ELSEVIER Computers in Industry 34 (1997) 161-172

Future directions of R & D in the process industries

Jim Anderson *

Anderson-Barr Consulting, 12 Burniston Drive, Billingham, Cleveland TS22 5DD, lJK

Abstract

The process industries in the Western World have moved from a position of power and wealth in the 1960s to a struggle for survival in the 1990s. Part of this at least can be attributed to a complacent belief in the economies of scale and an unwillingness to invest: in emerging technologies. Recently there has been a reawakening; companies are cutting costs and repositioning themselves in the market place. In parallel, there has been a realisation that this is not enough and that modem control technology has much to offer. Recent developments in process control are having a dramatic effect on process operations. Techniques such as model-based predictive control, inferential measurement and multivariate SPC are becoming well-established. The emergence of modelling based on ‘intelligent’ methods such as neural networks and genetic algorithms underpin some of the advances. The benefit of effecting integrated plant design has been amply demonstrated and the capital productivity which results is additional to operational productivity improvement. Use of computers is fundamental to all of these advances. Advances in measurement technology are also striking and many lessons can be learned from progress in other fields, such as the motor industry where extensive use of cheap sensors and on-board computers for monitoring and control has transformed the motor car in terms of comfort, reliability and economy although the basic design has not fundamentally changed since the early days. In this paper it is argued that the return on investment of the innovative application of control and measurement technologies is very large. The more forward-looking companies are investing heavily. There is still further potential in some of the ideas emerging from academia and other industries. All of this will have a fundamental effect on the future shape of process control and how control engineers will be employed in the future. 0 1997 Elsevier Science B.V.

Keywords: Control; Prows; Profit; Investment; Future

1. Introduction

Until the late 196Os, the process industry enjoyed a spectacular growth driven by rapidly expanding world economies and inventions in plastics, man- made fibres, pharmaceuticals and the like. Profits were easy to make (although nobody thought so at the time), and the drive was to build ever more

* Corresponding author. Tel.: f44 1642 551211; fax: +44 1642 551211.

capacity worldwide. At the time of the onset of the recession in the late 197Os, it was calculated that the world overcapacity in ethylene manufacture meant that no new plant would be needed until 2025! In

this dash for growth, manufacturing efficiency had not been seen as critical to profit performance and plants were designed for steady-state operation. This usually wasn’t a problem since most plants ran flat out for month after month. Plants were normally provided with significant intermediate storage, and measurement and control systems were simple and

0166-3615/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved.

PII SOl66-3615(97)00052-3

162 J. Anderson/Computers in Industry 34 (1997) 161-172

often required significant manual intervention. Alarms and trips were provided to deal with the

unknown. The consequence of this industrial climate and a

belief in the economies of scale led to overengi- neered but inflexible plants. High stocks were held to satisfy customer demand because plants could not quickly and reliably change products or grades. Make for stock was the operating philosophy and since it was easy to blend to achieve specification, holding stock was seen as a virtue. However, it masked fundamentally poor control. Most of these plants are

still in operation today.

2. Today

Environmental and economic pressures are forc-

ing management to look very critically at the opera- tion of existing assets. Companies are cutting costs and are targeting strategic dominance in selected markets by rationalising their product portfolios. The market place is also having an increasing impact on manufacturing in many ways. With consumer prod- ucts, the customers effectively dictate production schedules and so the supplier must be in a position to respond to this demand without holding massive

stocks. Much of industry has concluded that it can’t solve

its problems by cost-cutting alone. Where the tech- nology base is weak, it will have to look outside for help. A number of recent papers have argued that advanced process control and measurement technol- ogy can have a dramatic effect on company prof-

itability. Unfortunately, for the few managers who have thought about it, advanced process control con- jures up visions of wall-to-wall computing or pages of impenetrable matrix manipulation. Managers, who have always been suspicious of the specialists’ claims for their favourite technology, are even more leery of similar arguments today. They find it is far easier to understand and approve major and very expensive mechanical and civil engineering projects.

3. Credibility gaps

Why is there a lack of comprehension of the needs of industry by many academics and of the

power and potential benefit of process control and

measurement technology by management? In a perceptive and wide-ranging dissertation to

the United Kingdom Institute of Measurement and Control in his 1992 Thomson lecture, entitled ‘Mea- surement, knowledge and control of nearly every- thing,’ Robert Malpas, Chairman of Cookson in the UK, pointed out that Britain was under invested in people, assets and technology. Productivity is the key to future success, and measurement and control can contribute greatly to cost reduction. Maybe this is also true in continental Europe. Quoting Malpas, “Most businessmen do not live comfortably in the domain of technology.. .‘I It is up to the technolo- gist to bridge the gap between business and technol-

ogy. And so it is; we, technologists, just haven’t been

very good at it. How does the successful salesman go about selling anything and have the customer come back asking for more? He does this by making sure he understands his customers’ needs. The academics must find out what the customers need from their control theories. Similarly, industry-based technolo- gists must find out what their business needs are and tailor their offerings appropriately. In the future, in

both cases, profit will feature high up the list. Indus- try is not interested in the latest technological offer-

ing, be it a smart sensor or the latest distributed control system, unless its money-making potential is clear and demonstrable. The potential is there but one of the key requirements is education,

4. Operations

It is evident that much of the process industry is reluctant to contemplate the huge investment neces-

sary to build new plant and prefers, where feasible, to look at ways of extending the life and profitability of existing assets. How can the life of a steel-making furnace be extended? How can production from a chemical reactor be maximised without compromis- ing its operational life? Measurement and control has much to offer.

A study carried out in 1987 by the Warren Centre of the University of Sydney surveyed a number of Australian process industries to determine the bene-

J. Anderson/Computers in Industry 34 (19971 161-172 163

Years to return investment

51 I

”

0 0.3 0.6 09 1.2 1.5 1.6 2.1 Years

Fig. 1.

fits of advanced control [l]. A number of case stud- ies are reported which found benefits ranging from

2-6% of the annual operating costs. These studies covered a range of industries from metal processing through waste treatment to the chemical industry. The results were comparable. More recently, Du Pont in the United States commissioned a study into the practices of the ‘best of the best’ competitor companies in proces,s control [2]. Those which were properly exploiting tihe potential offered by the tech- nology were achieving benefits averaging 15% of manufacturing costs.

Clearly there are costs associated with such a spectacular improvement but experience suggests that

most costs from control-improvement projects are recovered well within two years - often in a much shorter time [3] (Fig. 1). There is no doubt that industry should pursue recovery of this potential profit very actively. ‘This will have a profound impli- cation for the vendor and consultancy industries.

4. I. Model-based predictive control

One of the most successful control techniques to evolve in the last decade has been model-based predictive control (MBPC). This is particularly well suited for retrofits and there are some striking exam- ples in the literature documenting the huge benefits which this technology can provide [4]. Figs. 2 and 3 show the effectiveness of an MBPC application in ICI. Fig. 2 shows the remarkable reduction of vari- ance compared with the best that conventional con- trol could do. This is undoubtedly a significant tech-

nical achievement, but is unlikely, of itself, to im-

press a sceptical budget manager. Fig. 4, however, might well do so. This shows the energy costs per tonne of product before and after the application of the improved control system. The benefit was around

7% saving in energy from operation closer to con- straints.

This form of control is now commonplace in some of the more advanced organisations. The next few years will see many more applications of this technology which takes account of and capitalises upon the time delays in the system to allow plants to operate close to constraints. This form of control is

being integrated into site-wide hierarchical control

systems ranging from the plant sensors through real- time optimisation to production scheduling and busi- ness planning. Experience with the existing linear techniques suggests the need for nonlinear methods and for data analysis techniques to evaluate and alert management to any developing mismatch between model and plant. Closer observation of Fig. 3 shows three distinct modes of operation before MBPC was applied, following MBPC initiation and then a much tighter period after about December 8. During the initial MBPC operation (December 4-8), the poorer

operation was due to the model being based on test data gathered during the summer months when the

plant was processing a different feedstock. The plant was re-modelled during this initial MBPC period and

the new model brought onto line with the resulting improvement.

Two explanations are possible, either the plant characteristics had changed or the plant is nonlinear in its response. For tighter control, plant models may need to take this into account. Two solutions are possible, one is to adapt the linear model parameters or to develop a nonlinear model and apply nonlinear MBPC. Where a process is highly nonlinear, adapta- tion of a linear model is unlikely to be satisfactory because of the necessity for effective control system jacketing. The consequences of the failure of such systems has meant that their use is far from common. In a recent study, the performance of four controllers were compared by application to pressure control of a high performance distillation column [5]. The four controllers were a detuned PI controller, a high-gain PI controller, a linear model-based predictive con- troller and a nonlinear neural network-based predic-

164 J. Anderson/ Computers in Industry 34 (1997) 161-172

1 LEVEL CONTROL b 100

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J. Anderson/Computers in Industry 34 (1997) 161-172 165

1 ENERGY COSTS 1

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-16 -12 -a -4 0 +4 +a +12

THROUGHPUT

Fig. 4.

tive controller. In practice, the high gain controller would have caused too much actuator disturbance

and would have been inoperable. Fig. 5 compares their respective performance.

Progress in all rhese areas is facilitated by the availability of low-cost computing and appropriate

software. Soon, such integrated systems will be the norm for large-scale chemical manufacture.

4.2. Inferential measurement

In traditional chemical manufacturing, many products are being produced and sold not for their

Closed Loop Controller Comparisons 1.52

Detuned PI Control Linear MBPC

,,5,: s/d=;.0091 , High Gain PI Nnet MBPC

Std = 0.0060 Std = 0.0038 Std = 0.002!

a, 5 1.50-

: 15 1.49-

E 1.48- 3 .- 0 0 1.47..

1.46-

0 500 1000 1500 2000 2500 Go0

Time (Minutes)

Fig. 5.

166 J. Anderson/Computers in Industry 34 (1997) 161-l 72

chemical composition but rather for their ‘effect.’ Such an effect is often physical and may for a polymer, for example, be related to its extrudability or, perhaps, ‘stickiness.’ Making on-line measure- ments of such characteristics is often difficult and can take a long time. Recent developments in ‘soft- sensing’ or inferential measurement are showing great promise and can allow the primary or critical mea- sure quality to be inferred at the frequency of the

secondary more easily made measurements [6]. These techniques are particularly helpful when it is neces- sary to monitor and control a process closely during,

for example, a grade change. Fig. 6 shows the prediction of a polymer quality

measurement using neural-net modelling. A number of very easily made process measurements were used in the prediction. The laboratory measurement is difficult to make and takes a long time. The predic- tion leads the ‘true’ laboratory measurement by sev-

eral hours. The tighter control which the operators can sustain, because of the insight into plant opera- tion which such a prediction of quality provides, has, in one application, halved the production of off-spec material during a product change. There is little

doubt that the next few years will see a dramatic increase in the application artificial intelligence tech- niques such as neural networks and genetic algo-

rithms to the determination of quality variables and the direct control of material properties.

4.3. Multivariate statistical process control (MSPCI

and fault prediction

Statistical process control charts such as the She- wart chart and CUSUM plot are well-established, statistical tools for monitoring the behaviour of a

process based on monitoring a small number of quality variables. These charts compare current per- formance against process behaviour when the prod- uct being produced was known to conform to the

customer/factory specification. Detection of the pro- cess moving outside the ‘in-control’ limits is identi- fied using well-established techniques which assume that data is both normally distributed and indepen- dent. This approach has been successful in a wide range of manufacturing industries for diagnosing

nonconforming production. Recently, the process industries have embarked

on major data collection programmes. With comput- ers hooked up to the process, massive amounts of

data are being collected on perhaps hundreds of process variables. Temperatures, flowrates, pressures

and so on, are available every second. Final quality variables, such as colour, texture or strength, are also collected, although much less frequently. Applying

40 /

Filter-based Neural Network Model of Polymer Quality

5 ‘,‘I’/‘l’,‘,‘,‘I’I’,‘,

0 20 30 40 50 60 Time (Scaled Units)

Fig. 6.

J. Anderson / Computers in Industry 34 (1997) 161-172 161

Sample Number

Fig. 7. Quality control of two variables (y, and y2) illustrating the

misleading nature of univariate charts.

univariate SPC techniques to these data is fraught with difficulty. Most SPC methods are based on charting only a small number of variables, usually the final product quality variables and examining

them only one at a time. This is totally inadequate

for most modem processes. All data should be used to extract information but there is a real problem with ‘information overload’ from the presentation of data on many charts. Furthermore, the univariate approach ignores the fact that not all the variables are independent of each other. Only a few underlying events are driving the process at any time and all these measurements are simply different reflections of these same underlying events.

The difficulty of using independent univariate control charts can be seen by reference to Fig. 7 [7].

The ellipse represents a contour for the in-control

process. Here, only two quality variables ( yi and y2) are considered for ease of illustration. Inspection of the individual Shewart charts clearly shows the process to be in a state of statistical control and none of the individual measurements is outside the upper and lower limits. The only indication is that a cus- tomer has complained about the product batch marked, 8, although many other values of yI and y2 are further from the mean. With only univariate charts, one would be confused. However, the multi-

variate chart y,-vs.-y, plot clearly shows that the product was outside the confidence limits.

The two techniques most usually applied to the solution of this problem are the principal compo- nents analysis (PCA) and the projection to latent

Scores for PC# 1 versus PC# 2

51 I -8 -6 -4 -2 0 2 4 6 8

Fig. 8. Scores plot for principal components 1 and 2 with nominal region defined using Hotelling’s statistic.

168 J. Anderson/ Computers in Industu 34 (1997) 161-l 72

structures (PLS). PCA is a tool which reduces the dimensionafity of the original data by defining a set of new variables, the principal components, which explain the maximum amount of variability in the data. The new variables are linear combinations of the plant measurements and are constrained to be orthogonal to each other. The first component ex- plains the most variability, the second, the next

greatest amount and so on. It turns out that only a few principal components (typically two or three)

can adequately capture most of the variation of interest. Thus it is possible to reduce a multivariable problem to one of, say, two dimensions. An example is shown in Fig. 8 for a plant with this underlying dimension of two.

This plot is derived from an Exxon polythene reactor simulation which has been validated against plant performance. The plant is 1 -km long and oper- ates at 3050 bar. The plot shows the development of a fouling fault. The period of good control is indi- cated by the * symbols and poor control by the +

symbols. The 95% and 99% confidence bounds, as in univariate theory, are based on the assumption that all of the data is normally distributed. More recent work allows more precise calculation of the confi- dence bounds [8]. Another useful plot is the squared prediction error (Fig. 9). The error is approximately zero when the plant is operating normally but in- creases rapidly as the plant moves out of normal

operation. Once it is detected that the process is moving out

of control, it is necessary for the operators to check on the behaviour of the observable plant variables. This can be done by use of contribution plots which show the values of the observation variables for points inside and outside the confidence bounds. The

change in the observable plant parameters is thus highlighted. Techniques such as those described are

allowing data gathered from plant to be turned into information and that information used for more effi-

cient and safer operation.

5. Integrated design

Over the fast 10 yrs, the use of computer-aided control system design tools has become widespread. These packages help in all stages of the control system design process. From modefling and simula- tion, to design, prototype testing, real-time code

generation and documentation, many process indus- tries need to shorten the lead times of projects to capitalise on commercial advantage such as patent protection and also need to look to an integrated design approach. This is particularly true in the pharmaceutical industry.

The improvements in capital productivity which can be achieved when an integrated and innovative approach to plant design is taken are startling. In a recent case, it was decided that only 1% of the plant capital items are needed to be above ground level.

Principal Component 2 Principal Component 1

Fig. 9. SPE plot for a fouling problem. ( * ), nominal data; (+ ), data from process malfunction.

J. Anderson/Computers in Industry 34 (1997) 161 -I 72 169

Conventionally, the figure would have been much higher. In another case, the integrated approach led to a plant design with less than half of the main plant items compared to the last conventionally designed similar plant and in one section of the same plant less than 10% of the conventional steelwork. The reduction in capital cost was dramatic!

At present control expertise is usually brought in

at the final design stage of a project, only to discover that the plant performance could probably have been improved with an alternative and equally acceptable

design. In future, controllability assessment will also

quite naturally become part of the early conceptual design function.

Control does not hold a monopoly in the com- puter-aided design stakes. Packages for computa- tional fluid dynamics, finite elements, CAD/CAM, etc., are common place, and may be found in indus- try and most undergraduate engineering courses. What is missing to allow more widespread applica- tion is a unifying desi.gn philosophy, a framework for the various packages that will enable a designer to

move easily from one package to another and bal- ance the conflicting d.esign requirements. By employ- ing AI techniques, the designer will be able to produce an optimised compromise between, material selection, control, manufacture, or whatever else the product requires.

Future emphasis for process plant will be on flexible designs featuring much lower capital and operational costs. New processes will involve fewer stages and have smaller inventories. There will be more recycling to increase operational efficiency and to remove unwanted by-products. There will be more

extensive heat integration. The plants will be physi- cally smaller with less intermediate storage. The

concept of ‘sealed for life’ units where small plants for the manufacture of, for example, sulphuric acid or chlorine delivered. to the customer on the back of a truck will become a reality in the near future. The transportation attractions are immense from an envi- ronmental viewpoint; chlorine is highly toxic but the

raw material for manufacture is electricity and brine! Necessary developments are many but will include embedded control and safety critical systems incor- porating sensors, actuators, hardware and software within a secure and agreed framework of interna- tional standards.

6. Modelling

Fundamental to advanced systems integration is a modelling base that will permit a seamless transfer from one design, or manufacturing tool, to another. For any given system it is possible to think of a minimal global data set, on which the structural design, the manufacturing process, control, opera-

tional management, and the like, are all based. Given that computers can easily handle and manipulate data, it is logical to re-examine these functions in

terms of this data set. Starting with a system’s minimal global data set, it is seen that the first stage of system integration is the development of a ‘uni- versal model’; a single model that can accommodate the various design packages. For control engineers, simulation and control system design is inextricably linked with the modelling process. However, the lack of a common model base means that even such closely related packages cannot communicate with each other. What is required is some new technology that will permit the development of a ‘universal model.’

Graphical user interfaces, like the windows envi-

ronment on a PC, or the standard plotting routines found in many control packages, are common place. Such interfaces simplify the communication prob- lems and make computing accessible. At the element level, model building needs to be object-orientated, with pipes, valves, pumps, heat exchangers and whatever else makes up the system, being connected together as required in order to produce a (physical) three-dimensional representation of the system. With large scale systems standard modelling packages with ‘units’ like, distillation columns, reactors and steam

generation plant, will become the norm.

7. Measurement [91

7.1. The market

It is a confident prediction that the market for sensors will grow significantly into the next millen- nium. That sensors now offer excellent value for money is reflected in their widespread use in the automobile and domestic ‘volume’ markets. Added

170 .I. Anderson/Computers in Industry 34 (19971 161-172

to this is the range of sensor types which can be incorporated into even the simplest process plants. The negative aspect of this optimism is, however, the need to offer ‘more for less’-sensors need to be cheap, reliable, need low maintenance, work in ad-

verse conditions and not interfere one with the other! The anticipated sensor types representing the

2lst-century market are those for the basic physical

parameters, e.g., pressure, temperature, accelerome- ters and vibration sensors are a powerful growth market. This is primarily due to their use in automo- tive air-bags and suspension systems. Further, flow sensors are in increasing demand for the automotive and medical sectors. Magnetic sensors are needed for throttle control and shaft speed and gas sensors are required for emission control in a wide variety of contexts. Chemical sensors are in many areas still in their infancy and the performance is often less reli- able than is required. However, the technology will

rise to meet the challenge driven by public pressure and legislative demand. Biosensors is another area of potential expansion as the technology develops to meet demand in a cost-effective way.

7.2. The technology

The expansion in the use of sensors in recent years has, in large part, been driven by the success of the technology. This has not just been a reliance on modification and improvement of ‘tried-and-tested’

sensing methods alone, but the growth of new tech- niques such as silicon technology. It has been possi-

ble to produce, first for the more demanding aerospace market and then more widely, a range of sensors, and subsequently to engineer them for a pound or two per item for the automotive and do- mestic market. This is a technological achievement which should not be underestimated in importance.

Today’s motor car is a moving sensor platform with multiple on-board computers. Only a few years ago, the only automatic control system on a motor car was the distributor advance and retard effected by a simple vacuum tube from the inlet manifold. Now engine management and control systems are fully computerised, multivariable and model-based. Braking systems are also computer-controlled as of- ten are suspension and traction control systems. Many quite modest vehicles now boast six or seven com-

puters and a multitude of inexpensive sensors that could not have been obtained at any price a genera- tion ago. And yet the cost of all the advanced measurement and control technology probably does not exceed El000 per vehicle but the effect on reliability, performance, comfort, safety and the en-

vironment is incalculable. No major motor manufac- turing company would have survived had it not

embraced this technology. In the home a generation ago the only

sensor/control system you would have found was the simple bimetal thermostat. Today, the home can be automated to an extent that would have been unbelievable only 20 years ago. The communications revolution has been driven by the rapid development of the optical fibre and optical communications-a crazy idea in 1966, an experimental reality in 1976 and a major transmission technology in 1986. It is easy to predict the continued growth situation in

1996, based on what is happening around us now, but 2006 and beyond really does offer both potential and challenge for measurement systems and the busi- nesses which use them.

The biosensor/chemical sensor markets offer par- ticular challenges to the process industry as a range of optical phenomena may be used to identify uniquely certain species such as environmental pollu- tants. Such species can yield highly distinctive ‘fingerprints’ using optical effects such as absorp- tion, Raman and Briilouin scattering, fluorescence, etc.

As sensors develop and proliferate, the ‘data ex- plosion’ they will produce emphasises the need for data integration and fusion. Incorporation of intelli- gence within the sensor itself is particularly likely with solid state sensors. Developments in sensor fusion will expand into the next century to make better use of data using computer-based methods, expert systems and logical modelling for the benefit

of the user of sensory data.

8. The future for control engineering

Control engineers, for understandable and quite natural reasons, have tended to concentrate on hard- ware loops. However, improvements gained from these challenging control problems will often have a

J. Anderson / Computers in Industry 34 119971 161-172 171

negligible effect when dealing with the larger sys- tem. For example, in an attempt to improve aircraft fuel efficiency, is it better to concentrate control effort on advanced engine management systems yielding perhaps a 5% or 10% saving, or in improv- ing air traffic control systems? The answer to this question is obvious, given the needless and avoidable waste of fuel incurre:d in queuing, e.g., an aircraft waiting to land at busy airports like Heathrow. Simi- larly, designing a robust system to predict plant

malfunction in a robust and reliable way may pre- vent plant shut-down or more major disasters and thus make greater savings.

Understanding the larger system requires a knowl- edge of both ‘hard’ and ‘soft’ factors. In this context ‘soft’ factors include the systems management, mar- keting and sales functions. Meeting the demands of customers and business objectives, (‘soft’ control), is as valid as designing an advanced model-based pre-

dictive control system and may be a necessary pre- cursor to convincing management for the need for such a system! Further the adaptive nature of the soft system, and the poorly understood causal relation-

ships between demands and responses, make it an area in which future control engineers should have

much to offer. Reference has already been made to the increas-

ing need to respond to customer sales and specifica- tion demands while keeping stocks at a low level. This is particularly true for manufacturing close to the consumer end of the chain, e.g., paint manufac-

ture for the domestic market. Here the process indus- try’s main customers are the large supermarket and DIY chains. They are demanding rapid, reliable de- livery at very short notice with trucks stacked so that

the various products can be transferred to the sales points in the most efficient way. This has a close analogy with the production partnerships set up by motor manufacturers such as Nissan where, for ex- ample, seats are ordered and manufactured at less than an hour’s notice and are delivered on-line by the upholstery manufacturer in the correct sequence to the precise point required on the production line. In comparison, in the process industry stock holding, is typically around 610 days and can be three times that long [lo]. The once-off and on-going financial benefit of developing methodologies, be they in mea- surement, control or management information, lead-

ing to a reduction in stock holding is very signifi- cant.

There are some encouraging signs that the indus- try is beginning to take notice of these dramatic figures but for real progress a new partnership be- tween academics, the user and supplier industries will be required.

9. Leadership

Where is the leadership in process control today? Once, the great chemical companies provided leader- ship, direction and, when necessary, new process measurement and control products. One of the first process control computers in the world (now in the

Science Museum, London) was installed by ICI on a small plant in England. ICI had its own Central Instrument Research Laboratory. Much pioneering work was done in measurement, control and comput-

ing. The Laboratory Director wrote the first book on the application of process control [ 111. ICI developed Media, an electronic interface system, RTLS, a real- time language, MTS, a multi-tasking system and pioneered work in batch reactor control and eco- nomic optimisation

In many ways companies such as ICI set the technological agenda. Now the major chemical com- panies, have slashed their R & D budgets and drasti- cally reduced their engineering presence.

The initiative has, to an extent, been taken by the manufacturers. Manufacturers, anxious to sell their ‘boxes,’ are packaging them in new ways. First, they are offering ‘solutions’ very much based on en-

hanced profit. This is exactly what the industry wants to hear. These solutions can cover the whole range of services from initial opportunities-and-be- nefit analysis studies, sometimes offered as a loss- leader, through detail design and installation to on- site maintenance. Second, they are offering innova- tive financial deals to make it attractive to the manu- facturing companies to invest in the new technology [12]. All of this is very welcome, but will it last? In many ways these moves are defensive; the advent of Fieldbus standard communication protocol means that it will not be necessary for the users to buy a monolithic instrumentation system from one manu- facturer. You can buy your intelligent control valve

172 J. Anderson / Computers in lndusiry 34 11997) 161-l 72

from one supplier and your temperature transmitter from another. However, if you buy a ‘solution,’ you

probably will buy all the instrumentation from one manufacturer, particularly if you are also tied into an innovative financial deal.

The downside to this is that, at best, you can only be as good as your competition-if you want com- petitive edge, you won’t get it by going down this

route. Companies find it increasingly difficult to be innovative on their own; the cost is just too great and the solutions too complex. Think of the analogy with a fighter aircraft. 50 yrs ago, one could be built in a few weeks, even days and relatively cheaply. Now what is the relative cost and build time of the

Euro-fighter? The same is true of high-technology industrial solutions in the process industries.

EU funding is having a major influence on re- search. It is stimulating cooperation between organi- sations across the old national borders. Elsewhere,

we are seeing the emergence of collaborations be- tween industry and academia in industrial/academic clubs and networks which is encouraging the cross- fertilisation of ideas and providing financial leverage for research and development projects.

Acknowledgements

The author gratefully acknowledges the many dis- cussions with industrial associates, particularly in ICI, which have contributed to ideas presented in this paper and, more recently, to the stimulating input from colleagues in the Department of Chemical and Process Engineering at the University of Newcastle.

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bounds for multivariate process performance monitoring

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Jim Anderson is an engineering consul-

tant with long experience in the chemi-

cal industry where he is particularly in-

terested in applying technology for im-

proved profit. He worked for ICI for

over 30 yrs and was the company’s

Senior Consultant in Process Control.

He is skilled in conceptual process con-

trol design and in troubleshooting to

solve operational problems. Three years

ago he set up his own independent con-

sultancy company, Anderson-Barr Con-

sulting, to provide services in process control. Clients include

BASF, British Steel and ICI. He is a Visiting Professor at the

Universities of Newcastle and Glasgow. He is currently President

of the Institute of Measurement and Control.