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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...

Reduce Process Plant Costs and Improve Availability with Affordable Event Prediction


A few minutes’ warning of approaching abnormal events in a process plant, such as column flooding, pump/motor failure or equipment fouling, can make all the difference for operators and dramatically reduce production losses. Traditionally, models for condition monitoring and fault detection were 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 user in a matter of hours.

In this free webinar, we’ll demonstrate how our method works and answer your questions. We’ll look at our mould-breaking software C Visual Explorer (CVE), a powerful data visualisation tool for investigating historical events and evaluating operating envelopes using data from the plant historian. Building from individual events into whole classes of similar events, we’ll pinpoint common causes of faults and identify precursor signatures. We’ll then look at C Process Modeller (CPM), an online 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 and bringing the operators’ attention to the key deviating variables.

Take a concrete step towards improving the stability, efficiency and availability of your process by registering now for a webinar in your time zone. There’s no charge and you are welcome to invite colleagues to attend.


Can't make it? Click here to access our previously recorded webinars

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. 

Modern Alarm Rationalization


Managing alarm systems and settings are vital for the operation of every process and often a major load on operation and engineering resource. Operator alarms direct the operator to take action to react to an abnormal situation to maintain efficient production, but commonly 50% or more of these are false or unnecessary requests, dramatically increasing the load on operators and leading to distrust of all alarms. The individual operator is left to decide which alarms are false and which are real, with no assurance that an operator on another shift would make similar decisions.

The problem arises because alarm limits are not truly independent of each other, so that it is incorrect to set them one-at-a-time in isolation as has been typically done in formal alarm rationalization and in periodic attention to last week’s or last month’s “bad actors.” This webinar demonstrates a new understanding of how alarm limits are related to process behaviour, leading to the new ability to create and manage an entire set of alarm limits together. This differentiates Modern Alarm Rationalization from the isolated alarm methods most people in the industry are still using.

Our innovative method generates much better alarm limits while taking less process engineering time, thereby reducing the number, duration, and staff levels of review meetings. It generates the functional specification for the detailed design (see IEC-62682 or ISA SP18.2) and implementation steps so that they are faster too.

The same tools are used in the alarm performance monitoring phase for monitoring alarm effectiveness in operating the process. This provides insights into causes and behavior that cannot be found in alarm logs that focus on only the variables that went into alarm without context, either in time or in the process.

This webinar concentrates on continuous processes, but the methods are equally applicable to batch processes. It is 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.

New tools for comparing alarm sets across modes or flooding events in the forthcoming CVE 2.8 release will also be demonstrated.

Reducing Process Energy Use with Improved Operation

Plants rarely operate consistently at their minimum energy input. This is partly because the minimum is not usually known and also because it is ‘easier’ to operate with excess energy. There is an inevitable drift back to higher energy operation without ongoing monitoring and feedback as part of energy minimisation. Where should you start?

The minimum energy required is not constant. It varies with ambient conditions; operational loads on the plant; changing disturbances such as catalyst activity, furnace efficiency and equipment fouling; and unintentional local optima and sink-holes that are difficult to identify or avoid.

In this webinar we demonstrate our simple no-maths visual method for bringing the many operational factors together which requires only the knowledge of the process and its operating practices and objectives – knowledge your senior process engineers already possess. It uses data that you already have in your plant historian and can identify operational changes to a low-energy operating window. We also discuss how to maintain low-energy operation into the future by monitoring operation against the low-energy operating window, or progressing to real-time use of an operating envelope for minute-to-minute guidance to the operator and/or existing process control systems.

Notable recent successes include increasing 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 up to 40%.

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.

GPC Technology for Big Data and Predictive Analytics in Process Plants

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 the webinar, delivered in May 2017, Dr Alan Mahoney, PPCL's Technical Director, demonstrated GPC using graphical tools to screen for key variables and eliminate the effects of uncontrolled variables.  He showed 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. He also demonstrated online GPC models for achieving quality targets and operational excellence.


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