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Machine Learning (ML) is a top priority for most manufacturing organizations— and for good reason. Machine Learning models can simulate processes and predict outcomes, drive continuous improvement cycles, and enable manufacturers to proactively maintain and optimize equipment. That’s a big deal; it can mean millions of dollars in savings and efficiencies in a short amount of time. However, leveraging Machine Learning in manufacturing settings is not as simple as finding the right algorithm or hiring data scientists. The engineers and plant operations personnel that understand their processes and the associated sensor data are critical participants in any effort to apply ML in an industrial context. They’re the folks that must understand and trust the predictions. They will want to carefully vet changes and then verify results. In these scenarios, Information Technology (IT) personnel like sysadmins and data scientists bring the algorithms and software knowledge – often including programming skills. Operational Technology (OT) employees like chemical engineers and operations managers bring the real-world manufacturing and process knowledge. The last necessary piece is an environment where they can collaborate effectively. Otherwise, promising ML initiatives can end up as frustrating failures. Seeq provides key components to enable IT/OT teamwork for successful Machine Learning efforts: data access, cleansing and contextualization; algorithm development and iterative workflow; collaboration and knowledge capture; publishing of Machine Learning models with simple point-and-click, workflow-centric interfaces for the OT personnel; and now a rich ecosystem of community-based ML solutions for manufacturing environments via the Seeq Open Source Gallery. View the full article

Process control engineers focus on optimizing the automatic control of the process operation, designing control strategies, specifying controller tuning, implementing advanced process control and real time optimization applications, and troubleshooting sources of abnormal process variation. In contrast to process engineers, control engineers focus much more on dynamic operational responses and signal trajectories. Control engineers often want to identify and analyze transient, non-steady modes of operation (e.g., setpoint ramps, startups, batch sequences, etc.)—for which Seeq’s extensive contextualization capabilities (capsules and conditions) were purposely built. Process engineers and control engineers typically work in different roles within the process automation pyramid. While they both derive analytics value from Seeq’s contextualization and calculation tools, there are several capabilities that control engineers can specifically utilize for their unique roles. View the full article

Case Study: Process Optimization through Digitalization at Covestro [email protected]… Wed, 09/29/2021 - 06:44 Case Study: Process Optimization through Digitalization at Covestro View the full article

For electric utility organizations, prioritizing maintenance and replacement strategies across fleets of complex assets is critical to avoiding costly downtime. But without the right combination of subject matter expertise, data connectivity, and analytics tools, these organizations are left with a number of questions and roadblocks. View the full article

Technology investments, regardless of industry, come with risk, and startup investments are often met with even more hesitation. While startups promise to bring greater innovation, major organizations fear these young, unestablished companies could bring even greater risk to mission-critical projects. View the full article

Chevron Technology Ventures Case Study [email protected]… Wed, 09/15/2021 - 05:32 Chevron Technology Ventures Case Study View the full article

SaaS Master Trust Policies roger.sprague… Tue, 09/14/2021 - 09:34 SaaS Master Trust Policies View the full article

Detect Valve Erosion and Predict Failure brittanyebaugh Thu, 08/19/2021 - 08:34 Industry Cement Chemicals & Petrochemicals Food & Beverage IIoT Mining, Metals & Materials Oil & Gas Pharma & Life Sciences Power Generation Pulp & Paper Utilities Challenge: In the process manufacturing industries, it’s difficult to characterize erosion by just monitoring the univariate process variables around valves. This lack of visibility leads to failure incidents, which are a risk to worker health, plant safety, and the surrounding environment. These incidents can also quickly lead to additional costs due to unplanned maintenance and production downtime. Solution: Using Seeq, manufacturers can implement a condition-based monitoring analysis to monitor valve health across their fleet and accurately estimate time-to-failure. Subject matter experts can utilize historical failure data to create a predictive maintenance forecast to preemptively detect valve performance issues before failures occur. Valve health is determined by monitoring its performance with first principles analytics (valve Cv), which leads to identifying bad actors. Results: The likelihood of unexpected valve failure is significantly reduced, saving costs due to lost production and unplanned maintenance. Implementing this analysis will also decrease health, safety, and environmental risks due to loss of containment if any valve fails. Data Sources: Process Data Historian: OSIsoft PI Maintenance Records: SAP Data Cleansing: Use Agile Filter to smooth noisy data Remove production downtime periods from analysis data set Calculations and Conditions: Formula to calculate first principles parameters: Operating Cv (actual) versus Theoretical Cv (expected) based on Flow Rate, Valve Position, and Pressure Drop across valve; also calculate Delta Cv Boundaries and Value Search to find areas where Operating Cv is > +/- 10% if Theoretical Cv Prediction to create a regression model to estimate Delta Cv behavior into future based on past historical failures (training window) Reporting and Collaboration: Prediction monitoring Trend in Organizer Topic Tables showing Estimated Failure Date / Recommended Maintenance Date / Time-to-Failure Use Case Activity Predictive Analytics Use Case Business Improvement Reliability DOWNLOAD USE CASE261.9 KB View the full article

Facilities large and small rely on machinery (rotating equipment) to make their operations tick. Reliable and efficient machinery operation is instrumental in ensuring facilities meet their safety, environmental, and production targets. World-class machinery performance is the result of good design practices, effective and skilled maintenance, and operation within appropriate design conditions (which, depending on operating requirements, can be challenging). View the full article

Case Study: Biopharmaceutical Company Uses Advanced Analytics to Improve Manufacturing Processes [email protected]… Thu, 07/29/2021 - 12:07 Industry Pharma & Life Sciences Challenges At biopharmaceutical operations, fill weight analytics are essential in ensuring ready-to-ship products. Often while tracking the consistency of fill weights during batch manufacturing, manufacturers only find out if a product is over or under fill weight at the end of a batch, resulting in a product that needs to be scrapped or inadequate manufacturing capacity. Fill weights are critical for product quality assurance, ensuring consistency over time across different products, dosages, batches, and time points. Multivariate analysis is important to be able to predict conditions. Analysis on complex and multivariate data is challenging to use for determining and predicting when operating conditions are occurring that could be an indicator of a ‘bad batch’, signaling that proactive intervention can occur. Monitoring consistency of batches in near real-time, predicting batch quality, and performing root cause analytics to identify and determine the cause of batches outside of optimal operating conditions is difficult and time-consuming in applications such as spreadsheets. They also make it difficult for teams to collaborate and share insights on commonly accessible data and analytics work efforts within work teams, across different manufacturing sites, and across global operational reporting. On top of everything, it’s additionally challenging to aggregate multiple processes and contextual data sources into a single application. Solutions & Benefits Using Seeq deployed on AWS to notify operators and engineers of conditions empowers teams to predict a future bad batch. Self-service analytics enable subject matter experts (process engineers, managers, and operators) to easily access, cleanse, contextualize, and perform advanced analytics and machine learning on industrial data. Seeq’s analytics tools are broadly applicable, adding value across dozens of use cases, from control system validation to golden profiling to preventative maintenance. The ease of use for process engineers and teams to gain and share insights quickly on time periods of interest within the manufacturing process leads to immediate improvements to operational performance. Seeq increases the value of investment in OSIsoft Pi and Pi Asset Framework. Seeq’s extensive support for OSIsoft’s solutions enable teams to develop insights on one asset, batch, or process and quickly scale that analysis to hundreds of assets leveraging their asset framework. Seeq on AWS enables organizations to easily scale up, adding new users to new manufacturing sites and new data sources (including future plans to add Amazon Redshift as well as MES system data) across their global locations. Seeq on AWS also ensures application accessibility for remote and distributed teams with access to near real-time data while engineers and teams are working from home or from remote locations. The application provides ease of enterprise software procurement via Seeq on AWS Marketplace. Data Sources OSIsoft PI Amazon Redshift View the full article

Energy Storage Capital Expense Optimization [email protected]… Mon, 07/26/2021 - 11:14 Industry Power Generation Challenge: Each substation in a transmission and distribution (T&D) system has multiple transformers feeding distribution circuits. Each of these substations has a set capacity, and utilities must report capacity overages to regulators. Generally, the sum of all substation transformer ratings defines a substation's capacity. In this case, a utility could monitor the sum of MVA load for all transformers to total MVA normal and 24-hour emergency ratings. In order to avoid capacity overages, T&D operators install batteries. Batteries are expensive, so T&D operators must decide carefully what locations make the most sense. Considerations include: Capability of existing transformers to serve present peak loads Capability of existing transformers to pick up full load of station when other transformer fails Asset health Outage impact Forecasted demand Cost and pricing of battery installation Solution: Seeq is used to monitor a signal against thresholds and investigate what would have happened if one transformer had failed during a certain time period. These same analytics are also applied across a field of substations using asset swapping. Results: Implementing Seeq advanced analytics reduced Capital expenditures by prioritizing battery installation based on actual equipment condition and risk of overload. It also improved system reliability to avoid $10s of millions in unplanned downtime and capital expenditures for premature replacement. Seeq’s advanced analytics enable the aggregation of data from multiple data sources, investigation of "what-if" scenarios, and rapid iteration across assets. Installing batteries at a substation improves grid reliability and the ability to meet demand anytime. Specific benefits include: Solar smoothing: smooth short-term changes in voltage due to intermittent generation Distribution deferral: non-wires alternatives to defer or eliminate the need for traditional utility upgrade Outage management: reduces the cost of deploying mobiles for contingency resources during substation construction Microgrids for critical facilities: allows critical facilities to operate independently of the electrical grid during extended grid outages Peak reduction: help resolve potential overloads, address power quality issues at host sites, reduce bills for public sector customers Energy cost: discharge batteries every day for one to four hours, reduction in the amount of energy to purchase and charge battery at night at a much lower cost Data Sources: Load data is stored in PI Capacity ratings are stored in a SQL database Data Cleansing: Use Low Pass Filter to create a smoothed version of the load Signals are cleansed to remove outliers, downtime and abnormal operating data before establishing monitoring boundaries and models Calculations and Conditions: Periodic condition Formula (splice) Deviation search Low Pass Filter Signal from Condition Scorecard Asset swap 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. Use Case Activity Asset Optimization Use Case Business Improvement Regulatory Reliability DOWNLOAD USE CASE444.94 KB View the full article

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

World Cement: Room for Improvement brittanyebaugh Mon, 07/19/2021 - 12:36 Cement producers need to leverage their data to measure manufacturing productivity, ensure product quality, and monitor equipment performance. Advanced analytics can be utilised to gain important insights for maximising production efficiency, which are key for continuous improvement of manufacturing processes. Data is available at every cement plant, although it is not always easy to access or leverage. Disparate data sets are spread across historians, smaller relational databases, laboratory information systems, asset management systems, computerised maintenance management systems, and other sources. Advanced analytics applications can connect to all of tthese data sources and provide a central location for subject matter experts (SMEs) to access all relevant data. This article presents three case studies demonstrating how advanced analytics is used to improve process performance, quality, and availability. Download Now View the full article

True Transformer Age for Replacement [email protected]… Wed, 07/14/2021 - 11:29 Industry Power Generation Challenge: Knowing when to replace a substation power transformer is a difficult task. How do you know you have utilized the full life of the transformer? Most transformer manufacturers define the end of life as age 40. However, lightly-loaded transformers may not reach the true age of 40 until their 60th or 70th year in service. Without the correct data analytics tools, the effects of loading beyond a transformer's nameplate rating over time are extremely difficult to calculate, making it almost impossible to know a transformer's true age and when it should be replaced. Solution: The IEEE C57.91-2011 standard provides algorithms for calculating the aging acceleration factor for a given load and temperature and the percent loss of life of transformer insulation. Using Seeq, users can combine real-time temperature and loading data from data historians with nameplate/characteristic data stored in SQL databases to calculate true age for a system of transformers. Results: The use of Seeq advanced analytics reduces capital expenditures by prioritizing replacement based on actual equipment condition and improves system reliability to avoid $10s of millions in downtime and capital expenditures for premature replacement. The Seeq application also allows for aggregation of data from multiple data sources, extrapolation of data to installation year, and rapid iteration across assets. Data Sources: Load, temperature and oil/gas analysis data is stored in PI Equipment heat run properties are stored in a SQL database Calculations and Conditions: Formula Value search Scatterplot Scorecard Asset swapping Treemap 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. Use Case Activity Asset Optimization Use Case Business Improvement Financial DOWNLOAD USE CASE497.65 KB View the full article

R52 Tables brittanyebaugh Fri, 07/09/2021 - 06:42 View the full article