Unlock Efficiency with Soft Sensors.
OPOP leverages Machine Learning to optimize your operations and reduce costs.​
Application examples
1
Desulfurization process
Cost savings through resource efficiency
In a desulfurization plant at an oil refinery, the sulfur content of the final product was previously only analyzed weekly in the laboratory. Therefore, the process was operated conservatively, resulting in higher severity
than necessary. With OPOP, the sulfur content can now be continuously predicted in real time which enables more efficient (less conservative) operation of the plant. This reduces hydrogen consumption by around 2% and also increases catalyst life time due to lower severity.
2
Furnace Outlet Temperature
Greater availability through digital flexibility
A trip of the furnace outlet temperature sensor limited plant operation and flexibility. By using the OPOP software tool, a soft sensor based on historical data was built 30 minutes. This allowed for precise temperature estimates, allowing plant operation to remain flexibly controllable, and avoiding downtimes or giveaways.
3
Flue gas analysis
Energy savings through digital redundancy
If the Oâ‚‚ analyzer of a fired heater fails, operators increased the combustion air ratio as a precautionary measure – resulting in up to 10% higher fuel consumption during downtime. By using OPOP software, a soft sensor was built, which replaced the Oâ‚‚ analyzer digitally. This ensures optimal combustion control even during maintenance phases – more efficient, safer, and more sustainable.
Download our White Paper now and learn more about data-based process optimization using Soft Sensors.
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