Total Refining & Chemicals operations transform crude oil and natural gas into finished products or intermediates, which are then used to manufacture chemicals. Like many companies with global operations, Total evaluates, tests and deploys advanced analytics solutions to gain more insights into its real-time process data. These analytic solutions allow the company to quickly discover and diagnose potential issues and help plant workers improve performance.
Total is also a major player in solar energy with SunPower and Total Solar. The company prides itself on being “the responsible energy major,” which it interprets as meaning maintaining high health, safety and environment standards, meeting the International Energy Agency’s ambitious target for minimizing global warming, promoting responsible energy use by customers, and being recognized for its local services. According to the company, its 98,000 employees are committed to better energy that is safer, cleaner, more efficient, more innovative and accessible to as many people as possible. As a responsible corporate citizen, the company focuses on ensuring that its operations in more than 130 countries worldwide consistently deliver economic, social and environmental benefits.
To contribute to achieving these goals, Total’s Refining and Chemicals segment developed a digital roadmap toward becoming an even more efficient, data-driven organization. The priorities for this roadmap are: safety, availability, cost control and energy efficiency.
Refineries and chemical industrial plants have thousands of assets and millions of sensors, so they generate huge volumes of data for analysis. This emphasizes the critical need to analyze data in real time and deploy technology that empowers plant personnel to interpret the data themselves.
Total wanted technologies that made it easy for subject matter experts such as process engineers and plant operations and maintenance personnel to gain actionable insights from their data and quickly diagnose abnormal conditions or potential problems. The company launched a pilot project in 2017 at its Antwerp, Belgium, site using TrendMiner advanced analytics. The benefits demonstrated during that pilot were sufficient to justify a global rollout of the software to Total’s refining and chemical plants.
Pilot program objectives
Total decided to pilot an analytics software tool to help garner intelligence from its data so employees could quickly determine the cause of process behavior or events. Whenever a problem or abnormal situation occurs in the plant, operators and process engineers are often asked questions such as, “Has this happened before?” and “Which conditions caused the event?” While common, these types of questions can be difficult to answer and often require extensive, time-consuming data investigations.
Requirements for analytics
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
- Search capabilities, particularly the ability to access sensor and asset information quickly
To select its analytics software, Total looked at various types of analytics tools. The company categorized the tools into two categories: generic and operations-specific. Since generic tools require 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 IT configuration rather than IT development.
The company also classified the tools by its 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.
The company also required software tools that could combine historical and current data to determine the what, why and how of any issues or events, and that subject matter experts with no special analytics background could use directly on operations-specific data without IT development.
Discovery and knowledge sharing in hazardous production
By combining data and operational reporting from OSIsoft PI with self-service industrial analytics and data visualization, Total met its requirements. 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 they could easily discover correlations in the data, share their findings within the organization and capture it for future use by adding annotations.
Operating within constraints
“It is important to realize that we are not playing with water — we are working with hazardous chemicals,” said Fabric Leclercq, mechanical engineer at Total. Machinery and other asset limits must be integrated into the data because the limits or constraints cannot be exceeded. Normal operations were based on historical data rather than the information in their specifications. Pattern-based fingerprinting enables users to select one or more process tags and search the data for similar performance to determine normal or optimal operating conditions. They can then compare the fingerprinted patterns to live data and benchmark performance within the operating window.
Self-service industrial analytics
Total’s technology evaluation included a functional assessment and survey of pilot users. The Refining and Chemicals team determined that the OSIsoft and TrendMiner technologies are complementary, with little functional overlap. With approximately 45 users involved in the pilot, it was important that everyone could use the technology, giving the power of the data to the people who must interpret it — the operators, plant engineers and other plant workers — not just data scientists.
Total’s assessment gave high scores to the pilot’s self-service capabilities, user friendliness and the solution’s value potential.
According to Leclercq, the software can be managed centrally. As a plug-and-play technology, it is compatible with the IT security and landscape. The solution enables easy accessibility of the data and intelligence, comes fully programmed and can be configured with agility. It enables users to look at their equipment, troubleshoot and account for each asset separately. Leclercq believes they can do more with their data and improve productivity. Next steps include looking at assets as fleets with standard calculations and key performance indicators and using PI Asset Framework to manage them.
The Total pilot users described the key benefits of the solution as:
- Time gain — By obtaining intelligence directly from the system, they would not have to view multiple time-consuming spreadsheets to diagnose problems.
- Testing hypotheses — The tool allows users to test potential outcomes of different scenarios.
- Diagnose problems better — Users identified their ability to determine the cause of problems faster, send an alert or alarm to the user and impact potential problems to avoid abnormal behavior.
Based on the success of the pilot, the technology will be rolled out to Total Refining and Chemicals plants around the world throughout 2018 to empower all Total subject matter experts to interpret their own data and make faster decisions.
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 unexplored areas.
Not only did the technology reduce the time users needed to solve and diagnose problems, but it also improved their ability to share and capture knowledge.
Edwin van Dijk is vice president of marketing for TrendMiner NV. He has more than 20 years of experience in bringing software solutions for the process and power industry to market. Various roles from business consultancy to product management gave van Dijk the experience to expand TrendMiner’s footprint in the market.
Janice Abel is principal consultant for ARC Advisory Group’s regulated industry group. She performs research and provides consulting services for ARC’s clients in the process and discrete industries, and supply chain integrity and brand protection. Abel has more than 25 years of experience in the process industries and holds a master’s degree in chemical engineering and an MBA.
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