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  1. For many years, finding insights in data using analytics—and data collection and storage strategy—have been considered a package deal. This is because operations data is often siloed, has limited analytics options, and is in difficult-to-reach places. As a result, some companies embark on huge data strategy and migration efforts, often lasting months or even years. They believe data must be centralized to begin extracting value with advanced analytics technologies. And in the meantime, companies miss out on valuable insights that can optimize assets and processes, minimize waste, and prevent incidents. View the full article
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    R52 Tables

    R52 Tables brittanyebaugh Fri, 07/09/2021 - 06:42 View the full article
  3. Data-Driven Decisions in T&D katie@uncommon… Wed, 06/30/2021 - 12:51 Industry Power Generation Challenge: In the electric utilities industry, accessing existing data is difficult due to the required data often being distributed across data sources. The data is often incredibly dense, both in frequency and volume, with thousands of assets. As a result, data is often used only for reactive troubleshooting rather than proactive decision making around capital expenditures and the proper operation of assets.​ Solution:​ Seeq allows the end-user or subject matter expert (SME) to unify data access, allowing for a live connection to data across many different databases from a single application interface. The subject matter expert can interact directly with the required data, without having to go through extra steps to ETL and align the data, to perform calculations or condition-based analytics. These analytics are then scalable across assets, leveraging an existing, or built-in-Seeq, Asset Tree and the Asset Swap functionalities and Tree Map view. ​ Results:​ SMEs have a reduced barrier to entry for analytics, empowering them to use existing data to:​ Reduce OpEx and CapEx expenditures by extending the life of assets and plants through proper, data-driven preventative maintenance ​ Gain an increased lead time for decisions/maintenance to minimize costs and impact to operations​ Reduce catastrophic asset failures by increasing visibility to KPIs and early failure detection Data Sources:​ EMS/SCADA, DMS, ADMS, AMI, Metering, Relays and Sensors Data Cleansing:​ Signals are cleansed to remove outliers, downtime, and abnormal operating data before establishing monitoring boundaries and models. Calculations and Conditions:​ Capsules are created for unique modes of operation. Metrics are calculated to analyze the time aged and time elapsed. Calculations are scaled across multiple transformer assets leveraging the Treemap view in Seeq Workbench. Reporting and Collaboration:​ Trends, Metric tables, multivariate scatter plots, and Treemap visualizations are combined into an Organizer Topic for quick consumption of the analytics by stakeholders. Visualizations are configured with auto-updating and scheduled date ranges so that reports were ready for daily morning meetings. Use Case Activity Asset Optimization Data Analysis Data Management Use Case Business Improvement Financial DOWNLOAD USE CASE276.46 KB View the full article
  4. Sustainability is critical to the health and survival of our planet, and now, more than ever, companies are prioritizing it as part of corporate social responsibility initiatives. According to a recent McKinsey survey, more than 50 percent of executives believe sustainability is very or extremely important for brand reputation and overarching corporate strategies. Unfortunately, only 30 percent of those executives believe their companies actively invest in sustainability or have implemented measures to reach goals. While many companies set goals, the ability to affect change often fails to trickle down the front-line employees who can make the most difference. View the full article
  5. Overall Equipment Effectiveness (OEE) brittanyebaugh Fri, 06/11/2021 - 12:22 Industry Cement Chemicals & Petrochemicals Food & Beverage IIoT Mining, Metals & Materials Oil & Gas Pharma & Life Sciences Power Generation Pulp & Paper Utilities Challenge: An in-depth understanding of Overall Equipment Effectiveness (OEE) across a site is critical to identify process bottlenecks and maximize production. OEE analysis can be difficult to standardize and scale across many similar or dissimilar assets that are found at a manufacturing site. It is useful to develop a simple scoring method to categorize various process units on a spectrum of overall effectiveness. Solution: A large-scale manufacturing operation implemented Seeq’s advanced analytics. In Workbench, the teams are now able to utilize the Point and Click tools to identify unique modes of operation and time spent in each mode. Their engineers are empowered to use historical benchmarking to identify appropriate threshold limits to differentiate between ideal and non-ideal equipment operation. The Seeq integration with existing asset hierarchy systems enables analysis to be scaled across all site equipment. Results: Implementing the plant-wide OEE Dashboard, including high-level comparison across process units, unveiled some unexpected bottlenecks. While the site had historically been looking only at uptime as a means of measuring OEE, they were ignoring large periods of time when the unit was running but under some constraint. This analysis across assets enabled them to identify which processing units saw the greatest rate constraints while running, investigate the root cause of those constraints, and invest in those areas by installing capital projects to de-bottleneck the process. Data Sources: OSIsoft PI + Asset Framework Data Cleansing: Seeq capsules were created using Value Search to differentiate between each of the various modes of operation. Calculations and Conditions: Asset Trees Asset Swapping Treemap Histogram Value Search Signal from Condition Scorecard Metric Formula Reporting and Collaboration: A high-level dashboard was created to showcase the "big picture" at the top of the dashboard. The treemap color-coded by OEE score of the various process units is interactive, and consumers of the report can click into a unit shown in red to gain further insight into what aspects of that unit are driving the low OEE. Use Case Activity Asset Optimization Use Case Business Improvement Reliability DOWNLOAD USE CASE511.06 KB View the full article
  6. Run Length Optimization brittanyebaugh Thu, 05/27/2021 - 13:41 Industry Chemicals & Petrochemicals Food & Beverage Mining, Metals & Materials Oil & Gas Challenge: Many continuous manufacturing units run into process throughput constraints over the course of a run. These constraints are often reversible but come with the high cost of shutting down to clean or maintain equipment. The manufacturing plant must balance the cost of the shutdown with the regained efficiencies to optimize the overall production rate to meet its targets as soon as possible. Meeting targets sooner translates into more production and increased profits in the long term. Developing solutions to these types of optimization problems can be complex and often requires advanced modeling packages and programming experience. Solution: Seeq Formula can be used to calculate the number of shutdowns that minimize the total time required to produce a given order size. Once the number of shutdowns is determined, engineering teams can calculate the length of the run times between shutdowns and create a golden profile of these run cycles. The forecasted profile can then be used to compare against the actual production rate to understand if the operation is on track to meet their best-case order fulfillment date. Results: A sold-out production unit has been looking at ways to increase capacity in tiny increments. By implementing this proactive, optimized downtime strategy, they were able to meet supply chain targets an average of 11% sooner over the course of the year. This allowed them to increase production volumes for multiple products, growing sales and market share. Data Sources: Process Data Historian (OSIsoft PI, AspenTech IP21, Honeywell PHD, Wonderware, etc.) Data Cleansing: Invalid data was removed Capsules were created for the different production campaigns Calculations and Conditions: Seeq Formula was used to: Identify the current run’s existing data set Create a continuous signal for the time elapsed since the start of the run Create a running production totalizer Calculate the number of downtimes that would be required for a given order quantity Calculate the total cycle time (time between downtimes) for a given order quantity Signal from Condition was used to calculate the minimum cycle time for the order size A capsule was created from the start of the run to the point of optimum cycle time A Reference Profile was built to project optimal cycles for monitoring performance from the best case Reporting and Collaboration: The summary of this analysis was added to an Organizer Topic report that is run after the first few days of each product campaign. It’s used to evaluate production rate, degradation rate, and inform operations whether continuous (degrading) operation or periodic shutdown and maintenance will achieve their desired production outcome sooner. Use Case Activity Asset Optimization Use Case Business Improvement Yield DOWNLOAD USE CASE427.59 KB View the full article
  7. In the process manufacturing world, there’s been a significant increase in the accessibility into operational and equipment data. Teams now have visibility into both historical and near real-time data from their operation, and can even monitor this as it’s happening at remote locations. But the problem with this is that teams are drowning in data—”DRIP”—data rich, information poor. View the full article
  8. Using Prediction Tool for Modeling Signals brittanyebaugh Tue, 05/25/2021 - 11:34 View the full article
  9. Podcast: Digital Transformation Viewpoints seeq_admin Tue, 05/18/2021 - 12:51 Janice Abel, Principal, ARC Advisory Group, and Morgan Bowling, Sr. Analytics Engineer, Seeq discuss advanced analytics and machine learning (ML) innovations for process manufacturing data. Listen Now View the full article
  10. Podcast: How to buy from a start-up without getting burned seeq_admin Tue, 05/18/2021 - 06:52 Michael Risse, VP and CMO for Seeq, and J.P. Bauman, principal at Altira, a venture capital fund that focuses on venture growth equity stage companies serving the energy and broader industrial space, join Keith Larson to share some best practices when buying innovative new technologies. Listen Now View the full article
  11. Leading in Times of Uncertainty seeq_admin Tue, 05/11/2021 - 06:22 When we look back at our lives, it becomes clear that all of us have taken a series of risks to get to where we are today. We went out and pursued an education to enable us to succeed in traditionally male dominated fields, often at great cost and with substantial time commitment. We chose jobs that would give us the experience we needed to reach our career goals. Throughout this journey, we have shared our ideas, and there is certainly risk in that, as it can open up one to criticism. Beyond sharing what we think, we also navigate day-to-day risks, such as where we sit in a meeting and when we speak. Download the PDF View the full article
  12. Management Operations Reporting roger.sprague… Mon, 04/26/2021 - 08:18 Industry Chemicals & Petrochemicals IIoT Oil & Gas Challenge The plant management team reviews a high level summary report out of plant performance each morning. This report contains data from a variety of different business units and groups (environmental, safety, process engineering, mechanical) across the plant. Each group must manually input their data ahead of the meeting. The management team only sees this report once a day because it is manually compiled by the individual teams. The report is owned by one person, is stored in a place that not everyone can easily access, and is currently generated in Excel with macros that often require manual manipulation. This report must combine data from a variety of different systems and organizations efficiently. Solution Using Seeq’s organizer topic, different areas throughout the plant can collaborate to put together one report. The larger plant operations report is a roll-up of the individual unit reports, maintenance records, and operator shift logs. The report combines the top Key Performance Indicators for each unit with inventory data to get a snapshot of plant operations. Safety, Environmental, and Maintenance data is also included to ensure management can see the full picture of operations. Results A new report was created in Seeq which eliminates the manual data entry and allows members of the team to update the report at any time using the Auto Update function. Everyone can view the summary and drill down to the individual unit reports to view detailed unit operations, loss tracking, and operator shift logs with just one click. The report is stored on the Seeq Server so everyone in the organization can quickly access and update it whenever is convenient for them. Data Sources Process Data—OSISoft Pi or any historian Lab Data – LIMS SQL Data – Environmental, safety, and maintenance data Calculations and Conditions Scorecard Metrics for Key Performance Indicators on each unit – color-coding indicates whether the unit is running to target or not Simple Trending of inventories to monitor when tanks are getting low/full for planning groups to monitor Treemap to monitor asset wide performance Reporting and Collaboration Seeq’s Organizer Topic is used to roll up individual unit reports and auto-update the report on demand. Use Case Activity Collaboration Use Case Business Improvement Financial Reliability Download Use Case475.29 KB View the full article
  13. Online Furnace Conversion roger.sprague… Mon, 04/26/2021 - 05:57 Industry Chemicals & Petrochemicals Oil & Gas Challenge Making an accurate prediction for real-time furnace ethane conversion is critical to maintaining effluent purity and resulting ethylene production rates. Overestimation of conversion leads to undershooting production targets and lost opportunity. Underestimation can lead to overproduction, overloading downstream units or increasing inventory storage costs. For a large-scale petrochemical manufacturer, conversion calculations were complicated, typically performed offline, and required manual calculation each time a new weekly lab sample was received. Solution An online version of the existing furnace conversion calculation script was implemented using Seeq’s external calculation function. The actual conversion was compared to the online prediction signal and large deviations were flagged. Asset Tree functionalities were used to perform the calculations on a single furnace and then quickly scaled to all site furnaces, with results summarized for all furnaces in a dashboard. Auto-updates were configured so that anytime users open the dashboard, they see the furnace conversion and comparison calculations for each new lab sample received. They can then make decisions as to whether the predicted conversion signal, a value calculated in the plant’s distributed control system, should undergo a bias adjustment to better reflect recent data. Results This improvement eliminated the need for SMEs to manually calculate the furnace conversion, saving time, and ensured the calculation was performed for every lab sample received. When a high delta between the actual and predicted conversion is observed, the process engineers take proactive actions to adjust the predicted conversion signal calculation. This provides operators with an accurate account of ethylene production rate and allows them to make rate bumps when necessary to keep production rate at target. Recently, one site observed more than 1 million pounds of production losses due to running below target production rates when they believed they were on target. Since the implementation of the online calculation, they have seen less than half of these losses, projecting a year-on-year revenue improvement of more than $250,000. Data Sources Process Data Historian (OSIsoft PI, AspenTech IP21, Honeywell PHD, etc.) OSIsoft Asset Framework or other asset hierarchy LIMS (Lab Information Management System) Data Cleansing Process data signals were cleansed to include only data with timestamps matching lab data timestamps Calculations and Conditions Lab data and process data from different data sources were overlaid in Seeq Workbench. Seeq Formula was used to call the external conversion calculation script for calculating actual values from the process data and lab data, eliminating the need for manual calculation any time a new lab value is received. Scorecard metrics showed the delta between the actual and predicted furnace conversion at the time of each lab sample and used priority color thresholds to draw attention to high deviations. An asset structure was built for these calculations using Seeq Data Lab. Treemaps were used to view current predictor status across all furnace assets. Reporting and Collaboration Results were summarized for all furnaces in a dashboard. With configured auto-updates, users see the furnace conversion and comparison calculations for each new lab sample received upon opening. They can make immediate and accurate decisions as to whether the predicted conversion signal needs a bias adjustment to better reflect recent data. Use Case Activity Asset Optimization Use Case Business Improvement Quality Yield DOWNLOAD USE CASE439.94 KB View the full article
  14. When I graduated with a degree in chemical engineering, I was excited to join the workforce and make a positive contribution. Being part of different engineering teams at various companies made me believe that mining the many years’ worth of big data stored in the process manufacturing historians could be a gamechanger. There were many opportunities to contribute to sustainability, reliability, and profit maximization goals that would result in happier customers and a more environmentally responsible industry. View the full article
  15. Data Across Time for Transformer Health Insights roger.sprague… Tue, 04/13/2021 - 09:15 Industry Power Generation Challenge Knowing when to maintain one of the most critical components within the electrical network, the power transformer, is a unique challenge. Transitioning from a calendar-based maintenance plan to a condition-based plan requires the synthesis of information from a variety of data sources. This might include: Nameplate characteristics Diagnostic tests (such as fluid, dissolved gas analysis, and electrical tests) Maintenance and financial history Real-time data (such as cooling performance) Developing and refining transformer health analytics that take advantage of all this disparate, but valuable data is arduous, especially when applied to numerous assets. This article provides an example of one such analytic, Dissolved Gas Analysis, and demonstrates how it can easily integrate with multiple data sources and scale across a fleet of transformers. Solution DGA is the study of dissolved gases in transformer oil. Transformer oil is used to insulate the transformer's electrical equipment. When it breaks down, it releases gases within the oil. The distribution of these gases can be related to the type of electrical fault and the rate of gas generation can indicate the severity of the fault. DGA provides an inside view of a transformer. By analyzing dissolved gases, we can observe the inner condition of any transformer. Many faults like arcing, overheating, and partial discharge can only be detected by analyzing gases. Many electrical utilities have a DGA program. This typically consists of manually sampling the oil and sending the sample to a laboratory for analysis (every 1-4 years). There are a number of industry-recognized methods used to translate the lab results into fault codes. Seeq’s advanced analytics can be used to easily aggregate the data needed for such methods, evaluate the required formulas, and scale the analytics across numerous assets. Methods include: IEEE C57-104 Total Dissolved Combustible Gases IEC 60599 Roger's ratio Dornenburg's state estimation Duval's triangle Seeq can also be used to develop customized transformer health algorithms. Results Rapid iteration and refinement of analytics to identify assets for targeted, condition-based maintenance Improved reliability and reduced maintenance costs The ability to predict OpEx and extend the life of the transformer Maintenance of transformer versus replacement of transformer Reduction in catastrophic asset failures ($10s of millions per transformer) Minimal corporate exposure from preventable failures and outages Connected data, all in one place Individual transformer health algorithms Avoidance of transformer failures saves multiple millions of dollars in outage cost Data Sources Load and temperature data is stored in PI DGA test results are stored in an SQL database Calculations and Conditions Formula Value search Asset swapping Treemap Reporting and Collaboration Organizer topic Use Case Activity Asset Optimization Use Case Business Improvement Reliability Download Use Case482.12 KB View the full article
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