"Data analytic software from Seeq and other companies proposes to address the 'drowning in data but starving for information' issue by empowering engineers and process experts to interact directly with data to enable insights. These software offerings don't require programming expertise for use, and thus connect those who understand the process directly with the data."
"As discussed in detail in a previous ARC Insight, Shawn Anderson, Senior Research Specialist for Fisher Valves, a division of Emerson Process Management, gave a presentation on using IIoT to help end users reduce valve-related unplanned downtime...It soon became apparent that IIoT technologies were a natural fit for a remote mon-itoring service to help optimize customers’ valve maintenance practices and uptime via predictive or prescriptive maintenance services for critical assets."
"Recognizing that its internal data visualization tools were not designed as an effective plantwide solution, Emerson partnered with software company, Seeq, to improve the data visualization tools used to predict future valve alarms. Seeq used its expertise in accelerating insight into time-series data to help Fisher Valve Division build a collaborative environment connecting customers with local Fisher services experts and global valve experts. This environment enables data from multiple sources to be visualized and aggregated and allows people located around the world to look and work on the same data in a collaborative manner."
"By investigating and organizing the data using Seeq and then exporting the results to Excel, Avangrid Renewables was able to add price information and determine the cost of the curtailments to the company. It estimates that the technology can document revenue opportunities between $30,000 to as much as $100,000 per year depending upon the ISO contract, wind curtailment, and wind availability."
"For example, an engineer using the browser-based process analytics Workbench from Seeq can manipulate and compare time-series data from local databases and process databases—such as those from OSIsoft, Emerson, Honeywell, Wonderware and others—and now incorporate data from IT's Hadoop distributed file system or data from Microsoft Azure or other cloud service."
ABI identifies 60 young and reinvented companies set to transform the technology marketplace.
At this critical juncture for the technology industry, ABI Research, the leader in transformative technology innovation market intelligence, identifies 60 innovative young and reinvented companies in its annual top technology innovators report that are set to transform the technology marketplace. Rather than focus on the mega companies driving core markets, this research focuses upon those smaller—harder to see—young and reinvented companies that are enabling real, sustainable change from the margins of industry.
“While it is easy to spot the tech companies that cause disruption, the true transformative trends and companies driving those changes are harder to see,” says Stuart Carlaw, Managing Partner and Chief Research Officer at ABI Research. “Our analysts assessed a great deal of companies for this report, many of which we consider to be disruptive market innovators. However, some of the groundbreaking companies will not see the fruits of their labor and will ultimately disappear as the market moves toward more sustainable and transformative trends.”
Those companies featured in the research are aligned around a number of core areas where technology transformation is ripe:
- The path to 5G and the future network
- IoT and its digitization of the physical world
- The vehicle of the future
- The revolution in urban management
- Mass transit for the future connected megacity
- Security and privacy in the hyper connected world of the future
- Augmented reality and virtual reality enhancing the enterprise workflow and consumer experience
- The human machine interface
- Next generation video experiences
- Robotics, machine learning, artificial intelligence, and machine vision
- Digitizing the human domain with wearables, hearables, usables, and expendables
- Digital energy and powering the connected world
“ABI Research analysts identified the young and agile companies that should be on the radar of all market observers,” concludes Carlaw. “They should be considered pioneers, market makers, threats, disruptors, acquisition targets and transformation agents. Fundamentally, they represent some of the brightest value creators operating today.”
For more information, download ABI Research’s complimentary Hot Tech Innovators whitepaper.
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About ABI Research
For more than 25 years, ABI Research has stood at the forefront of technology market intelligence, partnering with innovative business leaders to implement informed, transformative technology decisions. The company employs a global team of senior analysts to provide comprehensive research and consulting services through deep quantitative forecasts, qualitative analyses and teardown services. An industry pioneer, ABI Research is proactive in its approach, frequently uncovering ground-breaking business cycles ahead of the curve and publishing research 18 to 36 months in advance of other organizations. In all, the company covers more than 60 services, spanning 11 technology sectors. For more information, visit www.abiresearch.com.
A new generation of analytics software simplifies extraction of insights and value from data historians. Like many process plants, pharmaceutical and biopharmaceutical facilities are awash in process and manufacturing data, but often struggle to extract value and insights from this information. The challenge of deriving process industry data insights stands in stark contrast to the commercial sector where innovative business intelligence software products are used to extract value from relational databases.
"Perhaps the better way to look at machine learning is to consider the computer and market angles separately. "There is a computer-science and a market answer to what machine learning is," comments Seeq’s Risse. "The computer-science answer is machine learning uses automated and iterative algorithms to learn patterns in data, so you don’t program the endpoint solution at the outset. Instead the algorithm adjusts itself—by learning from one data point to the next—to solve a particular problem as part of the process, using either a supervised, training-set or unsupervised starting point.
The market answer is that machine learning is on the cusp of joining big data and the IoT as a marketing necessity for modern software offerings, such that the technical definition or correctness of any particular solution is lost in the hype, continues Risse. "And that is just within machine-learning offerings,” he explains. “There are many other computer-aided insight tools vying for attention at the same time: deep learning, machine intelligence, artificial intelligence. The answer is getting more marketing-focused over time, given the competition within and across the ecosystem."
As published in the AFPM 2016 Show Daily (Day 3)
Refineries often find themselves data rich and information poor, with millions of data points stored in historians and no easy way to extract value from this information (FIG. 1: Traditional data analytics tools such as spreadsheets are general purpose and not specifically designed for extracting value from historian data.). This situation is changing; Seeq and other companies have recently introduced data analytics software specifically designed to enable insights and extract value from the time-series data stored in historians such as OSIsoft PI, Honeywell PHD and InfoPlus 21. There are several examples of how data analytics solutions are being applied to address specific refinery issues.
Better energy management delivers bottom line benefits. In a refinery, energy accounts for a significant share of operating costs, so even small gains in efficiency go straight to the bottom line. However, most of the low-hanging fruit has been picked, leaving the more difficult and challenging projects that can achieve significant energy savings.
AFPM 2016 Show Daily Refinery energy inefficiency sources include waste heat, steam systems and furnaces. Other sources of inefficiency are found in atmospheric distillation, vacuum distillation, delayed coker, hydrotreater, hydrocracker, catalytic reformer and alkylation units. Each operating unit produces huge volumes of data that is usually stored in information systems owned and managed by the operating disciplines. In many cases, this information goes unused.
A leading refiner is addressing this issue by deploying Seeq software to eliminate the excess effort required to visualize time-series data and align it with other contextual information sources. The refinery engineers can now search for historical patterns and uncover inefficiency signatures, process problems and potential equipment failure. More than 100 opportunities to reduce and suppress energy loss have been identified and addressed. What used to take days or weeks of analysis can now be accomplished in minutes or hours.
Increasing profits by using lower cost feedstock. A major refiner in the Midwest was looking for ways to increase profitability, and found they needed to make quicker decisions to exploit changing market conditions. Despite having what they thought were best-of-breed tools, decisions were taking too long and market opportunities were being missed.
Midwest refineries have typically been designed to accept lighter crude feedstock. Heavier feedstock occasionally trades at a discount, creating an opportunity for refiners to improve margins by sourcing this lower-priced supply. However, the information systems within many refineries make it difficult to make timely decisions about expected yields when accepting off-spec feedstock.
To make an informed decision, engineers need to quickly answer the following questions:
- How far off-spec are my column products?
- How much does this deviation cost? How do the costs compare to the profit opportunity?
- What changes need to be made to process the feedstock?
- Is there a set of operation specifications to process the crude?
To answer these questions, data was imputed into modeling tools, with results transferred into complex home-grown spreadsheets for analysis. With this traditional approach, missing data was a common occurrence, requiring the engineer to use a best-guess approach to fill in the gaps, or spend additional time to gather historical data from another point in time to rerun the model.
The refinery’s engineers can now use Seeq to perform advanced analytics and extract actionable insights from their data in a timely manner (FIG. 2: Data analytics software specifically designed to interact with the time-series information found in historians allows process engineers and other specialists to quickly analyze and compare temperature and other process parameters.). Using context-based search capabilities similar to modern web search engines like Google, internal operating properties such as flow data, column temperature and pressure, heat exchanger inlet and outlet temperatures, pressure drops and boiling point curves are quickly gathered over a specified time period. AFPM 2016 Show Daily Figure 2 Data is automatically checked for inconsistent and missing information and quickly reconciled if necessary. Unit of measure consistency is also ensured, eliminating the need for spreadsheet transformations. Search results are fed directly into the refinery model to analyze historical operating states for crude feedstock changes under current operating conditions, and cost-benefit scenarios are stored for each time period. Results can be recalled and visually compared against each other, providing an overview of the process through time.
Predictive maintenance cuts costs and increases uptime. The reliability team at another refinery was seeking to avoid unplanned equipment outages. Maintenance of equipment was required periodically, but performing these tasks on a calendar basis was inefficient. Some items were serviced too frequently, adding to downtime and costs, while others weren’t serviced often enough, negatively impacting quality. Refinery maintenance management system information was frequently out of date.
Applying Seeq to data already resident in a historian, plant maintenance personnel now use pattern-based anomaly detection techniques to identify the actual state of equipment or a process. This identifies precursor events to unscheduled equipment outages and anticipates emerging process problems.
With real-time trending in place, equipment performance is monitored continuously, preemptive measures are taken when maintenance is required, and maintenance intervals are stretched out wherever possible.
The data analysis required to predict problems before they occur is quite complex, as it requires looking at real-time and historical data in context with work orders, notifications, incidents, operator logs, alarms and events, corrosion, inspection and equipment data to build a picture of conditions that cause failure.
Process and reliability engineers most familiar with the equipment and the operating units perform this complex analysis using Seeq, creating data signatures by using information from sensors monitoring equipment known to be in good running order, tracking deviations from these signatures, and automatically alerting plant personnel before problems occur.
The refinery’s engineers now have affordable data analysis tools to prevent unplanned capacity losses.
Breaking away from traditional data analytics tools. Refineries often possess all the information they need to improve operations within their data historians, but creating insight from this information can be difficult, expensive and time-consuming using traditional data analytics tools.
Seeq now offers data analytics software specifically designed to interact with the time-series information found in historians. Because this software is designed for just one task, as opposed to a general purpose tool like a spreadsheet, it provides a faster and less expensive solution.
"Realizing the benefits of the IIoT requires a fresh look at software analytics offerings. The goal is to find a product providing a complete and agile approach to extracting insights from production data. We believe that Seeq's data analysis software, and possibly competing products down the road, will give process experts first hand insights to their data, enabling them to customize analysis and improve production outcomes."