Intelligent Decision Support for Waste Minimization in...

Pergamon PIhS0952-1976(97)00026-2

Engng Applic. Artif Intell. Vol. 10, No. 4, pp. 321-334, 1997 © 1997 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 0952-1976/97 $17.00+0.00

Contributed Paper

Intelligent Decision Support for Waste Minimization in Electroplating Plants

K. Q. LUO Wayne State University, Detroit, MI, U.S.A.

Y. L. HUANG Wayne State University, Detroit, MI, U.S.A.

(Received May 1996; in revised form March 1997)

Wastewater, spent solvent, spent process solutions, and sludge are the major waste streams generated in large volumes daily in electroplating plants. These waste streams can be significantly minimized through process modification and operational improvement. In this endeavor, extensive knowledge covering various disciplines is required, which makes problem-solving extremely difficult. Moreover, available process data pertaining to waste minimization (WM) is usually imprecise, incomplete, and uncertain due to the lack of sensors, the difficulty of measurement, and process variations. These hinder the use of rigorous mathematical approaches in formulating WM problems. In the present work, an intelligent decision support system, namely WMEP- Advisor, is developed by resorting to artificial intelligence and fuzzy logic. This system is capable of performing detailed process analysis on waste-generation mechanisms, evaluating WM practice for an individual process unit or an entire plating process, identifying WM opportunities, and providing adequate decision support to process and environmental engineers for process modification and operational change. The tool can be used for either on-site WM or off-line personnel training. © 1997 Elsevier Science Ltd. All rights reserved

Keywords: Intelligent decision support, fuzzy logic, waste minimization, electroplating, process modification, operational improvement.

I. INTRODUCTION

Waste minimization (WM) in the manufacturing industries is one of the major tasks in the prevention of industrial pollution. Virtually all manufacturing of precious metal products involves electroplating. The over 6700 electroplating plants in the US utilize more than 100 chemicals to electroplate parts with one or a combination of over 100 metallic coatings. This industry has been generating a huge amount of waste in the forms of wastewater, spent solvent, spent process solutions, and sludge (Freeman, 1988). The waste streams contain numerous hazardous or toxic chemical, metal, and non-metal contaminants that are regulated by the EPA (PRC Environmental Management Inc., 1989), and must be significantly reduced in order to prevent pollution and to reduce end-of-pipe treatment costs. Accord-

Correspondence should be sent to: Prof. Y. L. Huang, Department of Chemical Engineering and Materials Science, Wayne State University, Detroit. MI 48202. U.S.A. [E-mail: yhuang@cheml .eng.wayne.edu].

ing to the EPA's WM hierarchy, source reduction has the highest priority (Rittmeyer, 1991).

An electroplating process is a typical chemical process, where a number of process units are sequentially connected. For this process, source reduction can be realized mainly through: (i) process and equipment modification, (ii) process control and optimization, (iii) technology change, and (iv) material substitution and product reformation. Over the last decade, a variety of methodologies and technologies for source reduction have been developed in this industry (Meltzer et al., 1990; Noyes, 1993). According to a recent survey, however, they have not fully permeated the plants (Duke, 1994). One of the major reasons is that they have not been well classified and compared in terms of cost, applicability, and efficiency. An application of any of them requires extensive knowledge and ample experience which are usually heuristic, problem or process-specific, and locally available.

While the development of new WM methodologies and technologies is always important, maximizing the use of

321

322 K.Q. LUO and Y. L. HUANG: INTELLIGENT DECISION SUPPORT

existing ones in the electroplating industry is probably much more urgent, practical, and cost-effective. This can help plants achieve WM goals in a much shorter period of time. Huang et al., developed a prototype expert system, namely MIN-CYANIDE, to assist engineers to minimize cyanide- containing waste solutions in plants (Huang et al., 1991). This system was capable of identifying WM opportunities and recommending prioritized measures for source reduction. However, it was limited to dealing with cyanide-containing waste streams only, and was incapable of providing any decision support for the minimization of many other chemical, metal and non-metal containing waste streams. Nevertheless, it was shown that the expert-system technique is attractive in formulating a domain expert's knowledge and making decisions at an expert level in fighting against waste.

In this paper, an intelligent decision-making approach is developed to tackle comprehensive source-reduction problems in electroplating plants. This approach utilizes knowledge engineering and fuzzy logic techniques to explore both deep knowledge (the first principles) and shallow knowledge (heuristics) to the maximum extent. It has been implemented as a PC-based WM decision-support system, namely WMEP-Advisor. The system is capable of performing detailed process analysis on waste generation mechanisms, evaluating WM practice, identifying WM bottlenecks, and prioritizing WM strategies in terms of cost and technical efficiency. A variety of computer graphics greatly helps engineers quickly derive WM solutions.

2. PROCESS WASTE SOURCES

An electroplating process involves the application of thin metal through electrodeposition. In the process, workpieces are loaded in barrels or on racks, and are processed in a series of process units. As illustrated in Fig. 1, each process bath is followed by one or two rinse units for removing the residual process solutions from the surfaces of workpieces. Thus, cross-contamination and plating quality can be improved. The plated workpieces are finally air-dried. Waste streams generated from the process can be classified into four categories: wastewater, spent solvents, spent process solutions, and sludge. More detailed classifications of the wastes are given in Table 1 (Palmer et al., 1988). A major portion of the wastewater comes from the rinsing steps. Wastewater also comes from leakages, spillage, cleaning, and dumping process solutions. A plant may generate 80 to 200 m 3 of wastewater per day, which contains heavy metals, cyanide, oil, and many chemical compounds (Noyes, 1993).

Various solvents, such as soak cleaners, electrocleaners, and acid cleaners, are widely used to remove oil, grease, soil, and other extraneous substances from metal surfaces. Thus, large quantities of spent solvents are generated. All process bath solutions have to be dumped after exceeding their useful lives, due to contaminants in the baths. The contaminants contain a large quantity of metals, and some compounds are difficult to handle. The solutions can be bled

into on-site waste-treatment facilities for pretreatment and recovery; otherwise, they can be encapsulated for off-site treatment and disposal. Treatment residues always occur in the form of sludge, such as degreaser sludge, filter sludge, and wastewater treatment sludge. The sludge contains more than 65% of water on average, which can be largely reduced through waste segregation, water deionization, and sludge dewatering.

3. PRACTISING SOURCE REDUCTION APPROACHES

Source reduction in a plating plant can be realized through drag-out minimization, bath life extension, rinse water reduction, cyanide-free solution utilization, plating metal alternation, and process operational improvements (Freeman, 1995). In evaluating these approaches, WM efficiency, cost, and productivity are the main concerns.

When a barrel of parts is withdrawn from a process unit, the parts always retain some process solutions, which are called "drag-out". Usually, the drag-out can be minimized by: (i) reducing the speed of withdrawal and allowing sufficient drainage time, (ii) lowering the concentrations of the process baths, (iii) using surfactants, (iv) increasing the solution temperature, (v) installing drain boards between process tanks, (vi) enlarging hole sizes on barrels, and (vii) rotating barrels above the tank or placing the workpieces in an appropriate position. Note that all these measures must be appropriately adopted; otherwise, the production rate and plating quality will be negatively affected. For instance, a long drainage time and a low bath concentration are desired for WM, but these may be detrimental to the production rate and plating quality.

Keeping process bath solutions from contamination can extend the life of a bath. This requires the continuous improvement of rinse efficiency. Industrial practice suggests to deionize made-up water, to regenerate plating solutions by removing impurities, and to apply mechanical agitation. A properly maintained plating bath can be used for as long as 15 years without dumping, thereby greatly reducing the chemical cost and sludge volume. The quantity of sludge is usually proportional to the metal concentration in the spent rinse water. An empirical formula for estimating the sludge volume, Vs (1/1 wastewater), is as follows (EPA, 1982):

Vs=3.85 × 10-4Chm~5.8 X 10 -2 (1)

where Chin is the concentration of heavy metals in the sludge (mg/1). Here, wastewater is treated after one hour of settling (lime neutralization). Therefore, the reduction of rinse- water consumption is always the first step towards sludge reduction. To do so, the design of a rinse system should be evaluated. Moreover, it is always suggested to install an automatic flow control system and to have agitation systems in all rinse tanks.

Cyanide, a poisonous substance, exists in water as HCN which is a very weak acid. Volatile HCN is highly toxic and

K. Q. LUO and Y. L. HUANG: INTELLIGENT DECISION SUPPORT 323

indicative of a serious pollution problem. It is highly desirable, therefore, to replace a cyanide-containing solution with a non-cyanide solution. A cyanide-zinc solution, for instance, can be replaced with a non-chelated alkaline zinc solution (Meltzer et al. , 1990). Non-cyanide cadmium baths are now available to replace a cyanide cadmium bath (Higgins, 1989). It is possible in some instances, however, to replace cyanide cadmium plating with other materials, such as zinc, titanium dioxide (using vapor deposition), and aluminum. None of these coatings has exactly the same properties as cadmium. Hence, the replacement should be judged based on quality requirements, and economic and environmental criteria.

Source reduction can also be significantly enhanced by operational improvement. System optimization can always lead to the improvement of a plating operation, and to avoid excessive waste generation. Moreover, effective fluid con-

trol can prevent excessive rinse water consumption, and hinder oil and solid build-up in the tanks. A simple waste management program can reduce unnecessary chemical losses and prevent accidental spills. Usually, the improvement of waste management is cheaper than many other methods of source reduction.

4. INTELLIGENT DECISION SUPPORT

While numerous detailed source-reduction strategies are available, this implementation in plants is always ineffec- tive (Duke, 1994). In reality, a successful implementation requires knowledge in diverse fields such as engineering chemistry, biology, fluid mechanics, mathematics, statistics, economics, and law. Such knowledge can be divided into two classes: deep knowledge and shallow knowledge. The former covers the first principles for mass and energy

Work Flow

lAlkaline ~ Clean

Rins e

Acid Glean

Pins e

• 1 Neutralization / • -"'~and Pre ciloitation ~

Clean Water

Rin5 e

Acid Cle an

Rinse

~ ] R i n s e

[Chromium [ I Platin~ | ......... i

[ and Dry I

~[ Cyanide Oxldization I

[ Precipitation ~ Settle

~I Precipitation

d ~ Chr°mium I Water Reduction (To wwn,)

] Precipitation F Sludge

Off-Site I Treatment/

I)esposal Fig. 1. Typical electroplating process with waste treatment facilities.

324 K.Q. LUO and Y, L. HUANG: INTELLIGENT DECISION SUPPORT

balances, reaction kinetics and electrochemistry, and process dynamics; the latter implies various heuristics for process analysis and problem solving. Both classes of knowledge are essential in computation. Unfortunately, the available information pertaining to source reduction is frequently uncertain, imprecise, incomplete, and qualitative. This hinders the use of conventional mathematical approaches. Artificial intelligence (AI) based approaches, however, are viable in this endeavor (Huang and Fan, 1993a, 1995).

An intelligent decision support system (IDSS) can integrate different fields of knowledge in a systematic manner. It is an interactive system that helps decision- makers utilize information to solve the poorly structured problems that are often the case in environmental situations (Luo and Huang, 1995). An IDSS is such a tool necessary for structuring WM problems, gaining new insights about them, looking for examples that have already been solved, or otherwise deriving alternative solutions by means of AI techniques. The development of an IDSS is by no means aimed at replacing specific human capacities, but at supporting the users. In the end, the time and the steps necessary for identifying the best solutions to WM problems can be significantly shortened.

The degree of intelligence of an IDSS is largely determined by the type of AI techniques used. An expert systems technique allows an IDSS to perform at an expert level (Crowe and Vassiliadcs, 1995; Huang and Fan, 1994). The rules in the knowledge base of the IDSS are usually Boolean logic based. This implies that the decisions can be made only if the existing information and data are complete, precise, and certain. To solve complicated WM problems, however, the IDSS also needs to process incomplete, imprecise, and uncertain information and data. This suggests the use of more powerful theories, such as multivalue logic and infinitive value logic. This renders the introduction of fuzzy logic necessary (Zadeh, 1965).

Fuzzy logic is a type of logic that is much closer in spirit to human thought and language than conventional logic systems. In compliance with the spirit of logic, it attempts to be precise. Thus, fuzzy logic is, perhaps somewhat paradoxically, a precise system for imprecise reasoning (Zadeh, 1965; Zimmermann, 1985). Fuzzy logic, together with expert system techniques, permits the information technology to pass from rather restricted calculation applications to a wider area of simultaneous processing of knowledge and information (Dubois and Prade, 1980; Huang and Fan, 1993b). They are capable of dealing with

Table I. Major process wastes generated in the electroplating industry (Palmer et al . , 1988)

Waste Waste Process RCRA category description origin Composition code

Wastewater Waste rinse Drag-out, Same as the compositions in water equipment relevant process solutions and

cleaning, spills solvents

Aqueous NaOH, Na2CO 3, Na2SiO 3, F009 cleaning Na3PO ~, cyanide, soils,

EDTA + Mg/Ca, saponified and/or emulsified grease

HCI, H2SO~, HNO3, H2CrO4, H3PO4, Mg +, oils, soils

Spent solvent

Spent solution

Treatment residue

Spent alkaline cleaning solution

Spent acid cleaning solution

Spent plating solutions

Degreaser sludge

Solvent recycle still bottoms

Filter sludge

Wastewater treatment sludge

Vent scrubber waste

Ion exchange resin reagents

Acid pickling

Electroplating Same as the composition in F007 relevant plating solutions

Solvent recycling Kerosene, naphtha, toluene, F001 ketones, alcohols, ethers, F002 halogenated hydrocarbons, F003 oils, soils, water F005

Solvent recycling Same as above solvents. May contain HCI from solvent decomposition

Electroplating Silica, silicides, carbides, ash, F008 plating bath constituents

Waste treatment Metal hydroxides, sulfides, F006 carbonates

Vent scrubbing Similar to process solution composition

Demineralization of process water

Brine, HC1, NaOH


structured or unstructured, symbolic or numerical knowledge with imprecise information, and allowing vague and linguistic terms to be represented by precise mathematics. They are especially efficient in solving problems requiring significant human expertise.

5. KNOWLEDGE REPRESENTATION

While the first-principles knowledge is rigorous and readily accessible to deal with, heuristic knowledge usually contains a variety of uncertainties and imprecision, and is frequently expressed linguistically, qualitatively, or semi- quantitatively. Hence, maximizing the use of this type of knowledge becomes a key issue in developing a successful IDSS.

In the IDSS, the first principles are expressed as mathematical functions and models. Heuristic knowledge is represented in two forms: purely symbolic rules (fuzzy rules) and hybrid rules (fuzzy models). These knowledge representation schemes enable the knowledge to be used at all levels, including the conceptual, epistemological, logical, and physical levels, and thus permit the utilization of knowledge to the maximum extent.

5.1. Symbolic rules

Purely symbolic IF-THEN rules have been widely used in expert system development (Hushon, 1990). The antecedent part of a rule (IF part) contains a number of clauses which are connected via logical operators, such as AND, OR, and NOT. The consequent part of the rule (THEN part) provides one or more logical conclusions. In either the IF part or the THEN part, the logic involved is simple and rigorous, but the information about the variables can be of any precision. This may be caused by the quality of measurement devices, operators' subjectivity, etc. For instance, there is a rule in the knowledge base:

IF

THEN

the surface tension of a solution is very LOW, AND the temperature of the solution is MUCH LOWER than the optimal setting, AND NO wetter is added, EXCESSIVE drag-out will be generated.

/z~[0,1], i=1,2 ..... 5. These fuzzy numbers are defined below.

].Z/= 0,

/z2=0.25 ,

/z3=0.50, /Z4=0.75,

/xs= 1,

VERY HIGH surface tension HIGH surface tension MEDIUM surface tension LOW surface tension VERY LOW surface tension

The variable, temperature, in the second antecedent of the rule can be precisely measured. However, it is extremely difficult to determine how much a very low temperature, together with other factors, can affect source reduction. This is especially true when the bath temperature is not very stable. Industrial practice suggests the definition of three fuzzy membership functions:

t l,

/zs,(T)= 27 - 0.09T, t.0,

T--289 K

289 K<T--<300 K

T> 300 K

(2)

t l,

/zB~(T)= 28 - 0.09T, t.0,

T-<300 K

300 K<T-<311 K

T>311 K

(3)

/zB~(T) = f ~ I 0 9 T - 2 7 , t. 1,

T-<300 K

300 K< T<---311 K

T>311K

(4)

where B~, B2, and B 3 are the fuzzy sets quantifying the fuzzy linguistic terms, MUCH LOWER, LOWER, and CLOSER, respectively./z~,(T), ~B2(T), and/zB~(T) are the corresponding fuzzy membership function, as depicted in Fig. 2.

The last antecedent in the rule, the addition of wetter, can be either true or false, but not both. This allows the introduction of the following crisp membership function with no fuzziness:

#c(W)={0, Wis true 1, Wisfalse

(5)

where W denotes the addition of wetter; C is the crisp set

Note that it is nearly impossible to define precisely the boundaries between the fuzzy linguistic concepts of LOW, MEDIUM, and HIGH surface tensions, LOWER and MUCH LOWER temperatures, and LESS EXCESSIVE and EXCESSIVE drag-out. However, one can introduce the universe of discourse and corresponding fuzzy numbers, fuzzy membership functions, or crisp membership functions to quantify these terms (Zimmermann, 1985). The selection made is dependent on the type of information available.

In the above rule, the variable, surface tension, in the first antecedent can only be estimated based on experience, due to the lack of measurement devices in most electroplating plants. This estimation relies on subjective judgment. A set of fuzzy numbers can be defined empirically. The universe of discourse, U, can contain five discrete fuzzy numbers

0.5

0

278

\o..,,,w.\ I c o. R

] ~ LOWER

/- 289 300 311

Temperature, T(K)

Fig. 2. Fuzzification of symbolic knowledge about temperature.

322


quantifying the status of the addition of wetter; /zc(W) designates the membership function of crisp set C.

Note that the fuzzy set theory is a generalization of the ordinary set theory, and a crisp set is a special case of a fuzzy set. Hence, the above rule can also be mathematically expressed by fuzzy logic as follows:

AI ABMt A CN"'* DM (6)

where AL, BML, CN, and DM are the fuzzy sets for the linguistic terms of LOW surface tension, MUCH LOWER temperature, NO addition of wetter, and EXCESSIVE drag- out, respectively.

In addition to the IF-THEN rule form, an IF-THEN- ELSE form may be useful for many applications. This type of rule can simplify the knowledge base. For instance, one rule is:

IF the concentration of the solution in a bath is HIGH, AND the operating temperature in the bath is LOW,

THEN the viscosity in the bath is HIGH, AND there exists MUCH drag-out,

ELSE there exists NOT MUCH concentration temperature-related drag-out, AND the succeeding process variables should be evaluated.

This rule establishes a cause-effect relationship among concentration, operating temperature, viscosity, and the amount of drag-out. Fuzzy sets BL, BM, and B n, for example, need to be introduced to represent the concepts of LOW, MODERATE, and HIGH operating temperatures, respectively. The rule can be mathematically expressed by the following fuzzy logic expression:

AnAB:'~CNADM (7)

VAnAB:-*DuMA { Viii= 1,2 .... } (8)

where An, BL, Cn, Du, and Dsu quantify, respectively, the linguistic terms of HIGH concentration, LOW temperature, HIGH viscosity, and MUCH and NOT MUCH drag-out.

5.2. Hybrid rules

In solving a WM problem, mathematical models, if they can be readily developed, may be very useful. For instance, disturbances and fluctuations in process operation and the use of different solvents and chemicals in plating and cleaning processes may lead to a significant increment of the concentrations of toxic or hazardous chemicals in waste streams. These phenomena can be characterized by process models. However, due to the complexity, non-linearity, and uncertainty involved, the first-principles models are usually extremely difficult to develop. Empirical models, on the other hand, can be readily generated. When sufficient input- output data is available, statistical methods are always

preferred. However, a large number of WM problems are multidimensional and highly non-linear in nature. Thus, no matter how many experiments are done, there always exists a data shortage.

Takagi and Sugeno introduced a unique modeling approach by means of fuzzy logic and statistics (Takagi and Sugeno, 1985). The fundamental idea of the approach is to divide a data space into a number of sub-spaces, and then to develop a linear model for each sub-space. Since the system described by each sub-space is much simpler than that of the whole data space, the sub-model developed for the particular sub-space can be very reliable and simple. By this approach, the whole system is described by a set of sub- models that are managed by fuzzy logic. Takagi and Sugeno's approach is adopted here to develop hybrid rules.

A hybrid rule still has an IF-THEN form. The IF part still contains one or more symbolical antecedents as usual. The THEN part, however, contains one or more linear models. This type of rule enables the non-linearity of a complex waste generation process to be expressed, and numerical data to be converted to knowledge data more systematically. In addition, it enables human thinking to be expressed linguistically and numerically. This helps domain experts participate in modeling through an interactive operation of the modeling system.

Assume that there exists a set of q hybrid rules, {Rili=l,2 ..... q}. Each rule is a fuzzy-logic-based quasi- linear model, i.e.

R~: IF x~ i sA~ ,x z i sA~ ..... x,,isAi,, (9)

THEN y~l=Cil.o+Ci~.,X, +... +cil.,dC .

i i i i y2-c2.o+C2.~x~ + .,. +c2.,,xn

i - - i i i y m - - C m , O + C m . l X l + . . . + C m , , r X n

where xj is the jth process input variable, such as temperature, concentration, or mass flow rate. yj is the jth process output variable in the ith sub-model Ri; it is usually the concentration of a toxic or hazardous species in a waste stream, or the volume of sludge. Aj is the fuzzy set defined for xj in sub-model R i. cj.k is the regression coefficient.

The development of the hybrid rules undergoes three stages: data clustering, model development, and model validation. The model is generated by an interactive modeling system (Liu and Huang, 1997). This system consists of the modules of data analysis, structure analysis, parameter analysis, regression analysis, model generation, and model validation. The overall modeling system structure is sketched in Fig. 3.

While the rules in this form are logically simple, the key lies in the structure and parameter identifications in the rule development. These include the determination of the number of antecedents in the IF part and the coefficients in the THEN part. The structure of a hybrid rule can be identified by the analysis of available data and experience.


The parameters can be determined by a fuzzy regression algorithm (Liu and Huang, 1997).

6. DECISION-MAKING ALGORITHMS

While the knowledge base of the IDSS contains a huge amount of knowledge, how to manipulate the knowledge is essential for deriving optimal decisions for source reduction. Because of the existence of two types of knowledge representation schemes, different decision-making algorithms are employed. The computation based on the symbolic rules can be accomplished by the following fuzzy MAX-MIN reasoning algorithm (Zimmermann, 1985).

/~j(x)=max {minkEK {t-*,l(XO,tziz(X2) . . . . .

tXik(Xk) . . . . I, ZiK(XK) } } (10)

where/z~k(x k) is the membership function of variable xk of the ith rule; tz:(x) is the membership function of variable x of the rule selected to activate at the jth level.

The MAX-MIN algorithm is implemented in two stages. The antecedent in the IF part of a rule is expressed by the truth value expression,

Tie min {/zil(xi),l~i2(x2 . . . . . tzik(xD . . . . I-*iK(xr) }. (11)

The MIN operation yields a set of truth values through the evaluation of the membership functions of all the rules. Finally, the most appropriate rule is to be selected by performing the MAX operation,

Input Var's

Output Y~r'e

Moosmmd Data

i .

1 I Structure

Amu'ysis

I Model Generation

t

II

1 Parameter

Regression Coeffidents

Model Vafidation I

t Knowledge Ju~ement

Model

Fig. 3. Interactive fuzzy modeling system for the development of hybrid rules.

T= max(~'l,r2 ..... rl}. (12)

The same operation is repeated at the succeeding levels, based on the information received from the preceding levels. An illustrative example, Fig. 4 demonstrates the computation of equation (11) when a rule for drag-out minimization is evaluated. The truth value of the antecedent part is computed, based on the data input by a user. Figure 5 shows the computation process expressed in equation (12), where four rules related to drag-out minimization are used for selection. The selected rule provides most appropriate decisions for WM.

For hybrid rules, the following computational algorithm is utilized. When a set of input data is given, the truth value of the proposition y = y i can be calculated by

Surface Tension

(linguistic)

Solution Temperature- (numerical)

Wetter Adding (crisp)

1

- - l l~ l . t 1

la 2

0

P-3

V L L M H VH

Surface tension

280 290 300 310 320 Temperature

YES

NO

Wetter adding

MIN Operation

min{~l, ~, ~h,)

Drag-out Evaluation

Fig. 4. Decision-making process based on the fuzzy MIN operation.

328 K.Q. LUO and Y• L. HUANG: INTELLIGENT DECISION SUPPORT

I Rule Group 1

I Rule Group2 D2

I Rule Group 3 1.) 3

I Rule Group 4 D4

MAX Operation

max{oz, o2, o3, 04, }

A list of prioritized decisions for the drag-out minimization

Fig. 5. Decision-making process based on the fuzzy MAX operation.

ly=yq = IA =(x°)A...AA,(x°)IAIR'I.

The output y will be inferred from all the fuzzy rules involved as follows:

.y, ly= yil × yi

Y= Ely=yil

This algorithm allows the addition of different weights to the important data spaces in order to obtain more logical and precise estimations of the concentrations of toxic and hazardous chemicals and metals. By this computational algorithm, the optimal WM models should always be selected.

7. SYSTEM DEVELOPMENT

Based on the approach developed here, an intelligent decision support system, namely WMEP-Advisor, has been built for source reduction in electroplating plants. The structure of the system is illustrated in Fig. 6. The knowledge base, database, and system functionality are delineated in this section.

7.1. Knowledge base

As the core of the WMEP-Advisor, the knowledge base contains sufficient knowledge for problem solving. Numer- ous rules of the IF-THEN and IF-THEN-ELSE forms have been implemented. These rules are classified as the functional groups for: (a) identifying WM problems, (b) modifying process and equipment, (c) substituting materials, (d) changing technologies, (e) implementing environmental regulations, (f) utilizing engineers' experience, (g) evaluating WM practice levels, and (h) performing a trade-off among WM efficiency, cost, and operational

(13) criteria. The rules make different contributions to WM approaches. For instance, the rules for process modification and equipment redesign can be used to minimize drag-out, to extend bath life, to minimize the use of rinse water, etc. Figure 7 depicts the relationship between a set of functional

(14) rule groups and a set of WM approaches. The process units, and the entire process, are evaluated

according to their WM efficiency. In the WMEP-Advisor, the rating of WM practice is classified as follows:

Inference ~ngine II Fmcy Decision Mddng M oddl|

X~owiedp Base Data Rase Problem Indentification 51r~egy ] , .

Strategy ] I Process Data J

• Process Modification I [ Waste Stream Data [ • Equipment Modification . . . . . . . • Operation Optimization I Chemical Property Oats I

V~l Rating Strategy ] EPA Regulation Data [

Co= Arledysis Algorill~m I Plating quality Data I

I

User Interface i

| I

User

Fig. 6. Structure of the intelligent decision support system, WMEP- Advisor, for comprehensive waste minimization in electroplating plants.


Rule set (a) rules for identifying WM problems

Rule set (b) rules for modifying processes and

redesigning equipment

Rule set (c) rules for substituting materials

I Rule set (d) rules for changing technologies

Rule set (e) rules for implementing

environmental regulation

Rule set (f) rules for utilizing engineers' experience

Rule set (g) rules for evaluating WM practice level

Rule set (h) rules for trade-offs among WM efficiency,

cost, and operat onal criteria

Fig. 7. Relationship between a set of functional rule groups and a set of W M approaches.

(a) Excellent--all necessary WM strategies have been effectively implemented. The plant is now envir- onmentally clean. No further recommendations for environmental improvement can be made by the WMEP-Advisor.

(b) Good--many necessary WM strategies have been appropriately adopted. Under normal operating conditions, there will be no major WM problems. However, some improvement should be made.

(c) Fair--a few WM strategies have been implemented, but some implementations may be inappropriate. The effluent streams under some conditions are envir- onmentally permitted, but not always. There exist a variety of opportunities for environmental improvement.

(d) Poor--almost no WM strategies have been correctly implemented. The effluent streams are absolutely unacceptable according to the EPA's standards. Immediate actions must be taken.

(e) EPA regulation data--permitted effluent streams of chemical, metal and non-metal pollutants.

(f) Cost data---costs of chemicals, solvents, equipment, and waste treatment.

There are many intermediate data generated during computation. All the data are expressed by various data types such as string, numerical, compound and array. The database structure for two process units is illustrated in Fig. 8.

7.3. Implementation and system functionality

The knowledge-base, database and decision-making algorithms have been implemented as a WM tool, namely WMEP-Advisor, on an IBM PC. The system resides in the Level5 Object, an object-oriented multi-paradigm development environment. It combines expert system techniques,

Batlttank : Presoak

Capaciff : 440 GI

Chemical : LEC 201 C

Source : McGean Rohco

i Concentration: 109~ of Vol

' Temp. : 130 F Bathtank : HCI Double

Capacity : 600 GI

Chemical : HCI

Source : PVS

Concentration: 3 5 ~ of Vol

Temp.: 70 F

Fig. 8, Database structure for two process units in the WMEP-Advisor .

7.2. Database

The database is object-oriented, and contains numerous data, as classified below.

(a) Process data--process flowsheet and unit design information, process operational specifications (temperatures, concentrations, and mass flow rates, etc.)

(b) Chemical data--types of chemicals and solvents, and their chemical and physical properties.

(c) Waste stream data---=types of pollutants and their concentrations in waste streams from different units, and the chemical and physical properties of the pollutants.

(d) Parts and plating quality data--types of parts and plating metals, and their specifications.

An Intelligent Decision Support 3yetem for Cost Effective Waste Minimization in

Eleetroplating Plants

I?,~t,-,~,,em of Cbey,6~ EnlUbteerb ~ and Mamr.b.b Science

&~,lt, 1994

Fig. 9. System Initiation window in the WMEP-Advisor.


Y t ~

Fig. 10. Waste Minimization Option window in the WMEP-Advisor.

object-oriented programming, and relational database models. It has a Graphic User Interface (GUI) and uses a high-level language called PRL, a language similar to natural English. It also provides read/write access to a dBASE III database, and views the database as an object; it

Fig. 11. Plating Metal Selection window in the WMEP-Advisor.

. . . . . J

]annlyas: Produeaon Date: s . . eo l s9,

as: ~ ~,,~,~,.. i I ~ ' ~ f ~ t e m m t a r ~ b y c l i e l ~

Fig. 12. Information and Decision Making window in the WMEP- Advisor.

also has a function of hypertext application development through displays, pushbuttons, tables, and hyperregions (Information Builders Inc., 1993).

The WMEP-Advisor has a user-friendly interface. The first window showing on a screen is the System Initiation window (Fig. 9). A user can click the pushbutton, About .... to learn the functions of the system. By clicking the pushbutton, Continue, the WM Option window will pop up (Fig. 10). This system will ask the user to select one of the three options: source reduction, recycling/reuse, and source waste treatment. If the source reduction option is preferred, the user needs to click the pushbutton, Continue, under the source reduction logo. The Plating Metal Selection window will immediately be activated (Fig. 11). This window lists types of metallic coatings, such as Zn-Acid, Zn-Fe Alloy, Zn-Ca Alloy, Zn-Ni Alloy, and Ca-CN Alloy. Note that different plating processes usually have different types of pollutants in waste streams, and thus should be dealt with in different ways.

As a plating metal is selected, the Information and" Decision Making window, a main window of the system, will appear (Fig. 12). The upper half of the window shows an information-retrieval sub-system. By clicking the pushbutton, First, Last, Next, or Previous, the user can obtain the process information for any bath. This information includes the normal operating condition, along with its upper and lower limits, the chemical used, and the type of wastes possibly being generated. The lower half of the window is a WM decision-support sub-system. The lower left-hand side of this part contains six WM options, including Drag- Out Minimization, Bath Life Extension, Rinse Water Reuse, Cyanide Free Solution, Alternative Plating Metal, and Operational Improvement. After the user selects one of the WM options, the pushbutton, Data Input, will flash to prompt the user to activate a specific data-input window. After inputting data, the user should click the pushbutton, Evaluate, to let the system automatically evaluate WM practice in the plant and, as a result, get a rating, such as Excellent, Good, Fair, or Poor. The user can then click the pushbutton, ?, to view the reasons for the rating result. Furthermore, the user can click the pushbutton, Decision and Trending, to obtain the decisions including cost information and the trending information for the past 12 months.

8. APPLICATIONS

The WMEP-Advisor has been successfully applied to solve a variety of WM problems occurring in plating lines (Huang and Luo, 1996). As an illustration, source reduction through drag-out minimization and bath-life extension are exemplified below.

8.1. WM through reducing drag-out As described in the preceding section, the drag-out is a

certain amount of process solution retained on the surface of parts when they are withdrawn from a process unit. This must be minimized in order to reduce the contamination

K. Q. LUO and Y. U HUANG: INTELLIGENT DECISION SUPPORT 331

level in the succeeding units, and the consumption of chemical solvents and plating solution.

In practice, it is not clear how much the operational setting affects the amount of drag-out. It is extremely difficult, or sometimes nearly impossible, to conduct experiments to determine the relationships between the drag-out and operational parameters. The WMEP-Advisor can provide strong decision support, based on various rules in the knowledge base. A real environmental problem is selected here to show how the WMEP-Advisor works.

A zinc-acid plating line is being used in the operation of plating cup-shaped auto parts. The parts are packed in barrels, with about 200 kg per barrel. The estimated dirt on the parts is 0.0035 g/cm 2. It is required that the dirt residue on the parts after cleaning in the presoak tank be not greater than 0.0007 g/cm 2. This is equivalent to the removal of 80% of the dirt. However, the plant encounters the following problems: (i) the chemical usage has exceeded the amount that a supplier suggests, (ii) a high concentration of the chemical was found in the effluent rinse water, and (iii) plating quality problems were identified by quality assur- ance engineers. An operator wants to know if the operational setting is related to these problems.

To evaluate the drag-out minimization problems by the WMEP-Advisor, the operator needs to select the Drag-Out Minimization option in the WM Option panel. This activates the system to list all related process units in the ALL TANKS panel, as shown in Fig. 12. After the operator clicks the pushbutton, Data Input, the Data Input window for drag-out minimization pops up. As shown in Fig. 13, the operating temperature in the Presoak tank is 26°C (80°F). The chemical concentration and the solution viscosity are 20 vol.% and 0.03 g/cm sec (3 cp), respectively. For each barrel withdrawn from the tank, the drainage time is set to 3 sec. In addition to the numerical data, other information, such as the shapes of parts, estimated surface tension, the addition of wetter, and the installation of up-rotation, should be input.

After inputting data, the user should select the pushbutton, OK, to return to the Information and Decision Making

WHY ??? Cup-shaped workpieces without uwotalon in the process caused excess drag-out.

The concentration of chemical LEC 201C in the bath tank is too high. This caused

excess drag-out.

~ , h surface tension and lack of wletter caused excess d racou t ,

Lack of up-rotation, insuffident drainage time and small-hole barre l caused

excess drag-out.

Fig. 14. Problem Identification window for drag-out minimization in the WMEP-Advisor.

window. For this case, the system gives the rating of Poor after the operator clicks the pushbutton, Evaluate. The operator then clicks the pushbutton, ?, to view the supporting reasons, as listed in Fig. 14. By clicking the pushbutton, Decision, in the Information and Decision Making window, the system can further provide technical decision support with cost information (Fig. 15). For this case, the system suggests the installation of an up-rotation device, to lower the chemical concentration in the process bath, and to add a wetter. The cost will be in the range of $2500 to $10,000, depending on the type of up-rotation device used. The operator can make final decisions based on what the WMEP-Advisor provides. In addition, the system can provide some historical information about WM practice in a particular unit, or in the process plant. Figure 16 demonstrates one example about wastewater generation in the plating process.

8.2. Bath life extension

The extension of a bath's life can reduce the operating cost and the amount of waste in the form of spent solution. Usually, plating plants encounter problems such as unsat- isfactory plating quality, high chemical consumption, and

INPUT DATA : (/~m~l~

To~pe,atu,e ~: [ i - - - - 7 Concentation IVoI.~I:

[LEC 201 C}

Viscosity (cp):

Size of the part [in]:

Hole size of the barrel [in]:

Drainage time Isec.):

Shape of the workpiece

Surface eurasia! of the solution

Addition of welter ;

Installation of all up-rotation

device :

• Tube

Straight T

Very High High Medium

'~ Low Very Low

0 Y e s

• No

0 Yes ® No

You may try to:

] ~ a l l a~up -rotataondesnce to ensure all the workpleces betr~ turned so that chermcal ;olutaons can drain from the workpteces and not be trapped in grooves or cavaaes Estimated rest: An up~otaiton device will cost 02o500-$1 O, O0O.

Keep the chemical concentrauon of the process bath at the lowest acceptable level to reduce draB-out loses Estimated cost: No Capital cost is involved.

Add wetter to the process bath to reduce the surface tension of the sohtaon Ttus wdl alaow you to reduce the volume of drag-ou~ l¢,ss as much as 50 percent Estimated cost; Cost of adding welter is negligible.

nstall ~Ln up-rotation de'~ce, prolon~ the d~amage tame and select a l~ger hole sine b~'rel to reduce excess dr~c-out Estimated cast; All up-rotation devtcc will cost $2.500-$1 O.00O.

Fig. 13. Data Input window for drag-out minimization in the WMEP- Fig. 15. Decision Support window for drag-out minimization in the Advisor. WMEP-Advisor.


You may try to:

Use deiomzed water for process bath makeup and in rmsn~ operataons

Estimated cost: It will cast you about $0.02 per gallon of delonized water.

Use a continuous filtration system to avoid short bat~ life

Estimated Cost: About 8400 to 91.000 are needed to Install a flltraifon system in a tank.

Analyze the bath solution frequently (at least once a week) to avoid short ba~ I~I"e.

Estimated cost: No capital cost is involved.

Use a replemshing strategy to save the ba~ solution.

Estimated cost: No capital cost Is InvolVed.

Fig. 16. Waste Minimization Trending Information window in the WMEP- Advisor.

excessive solution dumping. Thus, how to effectively maintain process baths has both economic and environmental significance. The WMEP-Advisor can provide various suggestions on how to extend bath life in an effective way.

When a user selects the Bath Life Extension in the WM Option panel of the Information and Decision Analysis

Wld Date Input

Data Input : I~'f . . . . . ~l

0 Yes Is deinnized water used in the bath solution ? @ No

¥es Are particles present in the bath solution ? © N o

C) Continuous Is a f'd~ration system used in the bails ? Periodic

O None

0 m Is the bath always replenished ? No

0 ¥es Is the solution analyzed frequently ? No (at least once a week)

Fig. 17. Data Input window for bath-life extension in the WMEP-Advisor.

Fig. 19. Decision Support window for bath-life extension in the WMEP- Advisor.

window, the system will automatically list all the relevant process units in the ALL TANKS panel (Fig. 12). The user should then input the information about the plating line. Figure 17 demonstrates the Data Input window in which all circled dots, &'s , are selected by the user. Note that if excessive particles exist in the process bath, then a continuous filtration system is always needed. In addition, the use of deionized water, the adoption of replenishment strategies, and frequent analyses of process bath perform- ance are helpful in extending bath life. For this case, the system gives the rating, Poor, after the user clicks the pushbutton, Evaluate. The reasons for this rating can be obtained by clicking the pushbutton, ?. The WHY window provides the explanations for this rating (Fig. 18). More- over, the Decision window provides suggestions for problem solving and a cost range (Fig. 19).

The system can evaluate the WM practice for either an individual process unit or the whole plating process. The system also has an information retrieval function. In Fig. 19, if the user clicks on a hyperregion, such as the first decision, "Use deionized water .... " a description window will show much more detailed information about deionized water (Fig. 20).

WHY ??? Use of city water caused a short bath fife.

Perioaie filtration system maycause a short bath fife.

Lack of frequent analysis of process solutions m a y cause a short bath life.

Short bath life is dun to not using rnplonishbtg strategies.

D e i o n k z e d W a t e r

Deionized water can be used to replace tap water for process bath makeup and in rinsing operations The natural contaminants, such as carbonates and phosphates found m tap water can reduce rinse water efficiency, minimize the potential for drag-out recovery, and increase

the frequency of process bath dumping. These corttammants also contribute to sludge volume when removed from wastewater dunng treatment. Deiomzed water will cost about

$0.02 per gallon.

Fig. 18. Problem Identification window for bath-life extension in the Fig. 20. Decision Explanation window for bath-life extension in the WMEP-Advisor. WMEP-Advisor.


9. CONCLUDING REMARKS

Waste min imiza t ion (WM) in electroplating plants is one of the major tasks for the metal-f inishing industries in the

coming decades. Whi le a variety of W M strategies have been developed during the past decade, the effective adoption of these strategies in plants requires sufficient knowledge and ample experience. Unfortunately, the experts are not always locally available. Moreover, the information and process data available for problem solving are almost always imprecise, incomplete, and uncertain.

The W M E P - A d v i s o r described in this paper is an

attractive computer-a ided tool for platers. By resorting to basic chemical engineer ing principles, AI and fuzzy-logic

techniques, the system contains a huge amount of first- principles and heuristic knowledge for process modification, material substitution, technology changes,

process operation optimization, waste separation and treatment, and economic assessment. This allows the system to provide technically the most efficient and economical ly the most desirable options for source reduction.

The system is generic rather than problem- or process-

specific; it a ims at solving practical W M problems rather than only providing theoretical descriptions of the solution.

It can be used in plating plants of any size and any type. Currently, the system is be ing enhanced by adding more

types of plating processes; this will allow the system to solve more comprehensive W M problems. Moreover, a source waste t reatment sub-system is being added, which will greatly enhance the funct ional i ty of the system.

Acknowledgements--This work has been in part supported by NSE EPA, American Electroplaters and Surface Finishers Society, and Hughes Aircraft. Technical assistance from the Reilly Plating Company in Detroit is gratefully appreciated.

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AUTHORS' BIOGRAPHIES

¥. L. Huang is Assistant Professor in the Department of Chemical Engineering and Materials Science at Wayne State University, U.S.A. He received his B.S. from Zhejiang University, China, M.S. and Ph.D. from Kansas State University, all in Chemical Engineering, and was a postdoctoral fellow at The University of Texas at Austin between 1992 and 1993. Dr. Huang's research interests include process design and synthesis, process modeling and control, process simulation and optimization, and industrial process pollution prevention and waste minimization using artificial intelligence, fuzzy logic, neural networks, and large scale system theory. He has published widely in all these fields. K. Q. Luo is a graduate research assistant in the Department of Chemical Engineering and Materials Science at Wayne State University. He received his B.S. from East China University of Science and Technology, China, M.S. in Chemical Engineering from Wayne State University. Mr. Luo's research interests including process modeling, process optimization, and applications of artificial intelligence and neural networks in process modification, operational improvement, and pollution prevention.

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