Multivariate Statistical Analysis Finds the Bad Actors in Out-of-Spec Batches
Product
- Aspen ProMV
Tech Stack
- Machine Learning
- Data Analysis
Implementation Scale
- Pilot projects
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Chemicals
Applicable Functions
- Discrete Manufacturing
- Quality Assurance
Use Cases
- Predictive Quality Analytics
- Process Control & Optimization
Services
- Data Science Services
About The Customer
The customer in this case study is a large producer of synthetic rubber. The company has been facing quality issues with its batch products, leading to significant revenue loss. The company often had to either reprocess the material or sell it at a lower price than expected due to these quality issues. The company was unable to determine the cause of these out-of-spec batches. The company has a long-standing relationship with AspenTech and uses a number of products from the aspenONE® Manufacturing and Supply Chain and Engineering suites.
The Challenge
A large producer of synthetic rubber had been having quality issues with its batch products. These quality issues were resulting in significant revenue loss, as the company often needed to either reprocess the material or sell it for a lower price than expected. The producer was unable to determine what was causing the batches to be out of spec. The company was investigating issues with a reactor process that brings together ingredients to manufacture synthetic rubber. There were multiple reactors that performed this process, but the Aspen ProMV project would focus on the production of one reactor.
The Solution
Aspen ProMV desktop batch model was developed to identify the bad actors in the off-spec batches. The customer provided five months of production data, representing 55 batches produced from this one reactor. Input variables included initial temperature, amount of catalyst and amount of other raw materials for each batch. Since this was a batch process, there was batch profile data (e.g., temperature, pressure, level, reactor agitator speeds, etc.) from the batch run. Quality variables measured at the end of each batch were also provided. There were three key quality variables that customer wanted to keep in control. The analysis was performed using Aspen ProMV desktop for batch. Aspen ProMV found several variables with very low variations and excluded them from the model.
Operational Impact
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