Curvaceous Win AMEC Award From IChemE

We are this year’s proud winners of the AMEC Award for Innovation and Excellence in an SME. The Institution of Chemical Engineers presented us with the award at their annual awards ceremony. see photo

The Award is given annually to the small business that is deemed to have produced an outstanding technical invention. Geometric Process Control (GPC) impressed the judges by the originality of its approach to long unsolved problems, its invention and implementation of wholly new technology and its successful business application. Over 85 major blue-chip companies are already using GPC to reduce variable operating costs, to increase efficiency and throughput and reduce variability in their product quality. Users are gaining additional benefit from GPC’s impact in improving process safety, reducing environmental damage and saving engineering time.

The originality of Geometric Process Control stems from its provision of a graph for viewing and manipulating hundreds of variables at once. Thus GPC replaces the need for the two or three variable graphs which have represented the state of the art in data visualisation and analysis for the last 5,000 years. The technical methodology behind GPC is firmly rooted in the mathematics of n-dimensional space. This provides the ability to model the n-dimensional operating envelope of a process, another first, therefore allowing much better performance from existing processes without requiring capital investment. It is expected that in time GPC will replace single-variable statistical process control (SPC).  But although the underlying maths may be complex, the user requires no mathematical skill at all. All that is required are easily-learnt skills for visually interpreting the new graphs and a familiarity with the process being studied.

The IChemE Awards are keenly contested with over 60 entrants in this their twelfth year. Curvaceous Software and GPC have had an impressive year with recognition by the Carbon Trust and the Institute of Electrical Engineers in both of their Innovation Award programmes. Curvaceous was placed in the top four by the Institution of Electrical Engineers from a global field of entrants, and in April’s Carbon Trust Awards for reducing emissions of CO2 GPC ranked in the top three from over 250 entrants. In 2003 Curvaceous won the European Process Safety Centre Award for the biggest single contribution to improving the safety of process plants for their fundamental work in relating alarms to process operation. The EPSC is a part of the European Federation of Chemical Engineering.

Curvaceous Software is based in Gerrards Cross, Buckinghamshire with agents in Australia, Germany and North-East USA. They have been past winners of DTI Smart Awards and have made good use of the UK Trade and Industry schemes to encourage exports. Managing Director Dr Robin Brooks said "It can be a lonely life as an innovator so the recognition implicit in this Award is a major boost to everyone involved with the company. We are also very grateful to our 85 customers for their contribution because we are consistently aware that our success comes only as a result of the successes they are achieving through their use of our products."

This award was sponsored by AMEC plc, an international project management and services company that designs, delivers and supports infrastructure assets for customers worldwide across the public and private sectors. AMEC employs 44,000 people in more than 40 countries, generating annual revenues of around £5 billion.  AMEC’s shares are traded on the London Stock Exchange where the company is listed in the Support Services sector (LSE: AMEC.L).

The IChemE is the hub for chemical, biochemical and process engineering professionals worldwide. With offices in the UK, Australia and Singapore, IChemE is the heart of the process community, promoting competence and a commitment to best practice, advancing the discipline for the benefit of society and supporting the professional development of 25,000 members.

For more information regarding GPC technology and Curvaceous visit www.curvaceous.com.  To contact Curvaceous email enquiries@curvaceous.com or phone +44 (0) 1753 893090.

For information regarding AMEC or the IChemE please visit www.amec.com or www.icheme.org respectively.

1. NEWS FROM ABROAD

This quarter Curvaceous has been receiving enquiries from around the globe. Germany is providing a wealth of excited prospects as our new German Agent moves into position. More news on this to come as well as the unveiling of our brand new German website.

Australia is producing some good results for Mode2. Technical papers are being accepted as soon as they are written for events over the coming months. First up is Finn Peacock teaching the Aussie's how to become Curvaceous at AIM05 (Australian Industrial Minerals Conference), 17-18 March in Sydney with "New Dimensions in Mineral Processing." Making waves across the process industries already Mode2 is capitalising on recommendations from UK sites. So if you have a sister company enjoying the sunshine while we're stuck with the snow, share the wealth and they could soon benefit from being Curvaceous too.	Australasian Agent
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2. THE POINT VALUE PREDICTOR

A universal predictor of product properties from process measurements

The basic concept of Geometric Process Control (GPC) technology is the Best Operating Zone (BOZ). As you would know, this is the multidimensional envelope of a set of operating datapoints, all of which represent good operation. The selection of the BOZ dataset is a skilled engineering activity, which is undertaken using a larger collection of historical operating data that fully defines the GPC model.

During online operation GPC uses the geometry of the BOZ and the current values of the online process variables to calculate limits within which all process variables must lie for satisfactory operation. These are dynamic limits that change at every time-step.

Now consider the case when some key variables – usually product qualities – are not available online, but are determined after the event, e.g. by laboratory analysis. We call these “Quality Variables”. Historical values of these variables are included in the BOZ dataset.

We know that as long as the online process variables are kept inside the BOZ (exactly what GPC does), the values of the quality variables will be within specification. We know this because the BOZ dataset was chosen to meet those criterion. Further, the multi-dimensional geometry of the BOZ, together with the current values of the online process variables, provide a predicted range for the current value of each quality variable which is narrower than the specification.

It is often useful to have specific point predictions of the current values of quality variables in addition to their ranges. To provide this, GPC makes use of information contained in the interior points of the BOZ. The historical datapoints that are “close” (in a multi-dimensional sense) to the current (measured) operating point contain additional information that can be used to predict the (unmeasured) values of the quality variables.

In addition to making these point predictions, GPC uses another quite independent technique to predict narrow limits on the values of the quality variables. These are calculated using the multi-dimensional envelope of an inner “sliver” of the BOZ dataset. These are not confidence limits in the statistical sense, since confidence limits are a uni-variate construct, but do indicate the accuracy of the value predictions.

The resulting plots of the values of quality variables predicted, as described, against the actual values obtained by offline analysis are shown. There are a number of parameters of the prediction calculation that are user-settable at present. None has been found to be critical indicating that they can be discarded. This leads to a predictor that does not require parameters and hence may eventually not require to be individually ‘fitted’ to data. The method does not require any equation fitting for its quality value predictions. It is completely different from the neural-net, statistical and chemometric methods traditionally used for empirical predictions.

For more information please contact us

3. ALL QUESTIONS HAVE ANSWERS

As mentioned in the last issue, Curvaceous has been working closely with Huntsman Petrochemicals and jointly produced some fantastic results. The Chemical Engineer magazine showcases the Huntsman-Curvaceous partnership in their new Question & Answer section. A concise insight into process problems and their solutions. See the evidence here.

Curvaceous has featured in magazines such as Hydrocarbon Processing, Process Control News Europe and MPT Ireland over the past few months.

 If you have had an experience you would like to share please contact us and we'll do our best to get your voice heard.
 

4. HINTS & TIPS

Here's a something new to try on visualExplorer
 
Visualisation of the Distribution of a variable

The distribution of data values for a variable can be of interest particularly if many points occupy single values or narrow bands.

To visualise this distribution, the technique is to first create a SortOrder variable of the variable of interest

(Variables > Add new function variable > SortOrder)

Then display a Scatter Plot of the new SortOrder variable against the variable of interest. The result being a plot of the cumulative distribution.

SortOrder is a multi-variate sort capability of visualExplorer; it does not re-arrange the data, but creates a new variable with values from 0 to n-1 for n points. It allocates the value 0 to the lowest ‘value’ multi-variate point up to n-1 for the highest. The sequence of variables in its parameter list has positional significance, thus performing a ‘lexicographical’ sort.

For this particular requirement we are only passing the single variable of interest to SortOrder to create a new variable with content corresponding to the sort sequence of the target variable.

A classic cumulative distribution plot has the SortOrder variable on the vertical axis on a scale of 0 to 1; this linear transform maybe simply effected by plotting against a derived variable of ‘SortOrder/max(SortOrder) as in the example plot below.

A range query on the SortOrder variable can be used to quantify population percentage within ranges of interest. Many data points of similar value overlaid on the parallel axis plot will become apparent as a steep gradient on the distribution plot.

Click to see a larger version of this image

This technique arose from suggestions of the GPC User Group.
Watch out for more handy hints and tips next time.
 

 
5. USE IT OR LOSE IT!

The GPC User Group is suffering due to a lack of use. Group Leader, Mike Tyrrell of INEOS Chlor, is requesting that people use the group or you all face losing this valuable independent resource.

Register now online via the GPC User Group webpage
and get involved!

Anything can be discussed from the latest tricks and tips for CVE to the state of the weather. The group is entirely independent and therefore acts as a good networking tool for people from around the process industries in several different countries. So go on, pick each others brains, get independent advice and meet new people trying to get the same solutions to their problems as you!
 

Watch this space for more news from the Independent User Group.

 

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