Knowledge-based quality diagnosis

Knowledge-based quality diagnosis

1 } Delft University of Technology Faculty of Design, Construction and Production Man Machine Systems & Control Group Mekelweg 2 2628 CD Delft The Netherlands {a.c.trautwein & p.a.wieringa} @wbmt.tudelft.nl 2 ) Huntsman Polyurethanes, Rozenburg, The Netherlands

Abstract

For the polyurethane plant of Huntsman Polyurethanes an effort is made to monitor the product quality in real time so that intervention can be made in time to avoid deviations from the quality specifications. For this purpose a quality diagnosis system is developed based on knowledge-based fault diagnosis techniques. First a knowledge base is set up having a three-layer structure. For this purpose both knowledge from experts and system knowledge is used. The knowledge from experts is acquired using interviewing techniques, while the system knowledge is acquired from principles governing the process. The knowledge base is currently validated using an historical data analysis of quality data. The validation outlines that the knowledge base can be used for the quality diagnosis performing three tasks: 1. Detection of a deviation in product quality; 2. Qualitative predication of the deviation; 3. Indicating the causes.

Keywords: knowledge-based, knowledge acquirement, quality diagnosis, fault diagnosis

Introduction

Although safety and production goals have higher priority at a chemical plant, economie goals are also of importance. Product quality is directly related to these goals, because the products need to be within certain quality specifications in order to be acceptable for the customers. To achieve this economie quality goal operators receive information about the product quality, however this information is not always available. In such cases a quality diagnosis system that informs the operators about the product quality can be useful.

This paper discusses the development of a quality diagnosis system for a real plant of the chemical company Huntsman Polyurethanes. First the problem regarding the lack of sufficiënt information about product quality and its causes will be described. In the next paragraph the choice for knowledge-based techniques is founded leading to the goal which is to develop a knowledge-based quality diagnosis system. The development consists of the set up and validation of the knowledge base for the quality diagnosis system. The validation part is currently performed and conclusions are based on the current results.

Problem définition

At one of the Huntsman Polyurethanes plants in Rozenburg, The Netherlands, raw materials for polyurethanes are produced. The two final products, I and n, of the plant are both composed of the same components, a and b, but in a different ratio in order to receive different product properties (Figure 1.). The composition of these products is mainly determined in the plant's final process: a batch séparation process. The final products are both directly sold to customers who demand for a certain product quality related to the composition of the products. Product quality is expressed in terms of percentages of component a in both products as can be seen in the figure below.

séparation process

final product I final product LI

component a component b component a component b

\ I

%component a< threshold %component a> threshold igure 1. Quality spécifications for the final products.

he Separation process and the other plant processes are monitored by the operators, he condition of these processes can be monitored using on-line measurements of process data and trends in time of these data. However product quality can not be measured in real time having the conséquence that the quality can not be monitored directly. A possibility to monitor the quality might be to monitor process data that are directly related to product quality. But it is difficult to monitor déviations in these data, because the changes that cause a déviation in product quality are small. Besides,

he operators can not monitor the séparation process continuously because the rest of he plant also has to be monitored. In order to get knowledge about the product quality he operators take samples of both final products of which the composition is

etermined at the laboratory. Final product I is sampled every six batches (à 2.5hour) and final product IJ is sampled once a day. The conséquence is that when the product

uality déviâtes from its spécification, intervention is already too late.

hus the problem at the polyurethane plant is that the operators can not monitor the roduct quality in real time. The conséquence of this problem is that the operators can ot intervene the séparation process in time in order to avoid an unwanted déviation of he product quality. The problem is caused by a couple of facts:

1. the product quality can not be measured on-line

2. it is difficult to monitor process variables that are related to product quality 3. the operators can not monitor the séparation process continuously

conséquence ^mmm> can't intervene in time

t t

problem <^ ^> can't monitor quality

t t

^ ^ -difficult to monitor quality related process variables

-can't monitor process continuously

Figure 2. Problem définition including conséquence and causes.

Goal formulation

To avoid the problem concerning product quality as described in the previous paragraph, a quality diagnosis System has to be developed that informs the Operators

about déviations in the product quality. A first goal formulation is:

Goal formulation: development of a quality diagnosis system. The quality diagnosis system is developed for final product I. To achieve the goal fault diagnosis techniques can be used. These techniques are developed to detect a déviation of normal process behaviour and its causes early enough so that a failing of the overall system can be avoided [Gertler, 1998]. Fault diagnosis techniques can be used for quality diagnosis when detecting déviations in process variables that cause a déviation in product quality.

Within fault diagnosis two techniques can be distinguished: model-based and knowledge-based techniques. In model-based techniques the actual process behaviour is compared with the behaviour of a fault-free analytical model while in knowledge-based techniques expert and system knowledge about normal or faulty process behaviour in terms of rules and facts are used. Clearly a perfect analytical model represents the most concise knowledge of the process. However, précise analytical models are in practise hardly available [Frank, 1996]. Complex processes like the séparation process can not or at least not easily be described by mathematical models. In this case knowledge-based techniques are a realistic alternative allowing one to exploit as much knowledge as available; no füll analytical model is needed. So the final goal can be formulated as:

Goal formulation: development of a knowledge-based quality diagnosis system. The quality diagnosis system has to perform three tasks. The first task is to detect a

déviation in product quality so that the Operators can be informed about it. Secondly a qualitative prédiction of the quality déviation is needed, so that the Operators know whether the quality possibly déviâtes from its spécification (Figure 1.). The last task of

the quality diagnosis S y s t e m is that it has to indicate the possible causes for the quality déviation and when possible detect them so that the O p e r a t o r s have information about how to intervene the séparation process.

pproach for the development of a quality diagnosis System

The first step in the development of a knowledge-based quality diagnosis S y s t e m is the set up of the knowledge base for quality diagnosis performing the three tasks as described in the previous paragraph. In this phase one has to détermine what knowledge is required and how to structure the knowledge base. Finally the knowledge has to be acquired which is the main difficulty within knowledge-based diagnosis techniques [Castillo, 1997]. After the set up, the knowledge base has to be validated for which a historical data analysis will be used. This last phase is currently performed.

Set up ofthe knowledge base: The kind of knowledge that is required for the quality diagnosis system resuit s from the three tasks, which the diagnosis system has to

erform:

1. Détection of a déviation in product quality

2. Qualitative prédiction of the déviation

3. Indicating the causes

In order to perform the first task knowledge is needed about what process variables are directly related to product quality so that a déviation of thèse variables can be used for the détection of a déviation in quality. For the second task a qualitative model has to be found that qualitative predicts the déviation of the product quality based on the déviation of the process variables used for détection. To indicate the causes one needs knowledge about what provokes the déviation in product quality. When possible the cause has to be detected for which knowledge is required about what process variables are directly related to the cause.

The knowledge base can be structured with a tree structure having three layers each containing knowledge about one of the diagnosis tasks as can be seen in Figure 3.

Figure 3. Three-layer structure of the knowledge base including an example. 2.qualitative m o d e l f ™ ~ | f8" " ! " % c o m p o n e n t a T" 1 . d é t e c t i o n _{"température >threshold"} 3. c a u s e s "problem cooling System "température >threshold"|

"no flow cooling water" "cooling water too

hot"

All three layers contain knowledge formulated in simple rules with a gênerai form: layer gênerai rule

détection if process variable higher(>) or lower (<) then a threshold then conclude quality déviâtes

qualitative model if process variable higher (T) or lower(i) then % component a higher ( î ) or lower(sL)

cause: indicating detecting

- process variable higher(>) or lower (<) then a threshold caused by cause

-if process variable higher(>) or lower (<) then a threshold then conclude cause

Table 1. General rules used for the knowledge base

The knowledge base having the three-layer structure can be used easily for the quality diagnosis as shown in the example in Figure 3. The diagnosis starts when the détection layer detects a déviation in product quality using a déviation in a quality related process variable. In this phase an alarm can be generated in order to alert the

O p e r a t o r s . Reasoning upward using the rules for the qualitative model layer a prédiction is made about how the quality déviâtes. Reasoning downward using the rules for the cause layer causes are indicated and when possible detected.

The last step in the set up of the knowledge base is the acquirement of the knowledge. Two différent kinds of knowledge are used: knowledge from experts using unstructured and structured interviewing techniques and S y s t e m knowledge using principles governing the process. In unstructured interviews the interviewer asks questions to become familiär with the process, while in structured interviews the interviewer outlines specific goals and questions to obtain more detailed process knowledge [Boullart, 1992]. The experts used are Operators, process engineers and

process control engineers of the polyurethane plant.

For the knowledge acquisition the following procédure is used:

1. Unstructured interviews with experts to become familiär with the process and the product quality.

2. Set up knowledge base using principles governing the process.

3. Structurée interviews with experts for feedback of use knowledge base in practise.

n the first step not only a global picture of the séparation process and the product uality is received, but it is also evaluated whether the knowledge required for the quality diagnosis is present. After this step the first rough set up of the knowledge ase is made using process principles. This knowledge base is evaluated in the last step for use in practise considering for example the reliability of the instruments. The

rocedure finally leads to a knowledge base as shown in Figure 3 that is expected to e useful for quality diagnosis. However there are still some unsure elements in the nowledge base like roughly estimated thresholds. Before using the knowledge base n-line it is validated using an historical data analysis.

Validation of the knowledge base: The main goal for the validation of the knowledge ase is to evaluate the use of the knowledge base for quality diagnosis. The évaluation is dual, while both the possibility of the use of the knowledge base and the advantages f the use of the diagnosis System in comparison to the current situation are evaluated. or the validation of the knowledge base 60 days of quality data (% component a) are athered from the laboratory results of the samples of the final product I taken by the perators. Final product I is stored in three different tanks each having the ability to ontain six batches.

ithin the 60 days of quality data six cases in which the product quality is off pecification can be identified. Currently three of these cases have been validated. To chieve the validation goal for each case a couple of steps are taken. At first a case escription is made that describes the current situation of each case so that later on the rofits of the use of the diagnosis system can be evaluated. Each description contains nowledge about how the quality changes, whether the operators intervened the séparation process, how they intervened the process and why they intervened the rocess. The knowledge about the quality changes is acquired from the gathered uality data and the knowledge about the process intervention is acquired using structured interviews of the operators involved. The knowledge is collected and structured in tables.

or the next step in the validation procedure an historical data analysis is used. For ach case the historical data of the process variables in the knowledge base are athered. The historical data are coupled to the quality data in order to evaluate the se of the knowledge base for quality diagnosis.

he results after the validation of the three cases are:

The knowledge base can be used for quality diagnosis performing all three diagnosis tasks:

1. Détection of a déviation in product quality 2. Qualitative prédiction of the déviation 3. indicating the causes

- For some process variables the thresholds used for détection in both the détection layer and the cause layer of the knowledge base are too sensitive. These thresholds are adapted. The historical data analysis is thus usable for knowledge acquirement of more accurate thresholds.

- In ail three cases the diagnosis System could detect the quality déviations earlier then in the current situation, because in ail cases the operators noticed the déviation based on samples taken afterwards, while the process variables used for the détection layer in the diagnosis System deviated earlier.

- Information about the causes of the quality déviation supports the operators in the décision of when to intervene the process. For example in one case the operator intervened the process based on a sample of the final product, however the cause of this déviation already solved itself. Thus the intervention was unnecessary. - Information about the quality déviations from the qualitative model supports the

operators in the décision of how to intervene the process.

These validation results lead to the conclusions that are described in the next paragraph.

Conclusions

By using the knowledge of experts and system knowledge it is possible to develop a knowledge base that can be used for the quality diagnosis. The three-layer knowledge base enables the diagnosis system to perform all diagnosing tasks:

1. Détection of a déviation in product quality 2. Qualitative prédiction of the déviation 3. Indicating the causes

Main difficulty in the set up of the knowledge base is the détermination of the thresholds for the détection of both quality déviations and its causes. More accurate thresholds can be determined using historical data analysis of the process variables involved.

The knowledge based quality diagnosis Systems improves the current situation concerning the product quality for the polyurethane plant, while déviations in the quality are detected earlier. Information about how the quality changes and what causes the change supports the operators in their décisions about process intervention. Currently the knowledge base is validated further and when necessary the thresholds are adapted to achieve a knowledge base that can be used on-line for quality diagnosis at the polyurethane plant.

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