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The role of operator alarms is to alert the operator of abnormal process operation as early as possible, giving the most time to react, address the developing issue and keep producing product while the plant is in a degraded state, or at worst shut the unit down in the most sympathetic way. Good alarms produce the economic benefits of higher plant availability and more saleable product, while reducing operator load, giving operators the ability to react to abnormal situations.

Achieving these benefits requires well-designed and properly positioned alarms as well as a healthy process. Many organizations have reviewed these alarm limits repeatedly through rationalization exercises, never getting the real benefits they know are possible.

C Visual Explorer (CVE), PPCL's flagship software product, provides a graphical framework for bringing the entire process history into the context of alarm rationalization. CVE lets engineers, operators and all involved see directly the full richness of operation and the process envelope, relating this immediately to current and potential alarm limits. The range of conditions a process experiences, including variations in feed, ambient temperature, changing demand, different goals and normal operation variability are included, linking operations through from incoming material to product quality and economic KPIs. It has never been easier to explore years of process history for thousands of variables for rationalization and other applications.

In this webinar from June 2017, we demonstrate how to take full advantage of this display and data, leveraging the parallel plot to give easy access to historic data in the context of desired plant operation, alarm and process limits for hundreds of variables. Bringing historical operation into the alarm process allows the immediate evaluation of alarm limits, taking less time and enabling continuous alarm review, and giving better limits that have been pre-tested as well as vital process understanding. We give examples on real systems and discuss some of the successes our users have had.

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Here at PPCL we have spent 25 years developing GPC (Geometric Process Control), an innovative new method of viewing process operations. Our work with gas production includes production fields, gas treatment and processing, LNG production, landing and re-vaporization. We have worked with LNG producers worldwide helping them to understand their process better by analyzing their data in far more detail than was previously possible. This work has contributed to achieving better operation through better alarms, process optimization and product compliance – improvements directly impacting the bottom line. 

This webinar from May 2017 takes an in-depth look at gas production. We demonstrate our technology using graphical tools to optimize product split in an LNG production train, monitor performance week-on-week and identify targets for process improvement. By connecting historical data completely across the process with quality variables, GPC enables value-finding in your process through data exploration and discovery which are simply not possible with today’s techniques.

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The Problem: Process plants generate continuous data for thousands of variables at sub-minute frequencies. This is far beyond what process engineers can analyse with their conventional analysis tools. This data has enormous value, containing the records of plant operation and implicitly the relationships between process variables and quality variables. But only a tiny fraction of this data is used today. Many may consider the “Big Data” methods that are the current buzz, but these approaches are challenged by process plant data.

Big Data techniques focus on pulling subtle correlations from largely uncorrelated data, but chemical processes have extensive relationships due to balances and governing physical laws. Predictive Analytics provides generalized answers through simplifications; choosing a small subset of variables, processing or averaging data, and ignoring the fundamental complexities such as nonlinearity of the process. This can destroy much of the richness of the data and reinforce preconceptions. It can also be time-consuming and require a statistician to interpret the results.

The Solution: Geometric Process Control (GPC) is a visual technology: quick and easy to understand and implement by anyone familiar with the process. Hundreds of variables can be seen simultaneously on one graph to gain an overall understanding of your operation, target investigation, test hypotheses and quickly identify improvements.

PPCL presents a Geometric Process Control alternative to reach the goals sought from Big Data and Predictive Analytics that shows how to operate the process so that objectives are met, keeping their values at the desired targets.

In this webinar, delivered on 17th and 18th May 2017, Alan Mahoney, PPCL's Technical Director, demonstrates GPC using graphical tools to screen for key variables and eliminate the effects of uncontrolled variables.  He shows how to approach big datasets and explore them visually using the parallel plot, a unique graphical technique that puts axes parallel to each other rather than perpendicular, allowing the exploration of interactions between variables with orders of magnitude fewer graphs. By connecting historical data completely across the process from incoming conditions and initial processing conditions to final quality variables, KPIs and performance with the richness of years of data in a technique that can examine hundreds of variables, the parallel plot enables discoveries and exploration that are not possible with today’s techniques.

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Operational Excellence is based upon the idea that all activities in a plant contribute to a flow of increasing value towards the customer which all employees can see and understand so that when the flow breaks down they are able to fix it without the need for management intervention. The concept applies easily to process plants if one thinks of the value stream starting from raw material feedstocks and progressing through several stages of processing and intermediate products to final product followed by packaging and shipment to the customer. At the heart of the concept is the ability to see and understand the end-to-end value flow.

The concept has been less than well-implemented in process plants until now because there wasn’t a simple way to see the end-to-end flow of increasing value other than by breaking the flow into small pieces leading to local optimisations at the expense of overall optimisation or by averaging and totalising in dashboard displays until all the detail and abnormalities were lost from sight. Many hundreds of variables are necessary to describe the end-to-end flow from raw material to finished product. Most plants already measure all the necessary variables, and many more, as often as every few seconds, store them in databases but then find they are unable to look at more than five or ten at a time so cannot see the end-to-end flow in a single picture. They have resorted to summarising it into averages and totals until all detail needed to recognise and correct abnormalities was lost and the concept of a self-healing flow was defeated.

But now we can see the end-to-end flow in a single picture! PPCL's award-winning Geometric Process Control (GPC) technology provides a 1,000-variable graph and the first-ever general method of modelling the end-to-end flow envelope of a process as a multi-dimensional geometric object. The new graph allows anyone to see and understand the end-to-end value flow described by one thousand or more variables in a single graph without summarisation or loss of detail. The graph brings remarkable analytical, optimisation and monitoring power to the user without requiring more than high-school mathematics, and leads to the reduction of end-to-end operational variability which is at the heart of operational excellence.

Operating envelope models are used for detection and prediction of abnormalities, compliance with KPI’s, KPI target-setting, SPC replacement or “just” for helping the process operator to stay inside many sets of operating limits and KPI limits simultaneously. The appeal of these models is that they require no equations and no more than high-school mathematics to create and use but only a good understanding of the process and its objectives. Models are constructed from KPI Targets, product specifications and equipment limitations.

Before being exposed to Geometric Process Control, many have struggled to understand how a mathematical model could possibly be “equation-less” or why a 1,000 variable graph has such a big advantage over the traditional methods of Predictive Analytics. Seeing is understanding with GPC, and many have had a Eureka moment in our demonstrations and webinars as the accuracy of our descriptions and the potential of GPC to bring a step-change to Operational Excellence strikes home.

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Unscheduled downtime is one of the biggest threats to any process plant. Value lost to downtime and degraded product quality during abnormal events and the necessity of designed overcapacity is one of the largest preventable costs. Process events come in the form of disturbances, trips, and excursions that cause downtime and lost production. Achieving operational excellence requires reducing the frequency and impact of these events.

This webinar explores PPCL’s novel Geometric Process Control (GPC) technology for understanding the course and causes of these events and generating real-time operator alerts for early warning of future events.

The start is investigating events using historical data from the plant historian through C Visual Explorer (CVE). Building from individual events to discovering similarities between events, CVE allows investigating significantly larger datasets than most engineering applications, hundreds of variables across thousands of time points. This power lets us quickly see and explore data far beyond what we’d typically use.

PPCL’s online process monitoring tool, C Process Modeller (CPM), goes beyond traditional alerts by implicitly including the relationship between process variables and providing a sensitive detector for changes. By excluding events and event precursors from our model of normal operation, CPM provides a powerful low-cost method of building event prediction models that have been shown to provide hours or days of advance warning of process changes, giving plant personnel more time to react and mitigate the effects of disturbances and faults. We look at examples including a gas turbine-driven generator and surge in a large propylene refrigeration compressor. CPM models can be created in just a few hours, bringing the benefits of condition monitoring to applications where it isn't currently economical. Process availability and efficiency will improve and facilitate the move toward predictive maintenance and operational excellence.

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This webinar was presented by Michael Bell, Principal Applications Engineer at NOVA Chemicals of Canada in September 2016. The feed preparation section at NOVA’s Ethylene 1 plant receives and combines feed from a multiple of sources while reducing the feed pressure to prepare for thermal cracking in an ethylene furnace. What is unique in modelling this process is that it has multiple modes of operation. This multi-mode problem fits well with CPM and CVE, which allow for the automatic turning off and on of variables to minimize the number of alerts sent to the panel operator by the setting of the “Phase” variable.

Geometric models are a new class of mathematical model and well-suited to plant applications because of their very low cost due to the speed with which the wholly visual nature of Geometric Process Control (GPC) allows them to be created, implemented and maintained. They have been applied to continuous and batch processes in process industry segments ranging from pharmaceuticals through chemicals to oil refining and upstream oil and gas production. 

PPCL’s mission is to reduce variability in plant operations. This starts with the gaining of better process and operations understanding using our offline product, C Visual Explorer (CVE), to view years of process operation for hundreds of variables in a single interrogable graph. This much wider view than was ever previously available considerably simplifies and accelerates traditional process applications such as process analysis, de-bottlenecking, optimisation, alarm rationalization and KPI target setting and monitoring. Operating Windows found by CVE and expressed as independent Operating Limits on process variables are immediately usable by plant operations as a guide to greater achievement of the business objective.

Operating Envelopes are modelled by our online real-time product, C Process Modeller (CPM), updating on a frequency between seconds and minutes and providing alerts to supplement alarms, guiding operations to stay inside the Operating Envelope and providing early warning of impending events, alerting the process operator to take mitigating action.

Michael offers an invaluable account of NOVA's experience with PPCL tools. The webinar includes a Q&A session with process industry professionals addressing some of the issues which commonly arise when considering and implementing the software.

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Phillips 66 talk about their experiences using CVE for Alarm Rationalization in their Bayway, NJ refinery and field questions from users and non-users of CVE. If you are new to using CVE for Alarm Rationalisation you will probably want to start with one of our own Alarm Webinars, but this one is not to be missed. 

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This Webinar was delivered in May 2015 by Ian Nimmo, President of UCDS, one of our partners who we have worked with on several projects. Ian discusses in depth the IEC 62682 standard. This webinar gives a wider coverage of the whole of the new Standard from Philosophy Development right through to Implementation.

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John Rezabek is a senior control specialist at Ashland ISP, Lima, Ohio. His presentation "Traversing Hyperspace in the n-Cube to Rationalize Alarms" was voted one of the best at the Emerson Exchange 2014 Conference. See and hear what John thought of CVE and the PPCL GPC method after using it for alarm rationalization. 

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