Manufacturing Successes
         Process Diagnosis in Chemical Manufacturing
 		  
Mission:
To determine the cause of intermittent process upsets.

Challenge:
Scale-up of a new process was plagued with occasional severe failures.

Solution:
 iUnderstand diagnosis of process failures.

"Money Quote"
"I am a PhD Chemist that has been working in this field for 30 years, and you walk in and within 2 weeks you know what it took me 30 years to learn"

 
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Objective
A large chemical manufacturer was in a bad situation.  Their pilot facility was evaluating the feasibility of a whole new way to make nylon, before a $200 million expansion.   A tricky little mysterious quality problem stood in their way:  Every so often, the product failed,... miserably, completely.  These events were blocking the customer from moving forward into full production.  Their best chemists, engineers, statisticians and lab researchers slaved to save the project, their multi-million dollar pilot investment and the large capital roll-out, but without success.  Our mission was to determine the cause of these events.

The Methods
BioComp engineers came on site and teamed up with the Customer's process experts.  A box was drawn around the portion of the process where the problem was occurring.  Flows of materials, process control points and information in and out of this box were identified, much like a mass and energy balance.  Data was collected on these attributes from the local process and quality management systems.  This data was modeled using BioComp's iUnderstand analysis tool to find the key drivers of "Problem" vs. "No Problem".

Results
The iUnderstand analysis quickly pointed towards the catalyst composition as containing the cause, quickly eliminating all other process flows, operating conditions and other materials streams.  Further modeling was performed, specifically targeting the components (ingredients) of the catalyst.  Key "contaminants" (catalyst decomposition products) were identified as the likely cause.  Specific concentrations were calculated that would likely cause the problem.  A process change recommendation was made and the problem was eliminated.

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