minimal stocks, the “just-in-time” approach, whereas a power station reliant on long rail hauls or sea freight to obtain coal may hold larger “live” and “long term” stockpiles. The “just-in-time” operation has little opportunity to blend coals to even out quality variations, whereas the “remote” station can use its stockpiles to advantage.
On-line analysers can monitor the quality of coal feed to bunkers and waste products, allowing power station operators and management to optimise the performance of the station by making decisions based on knowledge in advance, rather than reacting to changed conditions as they arise.
"Accurate online measurement information and diagnostics show how the machines and the plant are working; a single piece of data can make or break the process"
Kari Heikkilä, Metso, Minerals Processing and Aggregate Production, 2013