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Register below for one of our live webinars that share new approaches to process improvement & optimisation. 


Or watch one of our Pre-Recorded Webinars here...


Revolutionizing Alarm Management in Process Plants with Geometric Process Control

 

The alarm system is a key tool for operators to maintain the efficient operation of the process plant. Why are there so many issues? Nuisance alarms and alarms that have been stale for far too long cost us production, as well as potentially contributing to process incidents. How can we make the alarms, and the alarm process, better? Geometric Process Control is the answer, demonstrating the relationship between operator alarms and process behavior, leading to a revolution in alarm management.

PPCL has pioneered the development and implementation of the only method for modelling process operations as a multi-dimensional geometric object. While this method is simple, it’s far from simplistic. It can integrate more than 100 process variables over thousands of time points - achieving very high detection sensitivity.

This webinar will demonstrate the fundamental understanding relating operator alarm limits to process behavior, enabling the creation and management of an entire set of alarm limits. This differentiates Modern Alarm Rationalization from the isolated alarm methods used across the industry. With this method, alarm limits and their performance can be evaluated before putting them in place, forcing underlying process issues to be recognized and addressed. This is dramatically different from today’s try-it-and-see approach to “bad actors” and allows a rational process for setting alarm limits that have never activated. The result is much better alarm limits taking up less process engineering time, thereby reducing the number, duration, and staff levels of review meetings.

The webinar will concentrate on continuous processes but the methods are equally applicable to batch processes. It is free and suitable for anyone involved in plant engineering or operations in process industry or energy industry segments, and who is interested in a fast, practical, no-maths method for extracting information understanding from process history data. 




Understanding and Avoiding Unwanted Events in Process Plants

 


The cost of downtime and degraded production from abnormal events is a major cost in the process industry. Giving your operators even a few minutes warning of situations such as column flooding, pump failure or equipment fouling can dramatically reduce production losses. Traditional condition monitoring and fault detection models are time consuming and expensive to create and maintain. Here we present a simple, low-cost method based on process history which can be implemented by a trained engineer in a matter of hours.  

In this webinar, we demonstrate how our method works and answer your questions. We start with examining historic events in C Visual Explorer (CVE), a powerful tool that allows process engineers to deeply interrogate large data sources without the need for training in heavy mathematics. Building from individual events into whole classes of similar events, we pinpoint common causes of faults and identify opportunities for making our process more robust and reducing downtime. We then move to C Process Modeller (CPM), a process monitoring tool which models the operating envelope of fault-free operation as a multi-dimensional solid, providing dynamic real-time warning of developing events. You will see us build a ready-to-use model complete with Operator Display and an OPC Client interface to connect to your DCS or historian.





Golden Batches, Faster Grade Changes and Smoother Start-Ups Every Time

 

In this webinar, Dr Alan Mahoney, our Technical and Operations Director, shares PPCL's insights and methods for higher quality batch processes.

He demonstrates improving the yield of a multi-phase, multi-stage batch process. Dramatic reduction of quality variability and improvement of cycle times for batch processes, product/grade transitions and plant start-ups can be achieved by finding the best historic operating envelope, identifying the processing stages and targeting the key variables that drive process differences. We show you how to find these envelopes and identify the key variables which drive achievement of quality goals. We also investigate real-time monitoring of multi-dimensional operating envelope models which goes far beyond following single-variable trajectories and instead considers the ideal relationship between variables at each point in the batch.

The data required to drive these improvements is already available in your plant historian. PPCL’s C Visual Explorer (CVE) is the vital tool to unlock that information, putting it on an interactive graph so that engineering and operations can investigate it for process discovery, test hypotheses and evaluate the effect of targeted operations. This visual method requires no higher mathematics training, only process knowledge to ask and answer the questions whose answers will drive better and better production.

Smarter batch processing is within your reach! Watch this webinar today and let us show you how.

Reducing Process Energy Use with Improved Operation

Process plants rarely operate consistently at their minimum energy input. A number of factors contribute to this, including lack of knowledge of the best conditions and also the ease of operation with excess energy. There is an inevitable drift back to higher energy operation without ongoing monitoring and feedback as part of energy minimisation.

Process engineers worldwide using C Visual Explorer (CVE) – innovative software developed here at PPCL - to investigate historic data from PI, PHD, IP21 and similar have produced some remarkable process improvements in remarkably little time. Results include increasing the product output of a paraxylene plant already at maximum energy input; identifying and steering an LNG refrigeration process to the best of several non-stationary local efficiency optima by adjusting the constraints of existing MPC controls; and providing a hydrocracker operator with a map to avoid several high energy sink-holes that were not previously known, reducing energy usage by 40%.

In this webinar we demonstrate our simple no-maths visual method which requires only the knowledge of the process and its operating objectives and practices which senior process engineers already possess. It uses data already in your plant historian and can identify operational changes to a lower energy operating window. We also discuss maintaining improved operation into the future and real time guidance for the operator and/or existing process control systems.

GPC Technology for Big Data and Predictive Analytics in Process Plants



Process plants generate continuous time-series 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 potential value, containing the records of plant operation and implicitly the relationships between process variables and KPI, quality and performance variables. What led to the best performance and can it be repeated; what led to the worst and can it be avoided?

The currently trending “Big Data” approaches 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 which can destroy much of the richness of the data. Such approaches have little appeal to the busy process and control engineers close to the plant who have the domain knowledge essential to the use of any analytical method. They can also be time-consuming and require a statistician to interpret the results.

Our ground-breaking “maths-free” technology Geometric Process Control (GPC) provides engineers with graphical tools to work with big datasets spanning the entire plant process. This webinar demonstrates analysis of a process using a small dataset involving a year of data for 750 variables at 10-minute intervals. By connecting data spanning the entire process from incoming analyses through processing conditions to final quality variables, KPIs and performance with the richness of years of historical data, GPC enables engineers to explore their data and make discoveries that are not possible today.

Achieving Operational Excellence

Do your Key Performance Indicators drive Operational Excellence? Or does Operations pay lip-service to some targets that they consider unrealistic?
ARE they unrealistic?
How and why do unrealistic targets get created?
Is an impending abnormal event, such as pump failure, about to sabotage your pursuit of Operational Excellence?

In a modern process plant using traditional methods, identifying and evaluating KPI targets is somewhere between difficult and impossible. However, it becomes much easier when leading KPIs are positioned on an operating envelope defined by lagging KPIs.

Geometric Process Control, PPCL's innovative new technology, provides the way to quickly and easily see such an operating envelope across many hundreds of variables. This makes it immediately obvious to everyone which targets are inconsistent or unrealistic. Performance monitoring and reporting become clear and consistent for everyone involved, allowing process refinement and increasing understanding of how KPIs interact. The approach is radically different but, as with all really good inventions, much simpler than what it replaces. 




Finding, Understanding and Repeating Best Operation with Operating Windows

 

Setting KPI targets and reporting is necessary, but for use in the control room these targets need to be translated into operating windows. It is easy to get this wrong and difficult to realize when the window becomes outdated. This one hour webinar demonstrates a better way to address these problems. There can be a new understanding of the relationship between KPI targets, operating targets and process objectives. The webinar shows how to use that understanding to find the best operating window to achieve KPI targets and other operating objectives. Providing the best operating window to operators is the essential first step toward repeating and improving best process operations.


 


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