Testing hypotheses and quick data analyses
Use case
Challenge
In the Chemicals industry, maintaining the right level of Chlorine gas in production processes is important as too much chlorine gas exposure poses significant safety and operational challenges. Affected areas become difficult to access, requiring gas masks and strict adherence to health and safety regulations. Addressing this issue independently would involve extensive hypothesis testing by process engineers or shutting down production to test the equipment, both of which are costly and time-consuming.
Solution
The team at this chemical plant utilized Intelecy’s data analysis tool to very quickly test their various hypotheses. The engineering team identified 20 potential process hypotheses and mechanical issues. By leveraging Intelecy’s Data Explorer, they were able to efficiently analyze and eliminate false hypotheses, pinpointing the equipment as the root cause of the chlorine gas leaks.
Result
- Eliminated irrelevant hypotheses related to the production process
- Significant cost savings in time and resources
- Avoided production downtime for equipment testing
- Resolved the issue, normalizing chlorine gas levels
Other use cases
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Avoid accidents and unplanned downtime with data-driven predictive maintenance. By creating and using no-code anomaly detection machine learning models, you can receive early warnings and act when needed.
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High quality of end products is crucial. At the same time, process engineers want to prevent unwanted incidents in production that lead to raw materials being lost as waste.