Case Studies Multivariate Statistical Analysis Finds the Bad Actors in Out-of-Spec Batches

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	Multivariate Statistical Analysis Finds the Bad Actors in Out-of-Spec Batches

Multivariate Statistical Analysis Finds the Bad Actors in Out-of-Spec Batches

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	Aspen ProMV was able to highlight the few process variables (from a total of around 80) that correlate closely to quality.
Impact #2	Aspen ProMV showed how the company’s batch operating procedures were affecting batch quality.
Impact #3	Aspen ProMV proved its ability to show which variables correlate the most with batch quality.

Impact #1

Aspen ProMV was able to highlight the few process variables (from a total of around 80) that correlate closely to quality.

Impact #2

Aspen ProMV showed how the company’s batch operating procedures were affecting batch quality.

Impact #3

Aspen ProMV proved its ability to show which variables correlate the most with batch quality.

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Overview

Multivariate Statistical Analysis Finds the Bad Actors in Out-of-Spec Batches

Operational Impact