an intelligent system for respiratory protection

Pergamon Expert Systems With Applications, Vol. 11, No. 3, pp. 309-322, 1996

Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved

0957-4174/96 $15.00+0.00

PII: S0957-4174(96)00047-4

An Intelligent System for Respiratory Protection

JAMES LIu AND YUNG KIN WAI

Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

Abstract--This paper presents an intelligent system which was developed for helping end-users choose suitable respiratory protection and estimate the service life of a respiratory protection system according to circumstances in different workplaces. In traditional rule-based expert systems, users are required to input system information completely before any respiratory protection recommendation can be obtained. If the particular user does not know any of the required information, the system would do almost no benefit to him. The present system incorporates with some artificial intelligent (AI) elements, such as fuzzy logic, genetic algorithm and case-based reasoning, to provide flexibility and versatility for general users. With a step-by-step question-and-answer interaction mode, the intelligent system would be able to elicit as much input as the user knows during processing. For some unknown information, the developed system would use fuzzy logic to guess their values based on the user's perceptions. In addition, for incomplete input, the system would use a genetic algorithm and case-based reasoning to estimate the service life of some recommended respiratory protection system. It can generalize from similar cases and provide an updated recommendation for later applications. Copyright © 1996 Elsevier Science Ltd

1. INTRODUCTION

IN MOST INDUSTRIES TODAY, under normal operating conditions, there are few routine manufacturing tasks that require the use of respiratory protection. Through effective and efficient engineer control, workers should be exposed to the lowest feasible exposure levels. However, there are tasks that may require the use of respiratory protection because of the technological or economic infallibility of other control strategies. The following are examples where respiratory protection should be used (Birkner, 1991):

• Maintenance operations that would or could result in an unmitigated exposure.

• Confined space that may be oxygen deficient or potentially lacking of oxygen.

• Manufacturing operations that involve uncontrolled, or poorly controlled, aerosols, vapours or gases.

• Confined space that may have a potential to build up a high concentration of toxic airborne substances.

• Emergencies requiring escape from or entry into contaminated areas or areas where the concentration of air contaminants is unknown.

• Fire fighting.

For all these cases, choosing a suitable and cost-effective respiratory protection system can help users protect their health and reduce operation costs of the work. This paper consists of six sections. The next section describes why

an intelligent system is required to help people choose respiratory protection systems. Section 3 shows the use of AI methodologies including rule-based reasoning, fuzzy logic, genetic algorithm and case-based reasoning which are being integrated to empower the intelligent system for problem solving in complicated domain. Section 4 describes individual functions of the system and how they work. A case study will be given in Section 5 to demonstrate its applications under various operating conditions. The last section discusses the limitations and provides some suggestions for future improvement of the system.

2. BACKGROUND

People have become more aware of work safety and more concerned about industrial hygiene, They are now giving more attention to the safety practices of respiratory protection in their workplaces. Currently, there exist many types of respiratory protection system in the market serving different purposes. The many kinds of classification methods that can be used to classify respiratory protection systems include air purifying and supplied air systems (Rajhans & Blackwell, 1993). The air purifying respiratory protection system is a system that filters the surrounding air for the wearer's respira- tion. On the other hand, the supplied air system is a system that provides breathable air from a remote or independent air source. In this paper, the latter type of

309

310

respiratory protection system classification method is employed.

Since each type of respiratory protection system has its own benefits and limitations, to obtain adequate and cost-effective respiratory protection, users are required to consider many factors before any respiratory protection system should be selected for use (Johnston, 1991; 3M respirator selection guide, 1990).

2.1. Oxygen Content in the Workplace

Oxygen is an essential element for a human being; nobody can stay or work in any place that has a lack of oxygen, less than 19.5% by volume. For those places that contain insufficient oxygen or have a potential lack of oxygen, a self-contained breathing apparatus (SCBA, a kind of supplied air system) is the only choice for people who need to enter or work in there. No other type of respiratory protection system can provide secure and controllable oxygen supply besides SCBA.

2.2. Identity of the Airborne Contaminant(s)

By knowing the identity of the airborne contaminant, we can understand its physical, chemical and toxicity natures:

• Permissible exposure limit is a guideline for determin- ing the toxicity of the substance. There are many organizations which publish such values based on past experience and laboratory testing data. For example, Threshold Limit Value (TLV) from the American Conference of Governmental Industrial Hygienists (ACGIH), Permissible Exposure Limit (PEL) from the Occupational Safety and Health Administration (OSHA) and Workplace Environmental Exposure Level (WEEL) from the American Industrial Hygienist Association (AIHA) are some well-recognized exposure guidelines in industrial hygiene applications. For people who have been exposed in places where the substance concentration is above the exposure limit, they are most likely to be subject to some long- or short-term adverse health effects.

• Some substances possess an Immediately Dangerous to Life and Health (IDLH) value from the National Institute for Occupational Safety and Health (NIOSH). Contaminant concentration above this value will cause permanent adverse effects on health or be even fatal to people who stay in the area for a short period of time. Similar to oxygen deficiency, SCBA is the only choice for people who need to stay in such an area.

• Odour threshold of the substance set by the American Society for Testing and Materials is a means for us to identify their existence. It also acts as an effective warning signal for an ineffective respiratory protection system or a system whose service life is finished. For those substances with a permissible limit lower than the odour threshold value, the air purifying system

J. Liu and Yung Kin Wai

would not be recommended due to the potential risk to users. In cases where the respiratory protection system is damaged or whose service life is due to end, there is no means to make users aware of the risk.

• Airborne contaminant can exist in two different forms: particulate and gaseous states. Different types of respiratory protection system are being designed to protect users against each or both of the contaminant forms.

• Each type of respiratory protection system has a protection limit against certain substance(s). As the concentration of an airborne contaminant increases, a protection system that has a higher protection factor is required. Hence, the more concentrated the airborne contaminant present, the less choice the user will have.

• Besides causing harm to health through an ineffective respiratory system, some contaminants can get into the human body through skin absorption. For those substances with this property, extra protection, such as eyes and skin protection, are required for people who need to work with these substances.

3. METHODOLOGIES

In this intelligent system, fuzzy logic, genetic algorithm and case-based reasoning (CBR) have been integrated with traditional rule-based reasoning. With fuzzy logic, the system can help the user estimate the contaminant concentration, workplace temperature and workplace relative humidity. With role-based reasoning, the system can help the user guess the saturated vapour pressure of the contaminant from information about the structure of the substance. The genetic algorithm and case-based reasoning help provide comparison with past cases stored in a database and allow modification of similar cases to establish new cases from the system recommendation. These intelligent elements act as supplementary parts of the rule-based reasoning backbone (Fig. 1). An intermediate diagram of the whole system is shown in Fig. 2. The system has three functional components recommending respiratory protection, estimating its service life and verifying new cases. Its databases contain the domain knowledge and are written in Lotus 123 format, rules, frames and cases.

3.1. Rule-Based Reasoning

The heart of the system is the general knowledge base which contains problem-solving knowledge of the particular application. Rules are the most important part of the system as they are being employed for storing knowledge and for flow control. In a rule-based system, knowledge is represented in the form of if-then rules. These rules are closed to the way in which human beings describe their own problem-solving techniques. The search process is based on special algorithms shown in Fig. 3, that generate efficient decision trees which reduce

Intelligent System for Respiratory Protection

User Input

Rule-based Reasoning

l Recommendation

J Fuzzy Logic I

1 I

~ Genetic Algorithm & Case-based

Reasoning

FIGURE 1. Context diagram of the intelligent system.

311

the number of questions that must be asked before a solution is reached (Turban, 1993).

This type of operation requires users to inform the system about the type of contaminants they have encountered, the physical form of the contaminants or the process that generates the contaminants, oxygen content in the workplaces, concentrations of the contaminants and so on.

3.2. Fuzzy Logic In a real life situation, it is sometimes very hard to expect users to know the exact values of some of the information, e.g. the concentration of airborne contaminant in the workplace. Under such circumstances, the system is incorporated with fuzzy logic (Turksen &

Tian, 1992; Bench-Capon, 1990) to help users estimate the unknown based on their perception. For those substances that human beings can smell, the system would use the lowest odour threshold value as a reference point for the estimation. Linguistics expression such as "little odour", "fairly strong odour", "very strong, odour", etc. are employed for representing uncertainty knowledge. Users can also adjust their personal perception via a typical slider (Fig. 4) provided by the system.

With a similar method, temperature, the relative humidity of the workplace and breathing rate of worker can be estimated accordingly. The system works with some intrinsically vague, imprecise and subjective input (e.g. very cold or hot temperature, very low or high relative humidity, very low or heavy breathing rate) and derives results with a certain degree of uncertainty.

USER

INTELLIGENT SYSTEM

RESPIRATOR RECOMMENDATION

Chem,ca, , . ,o l _ (- SERVICE LIFE ESTIMATION

VERIFY NEW L~ CASES

02 IRINDEX.WKI

Chemical Name ~D31ClNDEX WK1

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MASK.WK1 fnfo ~ W K 1

CBRSERLF.WK1

- "~ [ f .Ds I TEMP,WKt )nflrmed New Cases

~D61TEMP.WKI Verified Cases

• ,~J'D51CBRSERLF.WK1

FIGURE 2. Intermediate diagram of the intelligent system.

312 J. Liu and Yung Kin Wai

E Career

o:

User Input _~_ Rule-based system

Oxygen deficiency?

I en, i of eohomio I extact data

r is the substance in the [ database? [

yes

i Concentration of the ~ . ~ substance?

J . ~ known ~-.

~ Is IDHL concentration exceeded?

_ ~ Select respiratory -1 protection equipment J

Recommendation

n o I 1 ~ f Rule-basedReasoning I

I I

u n k n o w n I i

""--------~l Fuzzy Logic f I System J I I

Fuzzy data

F I i Genetic Algorithm & I Case-based Reasoning J I j

FIGURE 3. Searching process.

Vapour pressure

Functions o f buttons on the screen :

Confirm - Confirm choices you have made on the screen. Quit - Quit to the front menu o f the system.

FIGURE 4. User screen for estimating chemical concentration.

Intelligent System for Respiratory Protection 313

3.3. Genetic Algorithm and Case-Based Reasoning

Although fuzzy logic is employed to help users with estimating a suitable value for some data, there are still many situations in which data can hardly be estimated due to incomplete input given by the user. With the hybrid approach employing genetic algorithm and case- based reasoning (Ketler, 1993; Mott, 1993; Liebowitz, 1996), the intelligent system can perform a similarity comparison (e.g. Liu et al., 1994) of cases. It can compare the problem, which does not have complete data, with past experience or knowledge stored in the system database (Fig. 5). For example, the system would check the presence of each input value when estimating the service life of the case. Only known values in the new case will be compared with all cases in the database using the similarity comparison of genetic algorithm. Hence, if there are ten cases in the database, ten times the number of known values of comparisons will be made. A score is assigned to individual cases during the comparison process. The most similar is the case of the new case, the higher score is assigned to that case. The score will then be stored in a list and only a case with the highest score will be further manipulated in order to estimate the service life of the new case. Once a similar case in the database has been identified, the system modifies the solution of the case with reference to the difference between the problem and the case. As a result, a reasonable recommendation can be generated and given to the user. Effectively, the approach allows for the automation of the process of incorporating new knowledge into an existing knowledge base. It Supports learning like that of human beings.

4. OPERATION OF THE SYSTEM

A frame-based expert system building tool j was used in the development of the intelligent system: The operation

Kappa PC Software, Intellieorp, U.S:A.

requires the use of a PC machine of 50 MHz 486 CPU with 8 Megabyte RAM on board and super VGA (1024 × 786 and 256 colours) display in configuration.

There are basically two functions available in the system: choosing the respirator and estimating the service life of the respirator (Fig. 6).

4.1. Choosing the Respirator

When a user chooses this function, the system will ask the user about the oxygen content in the associated workplace. If the user knows that there is not enough oxygen, the system will make a recommendation immediately (Fig. 7). SCBA will be the solution as there is no other respiratory protection system suitable for such a working environment (Fig. 8).

On the other hand, if the user knows that the oxygen content in the workplace is sufficient, the system will request further input about the identity of the airborne contaminant. In the case where the user does not know what is/are the contaminant(s) present, the system will be able to make a recommendation based on the user's profession which can be selected from a given list of occupations (Fig. 9). Such a recommendation will be set according to the standard chosen by the user at the beginning of the run as shown in Fig. I0.

For example, if the U.S.A. standard is used and "Respirator with Organic Vapour Filter Cartridge" is the recommended respiratory protection system for the associated profession, details will be displayed as shown in Fig. 11 after the "Fullface Mask" and "Cartridge and/ or Filter" buttons are activated.

Heuristic matching between the occupation and suitable respiratory protection system is part of the built-in function of the system. Information for this matching is based on experience collected from industrial safety experts.

Now, if the user knows the contaminant identity, he/ she can follow the instructions given by the system and complete the input procedures. If the contaminant is

I New case 1

I Service life of ] Organic filter cartridge ~

Case database | _ _ J

I Cases comparison by genetic algorithm

[ Adjustment by [ Case-based reasoning I

FIGURE 5. Similarity comparison of cases.


known by the system, relative information about the respirator protection will be displayed (Fig. 12). In contrast, if the input contaminant is unknown to the system, the contaminant will be treated as an organic substance because this is most likely to be the nature of this unknown based on experts' experience.

For an unknown substance, the system will try to estimate the service life of some approved organic vapour respirator. If the user knows every detail about the substance, using equations derived by Wood and Olinger (1994):

tK co ]

where

tb = breakthrough time (rain) W~= equilibrium adsorption capacity (g/g carbon) W= weight of carbon adsorbent (g) Co = inlet concentration (g/cm 3) Cx = exit concentration (g/cm 3) Kv= adsorption rate coefficient (rain-~) pa= bulk density of the carbon filter (g/cm 3) Q= volumetric flow rate (cm3/min)

the service life of that particular organic vapours respirator would be estimated and displayed as the final recommendation. If the user does not know any of the required information, such as the breathing rate of the workers, the temperature, the relative humidity of the workplace or the saturated vapour pressure of the contaminant at the workplace temperature, fuzzy logic and a specially designed rule-based reasoning of the system will be triggered to help estimate the corresponding values.

There do exist cases in which some unknown data will hamper the estimation of the service life in spite of the

Functions o f buttons on the menu screen:

Choose Respirator - Begin the respirator recommendation function Estimate Service Life - Begin service life estimation o f particulate or organic vapours

respiratory protection systems *Verify New Case - Verify new cases in the temporary database Quit - Quit to KAPPA-PC Applications Development system

* Only appears when temporary database o f the system is not empty

FIGURE 6. Function menu.

Intelligent System fo r Respiratory Protection

f l . RESPIRATOR RECOMMENDATION~ | . .[ Chemical I ( ' 1.t ~ o ~ g . ¢ 1.2 ~ n o ~ , . , , , . l ~ " l Oxen CL~S~2~ | Content _ _ | '~--/J II u°r'l~ ~

_ _ . or . . . o _ ,

I ~ I , ~ . . ~ ~ . m , . , t ~ . . u l ~ _ .

Lr, ilRiIDE~WK1 "/" il.X~/'~/ ~'6,: t. . . . . . . . J-~'l_- I-D41~ROtII'WK1 _ , / I F "~ 1.6 ~'Oo~ -"-J..~Casel

I 0. ¢ oj . . . . . . . . ,~a~ t loa ICONCENTRA'I'IOI~ ] I USER/t ' ~ ~ ...... ,,, ,, J "~i Dill TEMP'WK1

FIGURE 7. Data flow for respirator recommendation.

315

hamper the estimation of the service life in spite of the above approximation, the incorporation of fuzzy logic and heuristics adopted by the system. To tackle such problems, similarity comparison using a genetic algorithm will be employed to search for the most similar

case in the system database. The system will first compare the input case with all cases in the system database. Individual factors of the input case will be compared with the corresponding datum in a case from the system. The smaller the difference between the data,


Main Menu - Return to Main Menu o f the system Explain - Explanation about why the shown respiratory protection system is

recommended to you. Quit - Quit to KAPPA-PC Applications Development system.

FIGURE 8. Recommendation for self-contained breathing apparatus (SCBA).


Please choose your career from below

/~lhesive .and Sealants Agricullurul Appliciatiun Anhydrous AInrrlonia #V.jricullurul Produclion Crups /~. ric.LdltJral Prnduclion I ivP..~t~c.k /~Jdcultural Services Aircraft and Par I'.; #ulthraclte Mlnln~J

FIGURE 9. Occupation list.

the higher the score given to that datum, i.e.

Individual score = { 1 - [Its-/~l/a] }* ~o

where

a = datum from a case in the system database t = datum from the input case to= weighing factor for that particular datum

Missing data will be ignored in the comparison. After all data in that case have been compared with the input case, a total score of that case will be obtained and stored in a list for later application. Comparison will be done with others in the system database and the one with the highest total score will be chosen for representing the service life of the input case. Case-based reasoning will be employed to modify the solution of some chosen case. Modification is based on the difference between the

individual factors of the chosen case and those of the input case, i.e.

Difference = [(a - ~)/a]* 100%

Service Life = Service Life*(1 + Difference*A)

where A is the correction factor for that particular datum. As a result, a reasonable solution will be deduced and recommended to users. Data and solutions of the new case will be stored in a temporary database. Users are required to verify the case later in order to determine whether to integrate the case into the system database for future application or to subsequently discard the case.

4.2. Estimating the Service Life

The system also provides a function for those users who want to check how long the respirator can serve them.


Confirm - Confirm choices you have made on the screen. Quit - Quit to KAPPA-PC Applications Development system

FIGURE 10. Choices of country standards.

Intelligent System fo r Respiratory Protection 317

FIGURE 11. Recommendation details.

For a particular respirator, simple rule-based reasoning is employed for the estimation. It requires the user to identify the physical state, concentration of contaminant, breathing rate of the worker and country standard that is

required to be followed before an accurate result can be obtained.

For an organic vapours respirator, a similar treatment as for that of an unknown substance in the "Choose


Main Menu - Return to Main Menu o f the system See Respirator Pictures - Go to screen that displays respirator pictures (Figure 8 or 11) Another Chemical - Choose respirator f o r another chemical. Quit - Quit to KAPPA-PC Applications Development system

FIGURE 12. Information about the contaminant.


Respirator" function will be employed. I f the user can input all the information requested by the system, all data and the solution of this case will automatically be stored in the system database as a reference for future application (Fig. 13).

On the other hand, if incomplete input is encountered, the system will apply fuzzy logic, rule-based reasoning, genetic algorithm and case-based reasoning, to estimate the service life of the organic vapours respirator. Similarly, all data and the solution will be stored in a temporary database for the user's future verification.

5. CASE STUDY

5.1. Respirator Recommendation Against Contaminant with Unknown Concentration

It is common for the user to know what substances have been experienced or detected in the workplace. However, the user often does not know the concentration of that substance in the environment. In this case, the system can help estimate the concentration of that contaminant based on the user's olfactory. The basic requirement of this function is that the system should have odour threshold values of this airborne substance.

For example, given that a user is working in a printing company under U.S.A. regulations and n-hexane is a common solvent used for cleaning, he/she shall input the chemical name "Hexane", and the system would identify that none of the chemicals with this name could be found from the system database. Then the system would display a message to the user and state that the input chemical name is new to the system. Two possible reasons may lead to t h i s situation. First, the user misspells the chemical name. Second, the chemical is not in the system database. To tackle this problem, the

system will give two choices to the user: Display the chemical name list in the system database or confirm that the input name is correct. Say, the user chooses the former choice. As a result, a chemical name list with "H" as the first character will be displayed for the user's choice. From the list, the user will find that there are two chemical names with "Hexane" as part of the name. They are "Hexane (n-hexane)" and "Hexane (other hexanes)." As stated beforehand, n-hexane is the contaminant in the workplace, therefore the user will choose the former choice.

After the chemical name is input, the system will ask the user to input the concentration of airborne contaminant in the workplace. Say, the user does not know the concentration in this case, the system will ask the user to input the concentration of chemical based on his/ her odour perception. A slider with scale from "Trace" to "Very High" will be displayed as a tool for the user's input, ff the user feels that the concentration of n-hexane is trace, he/she can move the slider to the "Trace" position and confirm the input to complete the input procedure. By fuzzy logic, the system estimates that the n-hexane concentration shall be about 100 ppm. Then the system will begin information searching from the system databases. A new screen with the following information will be displayed:

Chemical name: Hexane (n-hexane) CAS #: 110-54-3 Synonyms: Hexyl hydride

Normal hexane Odour threshold: 65-248 IDLH: 5000 Permissible limit: 50 Permissible limit type: TLV Permissible limit unit: ppm

2. SERVICE LIFE ESTIMATION

2.1 ~ C 2.2 r~mmm'lChemical State

CHECK J i CALCULATE CHEMICAL" SERVICE LIFE

STATE

2.3 I 2., CHECK InfoL...~.~o a ~ CHECK

CHEMICALINFO I r l OPERATING • ,~ , INFO

• 2.5 • . "] Operating L- 2.6 CHECKINPUT ~ ' ~ P / CALCULATE

COMPLETENE. SERVICE LIFE

FIGURE 13. Data flow for service life estimation.

Intelligent System for Respiratory Protection 319

Remark: Workplace concentration: Carcinogenic properties: Skin absorption: Respirator type: Comment:

TLV is lower than PEL 100 BLANK (no information) BLANK (no information) Supplied air respirator Poor warning

The system recommends the user to use the supplied air (SA) system because n-hexane does not have good warning properties.

pheric pressure, adopted standard, challenge concentration, maximum allowable and breakthrough concentration. The system would check whether the input is complete and employ the appropriate measure to estimate the missing items such as saturated vapour pressure, application temperature, breathing rate and relative humidity. For illustration, if the user follows the U.S.A. regulation, a set of information about the organic filter media that complies with U.S.A. regulations will be used automatically:

5.2. Respirator Recommendation Against New Chemicals

There are thousands of chemicals existing in the market and the numbers are increasing due to various technological advancements in industries. Currently, the system database of this intelligent system contains information on about 700 chemicals. Hence, it is not unusual that users face airborne contaminants whose details are not yet covered in the database of this system.

In this situation, the system would ask the user whether the contaminant is in a particulate state, liquid state or gas/vapour state (Fig. 14).

If the user chooses a particulate state, the system would ask how the contaminant is being generated: by mechanical means or thermal means. For a mechanically generated particulate, a dust/mist respirator will be recommended. On the other hand, for a thermally generated particulate, a dust/mist/fume or even high efficiency respirator will be recommended.

If the user has used 2-Bromo-l,3-butadiene in his/her workplace, he/she will choose "Liquid" from a list of choices provided by the system. Say, the user only knows the molecular weight, density, refractive index, atmos-

Total carbon volume (cm3): Total cross-section area of filter media: Carbon density (g/cm3): Carbon micropore volume (cm3/g):

153.9308 76.969 0.8315 0.1058

With fuzzy logic, the system helps derive the missing information:

Breathing rate (l/rain): Application temperature (°C): Relative humidity (%):

33 24 50

For the remaining unknown data, the saturated vapour pressure of the chemical, the system would display a screen to let the user input the structure of the substance. For 2-Bromo-l,3-butadiene, there are four carbons as the backbone of the chemical, two carbon-carbon double bonds and one bromide functional group. After the input has been made, the system will estimate the boiling point and saturated vapour pressure of the chemical as 94.42°C and 0.0694 atm, respectively.

After a complicated calculation has been done by the system, the organic vapour respirator with a minimum service life of 362.4 min will be recommended to users. The estimated service life has already involved a 50%

Input

I us,,S°.-..

1.5 NEW CHEMICAL TREATMENT

r - - ' ( cHECK / / / ] CHEMICAL | |

/ t ) L - - w.,a~Otl ~

1.5.2 CALCULATE

SERVICE LIFE

i 1.s.3 hemical Name lID CHECK OPERATING I ~ BollinoPolnt Info INFO I ~

"~'[ t.S.S c.Elc2. .,o. 1

I R~endation ~ l r ~

I D41 FGROUP.WK1

_.~D51CBRSERLF.WK1

I)61 TEMP.WK1 s endatlon . . ~ _ ~

FIGURE 14. Data flow for new chemical recommendation.


safety factor. Typically, any organic vapour respiratory protection system which complies with the standard in the market should provide a longer service life when compared with the estimated service life. After calculation, the case will be automatically added to the system database for future application. Hence, the intelligent system has learnt and gained more experience.

5.3. Estimate of the Service Life of a Respirator

Sometimes, users of respirators are very interested in how long their respirators can last so that they can ensure they have enough stock for their application or to determine when to change to a new respirator according to the respirator management program against a chemical with poor warning properties.

If a user wants the system to estimate the service life of organic vapour filter media against a certain organic vapour, procedures similar to those discussed in Section 5.2 will be followed. However, in this function, the user is required to input properties of the carbon filter media in use. Assume the user knows all information except the refractive index of the chemical. As a result, the system cannot perform the estimation using equations derived by Gerry Wood and Miles Olinger.

To tackle this problem, genetic algorithm and case- based reasoning have been used (Fig. 15), By similarity comparison of genetic algorithm, all cases in the system database would be compared with the input case. A case that is most similar to the input case will be chosen. According to the difference of each item between the old and new cases, a corresponding adjustment will be made to the service life of the old case. Finally, an adjusted service life will be obtained as the estimated service life

for the new case. From the safety point of view, a 50% safety factor will be reduced from the estimated service life as the minimum service life recommended to the user.

To be adaptive, this new case would be stored in a temporary database of the system for future verification. The user can verify the case later and store the case in the system database for future reference.

6. DISCUSSION

6.1. Contribution

A respiratory protection expert system is an expert system that helps workers, who do not have enough respiratory protection professional knowledge, to choose a suitable respiratory protection system against airborne contaminants in their workplaces. Initial responses from end-users indicate that the developed system has been able to provide a recommendation for respirator protection based on the limited information available. It is specially useful for general enquiry from people who do not have complete knowledge related to their specific needs.

In normal practice, users are required to hire professional(s) to evaluate the working environment where personal respiratory protection is required. According to the local regulations, approved respiratory protection systems will be recommended to users. However, in cases where the professional is required to estimate the service life of the respiratory protection system against some particular airborne substances, cumbersome calcu- lations will be involved. The professional needs to obtain relative information from a data book, such as the CRC

~ ' - 3. VERIFY NEW CASES

] D6 J TEMP.WK1 3.2

c =., . . ) , , . .c.[ • I =.,..u.j -lo...-c.s-2

?

J

Verified New

Cases ~'~I DS J CBRSERLF.WK1

hle~u,

,~eel I Ds I TEMP.WK1 o~ G P " ' '

FIGURE 15. Data flow for case verification.

Intelligent System for Respiratory Protection

handbook for chemistry and physics (Lide & Frederikse, 1993), or the Material Safety Data Sheet provided by the material supplier or by environmental evaluation. Most probably, the professional will need several weeks to several months before a recommendation can be made. Within this period, workers will be suffering from the airborne contaminants without any protection.

With the intelligent system, even people who are not professionals in this field, such as the employer or safety officers of any industry, can immediately obtain a recommendation from the system although the recommendation may not be the best one. The system can help users get a reasonable recommendation even in cases where a complete set of relative data is not available. Hence, the system accepts incomplete input from users.

The intelligent system provides step by step procedures to lead the user's input to usable information. For a traditional expert system in the market, there is only one straightforward data input path available for users. If a user does not know any of the required data, the system will be terminated as there is not enough information for the system to make any recommendation. However, in this system, flexible information input paths are provided for users. Depending on how much information a user knows, the system will use a different input path to elicit as much information as possible from inputs f rom that user. Through the data input path, which is chosen based on the user's input, the system would help "guess" many known data. By using specific rule-based reasoning procedures and fuzzy logic, the system can help users make a reasonable estimation of some data, such as the concentration of the contaminants (if the odour threshold of those substances exist), the breathing rate of the workers, relative humidity in the working environment, temperature in the workplace and saturated vapour pressure of the chemical.

In many cases, there are still some data which cannot be guessed by the system. The system will treat the input substance as organic in nature because organic substances are the most likely substances not to be stored in the system database. For incomplete data, the system would use both genetic algorithm and case-based reasoning to estimate the service life of this organic substance. Within just a few minutes of interaction with the system, users can obtain a reasonable recommendation from the system.

However, the system is not intended to replace the job of the professional in this area. The system is just acting as an aid to help end-users or professionals take some immediate action to protect the workers. Since most data the professional needs to find in the literature are stored in databases, the system can provide a faster means for a professional to obtain relative data and make judgment within a short period of time. For incomplete data, end- users or professionals are still recommended to look for it as much as possible. As a rule of thumb, the more accurate data we have, the more reliable the recom-

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mendation that can be made by both the system and the professional.

6.2. Limitations

Although the expert system can provide a good means with which to help the user obtain reasonable respiratory protection information, there are still some limitations. First, there are only about 700 full-sets of information of substances in the system database. If the input chemical is not one of these 700 substances, the system will treat this substance as a new chemical. Unless the user can input all the required data to the system, the system will help estimate the unknown data. Since some data are generated by the guessing mechanism of the system, the accuracy of the recommendation may not be that high.

Second, for every recommendation made by the system, except those recommendations based on the user's occupation, only one airborne contaminant will be considered. In real life, it is not unusual to have many different airborne contaminants present in a workplace at the same time. In such cases, the user is required to obtain a recommendation from the system for all the substances one by one, and then to combine all the recommendations into a single solution. For the professional, it may not be difficult to undertake such a combination process. However, for general users, it may be very hard or even impossible to resolve the problem.

Third, the concentration estimation can be done only for those chemicals with odour threshold values. There is no means of estimating the concentration of a substance if its smell cannot be detected naturally. Hence, the concentration estimation function can only work for limited substances. As a result, the concentration estimation function is only limited to some substances that have significant odour.

Fourth, the system can only estimate the saturated vapour pressure of organic substances and not inorganic substances. When a user is using this function, he/she needs to be quite familiar with organic chemistry and to understand the actual structure of the substance based on its molecular formula and chemical name. Consequently, the saturated vapour pressure estimation function of the system may simply be useless for non-experts in the field.

Fifth, the coverage of boiling point estimation is only a generalized approach. All organic compounds with the same chemical formula are treated as the same. However, in organic chemistry, even compounds have the same chemical formula but they may possess quite different physical properties (Murray, 1979). For substances with the same molecular formula, their boiling change trend is combined as a single value, i.e. the mean of their changing trend. As a result, the estimated boiling point for the substance with this function would not be that accurate.

Sixth, when using rule-based reasoning to estimate the


boiling point of a chemical, some exceptional cases have been excluded for the convenience of setting up the database. However, when these cases are encountered, a relatively large error may occur on the estimation and the accuracy of the recommendation may become worse accordingly. In addition, the system database only contains boiling point change information of 26 com- monly used functional groups in organic chemistry. Actually, there are more than 26 types of functional group present in organic compounds. Hence, the boiling point estimate function of the system can only handle a limited number of substances.

Seventh, all respiratory protection system recommendations are based on standards from four different countries/areas: Australia/New Zealand, Europe, Japan and the U.S.A. However, there are more than four sets of standards in the world, the GB in China, the Russian standard and the Korea standard, for example. When people from these countries try to use the system, they may not obtain a recommendation that complies with their local standards.

6.3. Recommendations for Future Development

The following are recommended to improve the system in the future:

(a) Add additional functions to the system to describe the principle and details of respiratory protection regulations and/or standards in different countries for the user's reference.

(b) Add more standards to the databases so that the intelligent system can be used globally.

(c) Add more functional groups to the databases in order to improve the coverage of organic substances that can be handled by the system.

(d) Complete the boiling point database with data available in the CRC handbook.

(e) Add a function to the system to take spacing or molecular arrangement into account when estimating the saturated vapour pressure based on the molecular formula of the substance.

(f) Integrate the system with available CD-ROM databases on the market. As a result, less data input will be required. Users will be able to use the system much more easily and conveniently.

(g) Reduce the number of instances and program size. As the development software tool adopted for the present study is an interpreter type software, the program must be stored in the memory before it can be run. As a result, the amount of system resources left in Windows 3.1 or Windows 3.11

software will be reduced. Sometimes, if more than one window has been opened, the functions of these windows will have no response because of a lack of system resources to handle the commands in the Windows environment. As the system becomes more complicated, in order to handle more complicated cases, the number of instances in the system would be increased to a point where the above-mentioned case occurs. To avoid such an occurrence, it would be advisable to develop more objects that are outside the system. The intelligent system can use these objects when a certain function is required. Hence, the overheads of the system in a Windows environment can be reduced.

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