Screening Alternative Degreasing Solvents Using Multivariate Analysis

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<ul><li><p>Screening Alternative DegreasingSolvents Using Multivariate AnalysisC . T R E V I Z O , D . D A N I E L , A N DN . N I R M A L A K H A N D A N * , </p><p>Civil, Agricultural, and Geological Engineering Departmentand University Statistics Center, New Mexico State University,Las Cruces, New Mexico 88003</p><p>Multivariate analysis was used to explore physicochemicalproperties of organic chemicals that would characterizeand identify degreasing solvents. The exploratory techniquesused in this study include cluster analysis, discriminantfunction analysis, and canonical discriminant analysis. Outof a compilation of 16 physicochemical propertiesevaluated, aqueous solubility, Henrys constant, andsurface tension were identified as relevant properties thatcould effectively screen degreasing solvents from among30 chemicals of similar chemical classes. The suitability ofthese three properties and the multivariate techniquesused in classifying degreasing solvents were demonstratedon an external testing set of 10 solvent- and nonsolvent-type chemicals. On the basis of the results of these studies,canonical discriminant analysis is recommended as apotential tool for screening purposes. The cluster analysisprocedure was informative for explorative purposes; thediscriminant function analysis procedure was not efficientin separating solvents from others.</p><p>IntroductionSolvents are a class of chemicals that can dissolve specificcomponents or break down certain chemicals in a complexmixture into more elementary forms. Because of this property,solvents have been used widely in various applicationsranging from cleaning, degreasing, coating, painting, andextracting to chemical processing, manufacturing, andequipment maintenance (1, 2). In addition to their directuse in the industry, numerous commercial formulations andproducts containing solvents are used on a daily basis in thedomestic, commercial, institutional, and military sectors.Common specific uses of solvents include mobilization ofsolids; preparation of reactants; application of particles ontoa surface for coating; extraction of oil, flavors, and fragrances;thinners for paints, oils, and ink; adhesive for plastics; cleaningprinted circuit boards and machine parts; dry cleaning ofgarments; decaffeinating coffee; etc. (3).</p><p>Over 30 different synthetic organic chemicals have beenused as degreasing solvents. It is estimated that the annualuse of the five most commonly used solvents [viz., trichlo-roethylene (TCE), tetrachloroethylene (PCE), methylenechloride, 1,1,1-trichloroethane (TCA), and trichlorotrifluro-ethane (CFC 113)] in the United States is around 800 000 t(4). Such large usage as well as improper storage and disposalof spent solvents over the past decades have resulted in their</p><p>release into the environment, contaminating soils, ground-water, and the atmosphere.</p><p>Because of their toxic, persistent, and recalcitrant nature,environmental contamination by degreasing solvents hasemerged as one of the serious problems in the industrializedworld. Recent studies have confirmed that many of thecurrent solvents are hazardous to humans and harmful tothe environment, causing (or suspected to cause) cancer,smog formation, ozone depletion, etc. As such, many of thecommon solvents are now targets of public concern andregulatory control. The Environmental Protection Agency(EPA) has included over 20 solvents in their list of 127 prioritypollutants. The Clean Air Act Amendments of 1990 have listedseveral solvents as hazardous air pollutants (HAPs). Theemissions of the most common solvents (viz., methylenechloride, PCE, TCE, TCA, carbon tetrachloride, and chloro-form) are now regulated by 40 CFR, Parts 9 and 63, underthe Toxic Release Inventory (TRI) program, whereby indus-tries are now required to report to the EPA on their productionand transfers.</p><p>In an effort to minimize the release and environmentalimpacts of solvents, industries are being forced to adaptprocess modifications, recycling, and reuse of solvents onone hand and to develop environment-friendly substitutesolvents on the other (2). In seeking substitutes or designingnew ones, it is important to identify or develop solvents thathave the desired degreasing characteristics and, at the sametime, are nontoxic and readily biodegradable and poseminimal threat to the environment. Evaluation of solventsthat are in current use in terms of their physical and chemicalproperties is the first step to characterize the desired featuresof a good solvent and to effectively develop a greener andefficient substitute solvent.</p><p>Selection of substitute solvents is not a straightforwardtask because no single physicochemical property relates tosolvent characteristics. The search for alternate solvents hasbeen characterized as Edisonian because of the trial anderror nature of the experimental evaluation of numerouspotential alternatives (5). While acknowledging this processto be a significant technical challenge, Zhao and Cabezas (2)have identified the following three steps in developingsubstitute solvents:</p><p>step 1: to determine the substitute candidates or thereplacement formulations;</p><p>step 2: to do performance and evaluation tests; andstep 3: to do the full scale test.</p><p>The first step has been recognized as the most importantand most difficult one. Efforts of previous workers in fulfillingthe first step have been classified the into three categoriesby Zhao and Cabezas (2): (i) screening of available solventdatabases for single chemical substitutes; (ii) using computer-based molecular designing tools to develop new chemicalswith the desired properties; and (iii) designing mixtures ofavailable chemicals to achieve desired properties. Severalspecial purpose computer software tools have been devel-oped and are being applied for this purpose (2, 6). The firstapproach of screening databases is a more simple approachand can also enhance the effectiveness of the other twomethods. Irrespective of the approach, identification ofdesired properties for a given application is a prerequisite inseeking substitute chemicals. One of the objectives of thisstudy was to identify physicochemical properties of goodsolvents.</p><p>The second objective of this study was to develop ascreening process based on statistical multivariate analysisof physicochemical properties of good solvents. The screening</p><p>* Corresponding author fax: (505)646-6049; e-mail: nkhandan@nmsu.edu.</p><p> Civil, Agricultural, and Geological Engineering Department. University Statistics Center.</p><p>Environ. Sci. Technol. 2000, 34, 2587-2595</p><p>10.1021/es9912832 CCC: $19.00 2000 American Chemical Society VOL. 34, NO. 12, 2000 / ENVIRONMENTAL SCIENCE &amp; TECHNOLOGY 9 2587Published on Web 05/16/2000</p></li><li><p>of substitute solvents remains a subjective process, dependingon the application and the experience of end-users. Two ofthe commonly employed methods are the weighted-sumevaluation method and the pass/fail screening method. Inthe first method, quantifiable screening criteria weighted byappropriate weighting factors are summed up and comparedfor the alternatives. The criteria used are indirect measuresof the overall effectiveness of the solvent. Some examples ofcriteria are reductions in raw material input, waste quantity,operational hazards, costs, etc. (4).</p><p>The second method involves a step-by-step evaluation ofthe alternatives against yes/no or pass/fail type of criteria.Those that satisfy all the criteria are then selected for furthertesting. Examples of criteria might be as follows: is flashpoint less than or greater than 140 C, is dielectric strengthless than or greater than 20 kV, etc. (7). Proposed solventsthat pass the necessary criteria are then evaluated furtherunder field conditions.</p><p>An expert system software named SAGE is now availableto aid in the screening process (http://clean.RTI.org/sol_alt).Users can run SAGE online over the Internet or downloadit to run on desktop computers to identify possible alternatesolvents. This software first prompts the user to specify thematerial, nature, and shape of the part or surface to becleaned; the contaminants to be cleaned; the degree ofcleaning expected; the process configuration; etc. It thenrecommends a list of possible alternate solvents and pro-cesses that best satisfy the input data.</p><p>The ultimate aim of this study was to develop and validatean alternate screening process to aid the substitute solventsearch process. A statistical exploratory approach involvingmultivariate analysis procedures is adapted in this study.The following procedures are used: cluster analysis, dis-criminant function analysis, and canonical discriminantanalysis.</p><p>Materials and MethodsA training data set of 45 common solvent and nonsolventchemicals was initially compiled as the starting point for thisstudy. The following physicochemical properties for thesechemicals were compiled from handbooks (e.g., refs 8-11)and literature (e.g., refs 12 and 13): boiling point (BoilPt),melting point (MeltPt), molecular weight (MW), octanol/water partition coefficient [log(P)], water solubility [log(S)],vapor pressure (VP), Henrys law constant [log(HC)], surfacetension (ST), solubility parameter (SolP), autoignition tem-perature (AT), excess molar refraction (R), solute dipolarity(), effective hydrogen-bond acidity (), effective hydrogen-bond basicity (R), and the characteristic volume of McGowan(MolarV). The significance of each of these parameters hasbeen discussed elsewhere (e.g., refs 2, 10, and 13). In addition,calculated values of zero-order and first-order simple andvalence molecular connectivity indexes (0, 0, and 1) werealso adapted as additional properties (14).</p><p>From the initial 45 chemicals identified, only 30 chemicalscould be evaluated in this study as a training set due to thenonavailability of all the 18 physicochemical properties. Thesolubility parameter and autoignition temperature could notbe found for several of the remaining 30 chemicals in thetraining set, so these parameters were discarded as variablesin the analyses. Each of the remaining 30 chemicals wasthen identified as a good solvent or a nonsolvent basedupon recommendations in solvent handbooks and usage inindustry. The final training set thus consisted of a total of 30chemicals, classified into 22 good solvents and 8 nonsolvents,each having 16 physicochemical properties.</p><p>Additionally, a testing data set of 10 chemicals consistingof solvent and nonsolvent types was assembled to test anyscreening processes developed from the multivariate analysis</p><p>procedures evaluated in this study. Because of the difficultyin identifying solvents and nonsolvents having readilyavailable the physicochemical properties being examined inthis study, preliminary cluster analyses were performed priorto forming the testing set. These cluster analyses identifiedwater solubility, Henrys law constant, surface tension, andthe zero-order valence molecular connectivity index asphysicochemical properties that were likely to be useful inthe evaluation process. Thus, the 10 chemicals in the testingdata set were selected based upon the availability of thesefour physicochemical properties, while also striving to obtaintesting chemicals that greatly varied in their ability to act asa solvent (e.g., propane is an extreme that must be classifiedas a nonsolvent by any reasonable method). Table 1 lists the30 training set chemicals along with the 10 testing setchemicals.</p><p>To evaluate the screening method developed from clusteranalysis, the 10 testing chemicals were added to the trainingset of 30 chemicals, and the cluster analysis process wasrepeated, noting the placement of the test chemicals in thedendogram relative to the good solvents and the nonsolventsof the training set. Evaluation of the method based ondiscriminant analysis was straightforward, simply giving apredicted classification of each testing set chemical ac-companied by a (posterior) probability associated with theclassification. A canonical discriminant analysis techniquegave a distance measure for each of the testing chemicals,which was then compared to the distance measures of thegood solvents and nonsolvents from the training set.</p><p>Cluster analysis, its accompanying graphs, and discrimi-nant function analysis were conducted in JMP (SAS InstituteInc.). All other graphs and canonical discriminant analysiswere run in SAS (SAS Institute Inc.). All computations andgraphs in both JMP and SAS were carried out on a 233-MHzApple Macintosh G3-based computer.</p><p>Cluster Analysis. Hierarchical cluster analysis is a com-mon, multivariate pattern recognition technique used togroup observations together according to their proximity toone another in the multidimensional space defined by thevariables being studied (15). A cluster is defined to be eithera single point or multiple points grouped together becauseof their relative closeness. To determine the closeness of twoclusters, one must define a multidimensional measure ofdistance between the clusters and also the point of referencein each cluster between which the distance is measured. Inmost studies, though not all, the Euclidean distance basedon standardized variables is used as a distance measure. Thepoints of reference, between which the distance of twoclusters is measured, define the cluster analysis method.The centroid method measures the distances between themeans of each cluster. The nearest-neighbor method mea-sures distances between two observations, one from eachcluster, that are closer than any other such pair. This studyinvestigated use of the centroid method but ultimately foundthe nearest-neighbor method with a Euclidean distancemeasure to be more effective.</p><p>The results of the cluster analysis are displayed in twographssa dendogram and an amalgamation schedule. Thedendogram is a tree diagram connecting all the observations,which are listed to the left, and illustrates the relationshipbetween the clusters that are formed. Clusters that areconnected by lower branches on the tree are closer thanclusters that are connected by higher branches. The amal-gamation schedule is a line chart whose vertexes arehorizontally aligned with the dendograms connectingbranches, having one vertex associated with each mergingof two clusters. The vertical distance between two vertexesindicates the distance between the two clusters that aremerged by the corresponding branch. A plateau acrossvertexes indicates strong similarity among observations, while</p><p>2588 9 ENVIRONMENTAL SCIENCE &amp; TECHNOLOGY / VOL. 34, NO. 12, 2000</p></li><li><p>a sudden change in height indicates a large differencebetween the adjacent clusters.</p><p>In this study, hierarchical cluster analysis was usediteratively on the training data set. Analyses began by usingthe entire set of the physicochemical variables discussedabove to cluster the chemicals from the training set.Numerous subsets of the physicochemical variables werethen tried, including all pairs and triplets, until a minimalsubset of variables was found that clustered good solventsapart from n...</p></li></ul>

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