Multivariate Statistical Analysis Finds the Bad Actors in Light Component Losses
Company Size
1,000+
Region
- America
Product
- Aspen ProMV
Tech Stack
- Data Analysis
- Process Modeling
Implementation Scale
- Pilot projects
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Process Analytics
Applicable Industries
- Chemicals
Applicable Functions
- Process Manufacturing
Use Cases
- Predictive Maintenance
- Process Control & Optimization
Services
- Data Science Services
About The Customer
The customer is a large petrochemical company from Latin America, producing products like ethylene and propene, mostly from naphtha. These compounds are key products in the manufacturing of thermoplastic resins. The company has a successful history of innovation and advanced technologies use, including simulation for conceptual design, revamps and process monitoring, as well as several profitable advanced process control implementations and real-time optimization. Currently, the company is evaluating potential solutions to support operation decisions based on advanced analytics technologies. Among these solutions there is a specific initiative on process analytics to make profitable use of the rich historical data information for process knowledge improvement, troubleshooting, optimization and process monitoring. This effort could also complement their current use of technologies like simulation using Aspen Plus.
The Challenge
The petrochemical company was facing significant losses due to light component losses that go to the bottom of a fractionation column and pressurize the downstream column. This pressure resulted in downstream column production being lost to the flare. This stream contained a very valuable product, and the loss represented more than $1M USD. The company was looking for a solution to understand and resolve this production problem faster to limit the losses.
The Solution
The company launched an Aspen ProMV pilot project to investigate the light component losses. Using Aspen ProMV for continuous processes, a model was developed, and bad actors that are highly correlated to the light product loss were identified. Aspen ProMV’s optimization tool was also utilized to provide better operating conditions to reduce losses. The software was able to highlight a few process variables that are highly correlated with the light product losses and optimize the fractionation column operating conditions to limit the losses. The software was able to demonstrate that by changing the operation conditions, the column product losses can be reduced.
Operational Impact
Quantitative Benefit
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