Many industrial companies today are evaluating and testing advanced analytics solutions to gain insights into their real-time process data. These analytics solutions allow organizations to quickly discover and diagnose potential issues and help plant workers improve performance.
Total wanted a solution that made it easy for process engineers, field operators and maintenance personnel to gain actionable insights from their data to quickly diagnose abnormal conditions or potential problems, as well as monitor good behavior and make better decisions. The company launched an analytics pilot project last year at its Antwerp site to assess whether TrendMiner could fulfill these key requirements in practice. Within the pilot, real-time process data from the OSIsoft infrastructure was combined with TrendMiner’s self-service process and asset analytics software. The benefits demonstrated during the pilot were sufficient to justify a global roll-out of the solution to Total’s refining and chemicals plants.
Refining and Chemicals’ industrial plants have thousands of assets and millions of sensors, generating huge volumes of data for analysis. At a recent OSIsoft users conference, Fabrice Leclercq, Mechanical Engineer for Refining and Chemical segment of Total Group emphasized the critical need to analyze their data in real time.
Requirements for Analytics
Mr. Leclercq said that the technology they use must empower plant personnel to interpret the data themselves. Total wanted to reduce spreadsheets and improve its ability to make sense of day-to-day situations using the analytics software. The following capabilities were required:
The technology’s search capabilities were important, particularly the ability to access sensor and asset information quickly. Total looked at various types of analytics tools. The company categorized the tools into two categories: generic and operations-specific. Since generic tools require both IT development and data scientists or experts, they are not ready for end users “out of the box.” Operations-specific tools, in contrast, are designed to work right out of the box with operations-specific data. Operations-specific tools require only configuration by IT, rather than IT development.
The company also classified the tools by their analytics capabilities: descriptive, discovery, diagnostics, predictive, and prescriptive. In this context:
Discovery and Knowledge Sharing in Hazardous Production
By combining data and operational reporting from OSIsoft PI with the self-service industrial analytics and data visualization capabilities of TrendMiner, Total was able to meet its requirements. Using TrendMiner, process engineers and other operations and maintenance personnel can select process tags and search for specific behavior. Data is represented graphically, so users can match patterns, compare with similar events in the past, and find correlations quickly. They can compare transitions in batch activities and share the information with everyone who needs it.
Users within the Refining and Chemicals activities pilot found that with TrendMiner they could easily discover correlations in the data, share their findings within the organization, and capture it for future use by adding annotations.
Benefits
The pilot showed significant benefits in all four of Total’s industrial priorities: reducing safety risks, improving asset availability, reducing operational costs, and increasing energy efficiency. Additionally, the plug-and-play diagnostics, monitoring and predictive capabilities are expected to bring benefits in areas not yet explored.
Not only did the technology reduce the time users needed to solve and diagnose problems, it improved their ability to share and capture knowledge.
ARC Advisory Group clients can find additional information the related ARC Insight.
Total wanted a solution that made it easy for process engineers, field operators and maintenance personnel to gain actionable insights from their data to quickly diagnose abnormal conditions or potential problems, as well as monitor good behavior and make better decisions. The company launched an analytics pilot project last year at its Antwerp site to assess whether TrendMiner could fulfill these key requirements in practice. Within the pilot, real-time process data from the OSIsoft infrastructure was combined with TrendMiner’s self-service process and asset analytics software. The benefits demonstrated during the pilot were sufficient to justify a global roll-out of the solution to Total’s refining and chemicals plants.
Refining and Chemicals’ industrial plants have thousands of assets and millions of sensors, generating huge volumes of data for analysis. At a recent OSIsoft users conference, Fabrice Leclercq, Mechanical Engineer for Refining and Chemical segment of Total Group emphasized the critical need to analyze their data in real time.
Requirements for Analytics
Mr. Leclercq said that the technology they use must empower plant personnel to interpret the data themselves. Total wanted to reduce spreadsheets and improve its ability to make sense of day-to-day situations using the analytics software. The following capabilities were required:
- Pattern-based search and discovery
- Ability to diagnose process behavior and anomalies quickly
- Monitoring live process and asset performance
- Prediction based on historical information
- Ability to analyze data in real time to enable faster decisions
The technology’s search capabilities were important, particularly the ability to access sensor and asset information quickly. Total looked at various types of analytics tools. The company categorized the tools into two categories: generic and operations-specific. Since generic tools require both IT development and data scientists or experts, they are not ready for end users “out of the box.” Operations-specific tools, in contrast, are designed to work right out of the box with operations-specific data. Operations-specific tools require only configuration by IT, rather than IT development.
The company also classified the tools by their analytics capabilities: descriptive, discovery, diagnostics, predictive, and prescriptive. In this context:
- Describe illustrates what happened based on historical data
- Discover enables the ability to search historical data to determine what happened in the past
- Predict tells what will happen in the future based on historical data
- Prescribe gives the user a recommendation on actions they could take in the future.
Discovery and Knowledge Sharing in Hazardous Production
By combining data and operational reporting from OSIsoft PI with the self-service industrial analytics and data visualization capabilities of TrendMiner, Total was able to meet its requirements. Using TrendMiner, process engineers and other operations and maintenance personnel can select process tags and search for specific behavior. Data is represented graphically, so users can match patterns, compare with similar events in the past, and find correlations quickly. They can compare transitions in batch activities and share the information with everyone who needs it.
Users within the Refining and Chemicals activities pilot found that with TrendMiner they could easily discover correlations in the data, share their findings within the organization, and capture it for future use by adding annotations.
Benefits
The pilot showed significant benefits in all four of Total’s industrial priorities: reducing safety risks, improving asset availability, reducing operational costs, and increasing energy efficiency. Additionally, the plug-and-play diagnostics, monitoring and predictive capabilities are expected to bring benefits in areas not yet explored.
Not only did the technology reduce the time users needed to solve and diagnose problems, it improved their ability to share and capture knowledge.
ARC Advisory Group clients can find additional information the related ARC Insight.