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Situation
At this plant, the waste gas treatment cleans incoming waste gas from a variety of different production processes. If the amount of waste gas is too high for the capacity of the plant (e.g. a spike of waste gas), it could lead to the emission of waste gas to the environment.
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Problem
If a waste gas spike occurs, the shift personnel needs to find the origin of the waste gas quickly and initiate countermeasures to avoid emission of waste gas. The shift usually goes through the known candidates one-by-one to check, which is time consuming and often unsuccessful.
Solution
The engineers wanted to establish an efficient and effective workflow to quickly find the origin of the emission of waste gas through use of the time-series data.
Challenges
There are dozens of possible candidates in one production facility alone, making it extremely difficult to identify the right one. If the origin is not a “usual” candidate, it will most likely be missed.
Approach
- Used TrendMiner’s Recommendation Engine to look through all waste gas valves of every production process.
- Visualized the spike of waste gas and ran the Recommendation Engine with a filter expression.
- Added the results to the view for analysis.
Results
Through use of self-service analytics, the engineers established a very fast and easy way to get the right root-cause candidate for the emission of waste gas. This results in:
- Reduced environmental impact through emission of waste gas
- Safety improvements
- Contribution to net-zero operations targets
Situation
At this plant, the waste gas treatment cleans incoming waste gas from a variety of different production processes. If the amount of waste gas is too high for the capacity of the plant (e.g. a spike of waste gas), it could lead to the emission of waste gas to the environment.

Problem
If a waste gas spike occurs, the shift personnel needs to find the origin of the waste gas quickly and initiate countermeasures to avoid emission of waste gas. The shift usually goes through the known candidates one-by-one to check, which is time consuming and often unsuccessful.
Solution
The engineers wanted to establish an efficient and effective workflow to quickly find the origin of the emission of waste gas through use of the time-series data.
Challenges
There are dozens of possible candidates in one production facility alone, making it extremely difficult to identify the right one. If the origin is not a “usual” candidate, it will most likely be missed.
Approach
- Used TrendMiner’s Recommendation Engine to look through all waste gas valves of every production process.
- Visualized the spike of waste gas and ran the Recommendation Engine with a filter expression.
- Added the results to the view for analysis.
Results
Through use of self-service analytics, the engineers established a very fast and easy way to get the right root-cause candidate for the emission of waste gas. This results in:
- Reduced environmental impact through emission of waste gas
- Safety improvements
- Contribution to net-zero operations targets
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