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Industrial Processes

Using AI intelligently

This project is a successful demonstration of how case-based learning (CBL), a type of artificial intelligence (AI), can solve process control problems in the pulp and paper industry. To develop the system, a British SME and a Belgian university, both experts in AI, worked with a large German manufacturer of process control systems. CBL takes a middle ground between AI techniques that need extensive training on historical data and others that demand an accurate mathematical model of the process.
Two other partners, a Portuguese pulp mill and a Dutch paper mill, have both made significant cost savings while maintaining product quality. The system will soon be available commercially and could be used by other industries such as food processing.

Industrial processes that use natural raw materials can be difficult to control. A mill producing wood pulp for the paper industry must produce a consistent product, yet wood varies in character throughout the year and often comes from different sources as market prices fluctuate. Pulp quality varies as a result. Paper mills using recycled paper rather than virgin pulp have the same problem.
To ensure that the pulp always meets customer specifications, the mill must set its standards a little higher. If there is high process variability this is expensive, for it means that for much of the time the mill is making pulp that is better than its customers need or will pay for. Paper manufacturers and food processors have the same problem.
Process control - the technology of maintaining product quality in process industries such as paper and chemicals - has recently started to use computer-based artificial intelligence (AI) to tackle difficult problems. AI has a long academic pedigree, but its successful application to real-world situations is problematic.
CLEAN is a highly successful attempt to bring AI to the pulp and paper industries using a new approach known as case-based learning (CBL). The project has linked two AI specialists, British Maritime Technology in the UK and the Catholic University of Leuven in Belgium; two user companies, and a control systems supplier, Siemens of Germany. The users are a small Portuguese pulp mill, Companhia de Celulose do Caima, and Roermond Papier, a Dutch paper mill that makes only recycled paper. Both have been delighted with the results and have experienced significant cash savings.

Problems with existing methods

Pulp and paper are competitive industries, and to gain a commercial advantage mills are investing heavily in advanced control techniques such as AI. Existing AI techniques, however, have disadvantages.
One example is neural nets, which can learn to control a process by studying past production records. Neural nets need little prior knowledge about the process but they do need plenty of 'training' data. This is not always available: the Caima mill, for example, includes processes for which full data may only be available once a week, so collecting enough information to train a neural net could take up to a year.
Another technique, 'fuzzy logic', is quicker to set up because its reasoning is pre-programmed using information collected from experienced human operators. Unfortunately this means that fuzzy logic systems must be carefully tailored for each site, and they may need to be re-tuned if the process or the raw materials change.
By the early 1990s Roermond engineers were looking for a control system that would help the mill cope better with changes in the quality of its recycled paper feedstock. They had already tried neural nets without success, so they asked control systems manufacturer Siemens to develop a better system.

CBL: the best of both worlds

Siemens, which has an estimated 80% market share for control systems in the pulp and paper industries, approached British Maritime Technology for help with more advanced AI. BMT specialises in marine engineering but has a history of successful AI research and development in other industries. Together with the Catholic University of Leuven and the Caima pulp mill, the CLEAN project started in 1993.
BMT and Siemens decided to use case-based learning, a technique that combines some of the flexibility of neural nets with the predictability of fuzzy logic. CBL uses past experience rather than a rigid mathematical model of the process, making it well suited to processes based on natural materials, yet it can also incorporate existing engineering knowledge where this is available.
The challenge for AI is to use the current state of the process to predict how the product quality will change as variations in raw material quality and process conditions travel through the plant from input to output. CBL does this by comparing the current set of process data with historical data and looking for the best match.
If the system finds a large number of past cases like the current one, and all these cases led to product of acceptable quality and cost, then it knows the process is on target. If all the past cases led to poor-quality or expensive product, the system steers the process towards conditions which it knows from past experience will produce better results. It does this by using the plant's existing low-level control system to change process temperatures, flowrates and motor speeds.
If the CBL system finds that the matching cases gave variable product quality, or that the current case has no precedent, it has to make an informed guess about how to get the process back on track. Experienced process operators are often wary of handing over control to a 'black box', but a CBL system has two features that help to win their confidence. First, it can explain how it arrived at its predictions, and if it lacks enough information to come to a decision it can say so - something other AI systems find hard to do. Second, it can be set up so that initially it never strays too far from known conditions. As operators gain confidence in its abilities, they can give the system more responsibility.

Installations and results

By the time the project ended in January 1996 both the Roermond and Caima mills had working CLEAN installations. The results were impressive. At Roermond, the factory's energy consumption per tonne of paper fell by nearly 9%, starch usage fell by more than 3% and water consumption was down by 24%. Product degradation fell by around 20%. Production staff also find that the new system makes it easier to switch between different grades of paper, so shorter production runs have become profitable.
At Caima, the basic yield of the pulp process has increased by 3%, with a simultaneous saving in steam of 1.4% and a reduction in the amount of reject material produced of more than 50%. Together these amount to a cost saving of more than 500,000 ECUs a year. Product quality has also improved. Caima is now installing a new CLEAN system in another process area.
In both plants the system has a modular architecture that simplifies software testing and maintenance. The modules run on a personal computer under Microsoft Windows, sharing data through a series of simple text files. The heart of the system is the Site Control Unit (SCU), a program that assembles, classifies and stores the operating 'cases' using plant data supplied from other parts of the system. Roermond has a single SCU; Caima has three, reflecting the more complex nature of its pulp process, which includes batch operations.
The other parts of the system are a module for obtaining raw data from the plant's low-level control system, a display unit for the process operators, and a modelling system. The latter, which was designed by the Catholic University of Leuven, helps the CBL system by predicting information, such as the results of laboratory analyses, that is not yet available.

A toolkit for the future

At present, the highly-customised nature of the Roermond and Caima installations means that CLEAN is not available as an off-the-shelf commercial product. This will soon change, however, because Siemens is working on a CBL 'toolkit' that operating companies can use to add CBL to their processes quickly and simply. Siemens plans to launch the toolkit in 1997 and BMT will share the revenues.
As well as pulp and paper, the companies will be targeting the fibreboard and food industries, for which CBL is also a good choice. Siemens and BMT have taken out two joint patents to protect their know-how. The next generation of AI is already on its way BMT is working on an even newer technique, known as conceptual clustering, that may produce even better results.



Project Title:  
Case-based learning environment for plant process management

Industrial and Materials Technologies (BRITE-EURAM/CRAFT/SMT)

Contract Reference: BE-5285

Cordis DatabaseFor more information on this project,
go to the CORDIS Database Record