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Anal. Chem. 1988, 6 0 , 541-544
Monitoring a Dryer Operation Using an Array of Piezoelectric Crystals W.Patrick Carey and Bruce R. Kowalski* Laboratory for Chemometrics, Department of Chemistry, BG-IO, University of Washington, Seattle, Washington 98195
Gas-phase components of evaporated solvent mixtures are monitored by a slx-channei coated plezoelectrk quartz crystal array. The sensor array, which is located in-line on the exhaust line of an industrial process dryer simulator, provides multivariate data on gas-phase mixtures of vapors. Principal component regresslon is then used to predict the vapor concentration of each solvent based on a calibration of pure solvent responses to the array. This analysis results in the capability of profiling the rate of evaporation of each solvent and monitoring of drying progression.
One of the goals of process analytical chemistry is the development and application of in-line analytical instrumentation for the real-time monitoring and/or control of a chemical process. The advantage of real-time analysis is the ability to know more information about the process at any given time. This knowledge allows more efficient control and optimization, thus saving time and money and improving quality. The more common types of process instrumentation on the market consist of gas chromatographs, mass spectrometers, and optical spectrometers (UV-vis, IR, near-IR) converted or designed to handle process streams (1). Although the degree of information about the process from these types of instruments is substantial, they are expensive to buy and operate and in some instances too bulky for application. An alternative approach that solves these deficiencies is the application of chemical microsensor arrays. Microsensors in the array form can play an important role in maintaining the multivariate information capability needed for process analysis while also being adaptable to the process environment due to their small size and ruggedness (2). One such microsensor that is a candidate for process applications is the coated piezoelectric quartz crystal sensor. The piezoelectric quartz crystal commonly consists of an AT-cut quartz with a range of operating frequency between 5 and 20 MHz. The analytical application of gas detection is achieved by applying a polymer coating to the surface of the crystal and monitoring crystal frequency versus change in mass on the crystal due to vapor interactions with the polymer coating
(3-5).
AF = -(2.3
X
104)F A M / A
(1)
The change in frequency, AF, is directly proportional to the fundamental frequency squared, F ,and the change in adsorbed mass, AM, and inversely proportional to the area of the deposited coating material. A variety of sensors have been constructed by the application of coating materials such as quadrol and triethanolamine for the analysis of SO2 and organic vapors, respectively (6). Additionally, the application of an array of quartz crystals for the multicomponent analysis of vapor mixtures of chlorocarbons indicates the versatility of these sensors to perform multivariate analysis (7). The monitoring of an industrial process with an array of microsensors is an important step in the evaluation of their 0003-2700/8S/0360-0541$01.50/0
capability and future EU process instruments. One process that can be used to carry out this evaluation and for which the piezoelectric sensor is ideally suited is bulk drying of materials. In this process, in which large amounts of materials are dried, energy intensive lamps and rotating columns operate on a time scale of 72 h. The degree of dryness is checked periodically by manually taking a sample and measuring the solvent content. Since these checks are performed at intervals, the real-time knowledge of the dryness is unknown. The use of the piezoelectric crystal sensor array provides the unique ability to simultaneously monitor in-line the evaporated gases in order to determine the degree of dryness of the sample. This information can then be relayed to the operators, who can make efficient, cost saving adjustments to the process. The multivariate capability of array instruments is established by the use of mathematical methods such as multiple linear regression (MLR), principal component regression (PCR), and partial least-squares regression (PLS). These techniques take advantage of the response pattern produced by a sensor array for a given analyte to both identify and quantitate that component. The requirements for these methods include existence of a unique response pattern for each analyte, linear additivity with respect to analyte concentration, and representation of all components of an analysis in the calibration. The use of arrays of sensors takes advantage of the partial selectivity of an individual sensor by combining several sensors and examining the relative responses of all the sensors together. The array response pattern can be analogous to an infrared spectrum of an analyte. An analyte's array response must be unique from other analyte response patterns so that linear combinations of two or more patterns do not equal a third pattern of an analyte in the system. This requirement is usually met by having more sensors in the array than analytes in the calibration. The prediction of a component's concentration, Ci, in an unknown sample is performed by the following equation:
Ei = runT R+ci where r,' is the response pattern (column vector) transposed from the array for an unknown sample, R+ is the pseudoinverse of the known calibration response patterns, and ciis the vector of calibration concentrations for analyte i corresponding to the response patterns in R. The pseudoinverse of R is calculated by its singular value decomposition, R = USVT, and recalculating R+ by using the inverse of the singular values in S, R+ = VS-'UT. When all the singular values are used, MLR results; when only the relevant singular values are retained, PCR is performed. The diagonal elements of S contain contain the square roots of the eigenvalues of R, and U and the eigenvectors of RRT and RTR, respectively. This study is focused on the evaluation of a six-element chemical sensor array of piezoelectric crystals in monitoring the evaluation of common solvents. This evaluation is performed on a laboratory rotary evaporator system developed to simulate an actual plant drying unit. The two samples being tested consist of pharmaceutical products supplied by 0 1988 American Chemical Society
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ANALYTICAL CHEMISTRY, VOL.
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1
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Acetone Met. Chloride
6
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m e 1. Dlying slmu$tw l n s b w m t : (A) sample chamber: (B) rotary evaporator; (C) ceramic heater; (D) detector cell; (E) temperature probe; (F) nitrogen cylinder and flow gage.
Flgure 2. Response patterns for each of the four sobent vapors to the piezoelectric crystal sensor array.
Eli LiUy and Co. Sample A contains a two component solvent
wnductivity detector was used for the water analysis and a flame ionization detector was used for all organic solvents.
system of water and acetone, and sample B contains a water, hexane, and methylene chloride system. Multicomponent prediction of solvent concentrations in the exhaust gases is used as an indicator of the degree of dryness of the products. EXPERIMENTAL SECTION The laboratory simulation apparatus consists of a rotary evaporator system, which is connected to a vacuum source and nitrogen purge, and a piezoelectric crystal sensor array detector cell, Figure 1. The purge gas is set at 30 mL/min flowing into the sample chamber with the vacuum source set at 100 mmHg. Heat is applied to the sample chamber via an external ceramic heater controlled to constant temperature. The piezoelectric quartz crystals are mounted in a flow cell located between the rotary evaporator and the vacuum source. In order to eliminate airborne particles at the detector, a nylon filter was fitted over the vacuum tube in the chamber. A sample analysis wnsists of introducing a wet product into the drying chamber, rotating the chamber at approximately 2-3 rpm, heating the chamher to approximately €&70 OC, and monitoring the exhaust with the sensor array. The sensor array instrument consists of the crystals, an oscillator circuit board for each crystal, a multiplexer, a HewlettPackard HP-5384A frequency wunter, and an IBM PC-XT. The multiplexer, which is wntrolled by the wmputer, allows the output signal of an individual circuit hoard and crystal to be channeled to the frequency counter. The frequency is then acquired hy the computer via an IEEE-488 interface. The detector cell is composed of six coated 9-MHz quartz crystals housed in a Plexiglas block with sealed wire leads where the rrystsls are arranged horizontally with respect to the flowpath. The coatings used for the six crystals are ( 1 ) his(2-ethylhexyl) sebacate, (2) ethylene glycol phthalate, (3) 1,2,3-tris(Z-cyanoethoxy)propane, (4) DC-710, (5) OV-225, and (6) SE-54(hereinafter the above numbers will be used when referencing coatings in figures). The selection of wtings materials out of a larger group of coatings for the sensors was performed by a method based on principal component analysis (8). Each crystal is coated to an approximate 500Q-H~shift in frequency. A temperature probe (thermistor) is placed in the exhaust tubing at the detector cell to monitor gas temperature. Six sensors were selected in order to maintain unique response patterns in each calibration since a maximum of only three solvents were used in each sample. These solvents being widely diverse in molecular structure provided enough differential response to the array, Figure 2, that additional sensors were not needed. In order to correlate the drying process to the detector signal, a calibration scheme based on the measurement of the solvent content of the wet material by gas chromatography was estahlished. During the drying process, samples are drawn from the chamber and analyzed. This analysis is performed by extracting the solvent from 100 mg of the sample with acetonitrile (1mL) for sample A or dimethylformamide (1mL) for sample B. The extracting solvent contains an internal standard that is used to ratio the peak areas of the sample analysis to a standard mixture. A fused-silica capillary column (25 m X 0.32 mm id.) with FFAP stationary phase was used for all analyses. Additionally, a thermal
RESULTS AND DISCUSSION The calibration method used in this study is principal component regreasion (PCR) as denoted in eq 2. This method is the primary choice since it performs well within collinear data (9). An alternate method of partial least squares regression was first attempted but ruled out due to the lack of calibration samples used in creating the model. The means of calibration was to use the sensor array response patterns measured when the pure solvents were tested in the apparatus, Figure 2. From this data, a calibration model that best relates the degree of dryness of the sample to the sensor responses of the array was established. The main difficulty in this type of analysis is that there is no linear relationship between the sample dryness and the amount of solvent vapors reaching the sensors. During a drying process, the evaporation rate of a solvent is wnstant giving a steady signal at the detector. However, the solvent content is decreasing at the same rate as the evaporation, but the detector in the gas phase does not respond to this decrease in concentration, only the constant flux of vapor reaching it. Therefore, the response patterns at each of the array scans of the exhaust vapor during a material drying run were used for predicting the vapor concentrations of the solvents based on the calibration response patterns of the pure solvents. This information gives the solvent concentration values in the gas phase and can then be related, if desired, to the calibration data from the gas chromatographic analysis to confirm the drying progression. The drying process of any substance, at a constant temperature, follows an evaporation pattern in which only a fraction of the solvent dries from the material dependent upon the various energy states that exist between the solvent and the sample. These energy levels depend on whether the solvent is part of the crystalline structure or is excess or bulk solvent. The heat energy going into the sample drying chamber is fmt used to drive off the lower boiling substances, similar to a distillation, and also will drive off the bulk or excess solvent over the solvent directly adsorbed by the solid sample. An example of the drying process of sample A measured by summing all of the sensor responses is shown in Figure 3 (each scan is 0.42 min). The initial spike is due to the fast evaporation of acetone, and water constitutes the bulk of the drying. The second spike is due to a change in pressure when the sample chamber was opened for sampling. By the end of this response curve, the semors detect low levels of vapor in the exhaust, but the sample bas only evaporated half of its water content. The analysis of the sample A included the monitoring of water and acetone vapors simultaneously. The initial sample contained approximately 10% water and 3.5% acetone by weight and was a crystalline powder with various sizes of lumpy material up to a quarter of an inch depending on
ANALYTICAL CHEMISTRY, VOL. 60, NO. 6, MARCH 15, 1988 400
Table I. Calibration of Drying Processes by Gas Chromatography
300
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time, min
water (w/w), 9~
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time, min
water (w/w), %
hexane (w/w), ppm
3.8 13.0 37.4 52.5
7.8 6.6 5.8 5.3
76.2 71.4 46.4 46.9
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200
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time, min
water (w/w), %
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4.2 14.7 33.6 63.0 105.0 125.6
9.3 10.3 5.2 3.5 4.6 7.8
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solvent content. The specifications for the drying process allow for water less than 4% and acetone less than 2%. The analysis of the calibration samples taken from the drying process and their times of sampling are listed in Table IA. The prediction phase of the analysis used a calibration model based on the pure response patterns of water and acetone, Figure 2, to determine the vapor concentrations by eq 2. The drying profiles for water and acetone, Figure 4, show the predicted concentrations of the solvents in the exhaust vapor over the entire range of the drying process. The acetone was quickly dried as is also evident from the calibration samples in Table IA, and the water vapor concentration follows a drying rate similar to the calibrated values. There is some uncertainty in the calibrated values due to both the error in the GC analysis and error in the representative sampling of the material (lumps vs powder). Spikes in the sample profiles occur when the calibration sample is taken. The opening of the chamber induces pressure and flow changes sensed by the piezoelectric crystals. A good example of the solvent-sample interaction effect is evident in Figure 5. This figure shows the prediction of hexane and water concentration in sample B. Sample B was first studied with only hexane and water and then with the addition of methylene chloride. The physical nature for this compound was very fine powder with hard rock type formations up to 1/16 in. again dependent on solvent content. For the two-component solvent system, the calibration data is given in Table IB. For the first half of the analysis, the prediction results follow closely with the rapid evaporation of solvent from the sample. However, this analysis clearly
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shows the difference between bulk and crystalline solvent by the time 20 min was reached. The bulk of the solvent was evaporated, the samples seem to become constant in solvent content (Le. the last two calibration results in Table IB), and the sensor array analysis shows relatively no solvent vapor in the exhaust stream. An additional test that was performed in this drying analysis resulted in the large downward spikes in Figure 5 when the reference samples were taken. These spikes were due to a nitrogen blow-back through the detector and into the sample chamber in order to clean the particle filter. As can be seen from the prediction plot, there was no direct effect on the signal after its execution. A unique signal structure occurring in the prediction profiles of Figures 4 and 5 is the small oscillation of the predicted vapor concentrations. Although it was not verified, it seems to correspond to the rotating of the sample chamber. The explanation that would best indicate this is the source of the pattern is that due to the slow rotation of the chamber, the surface of the sample exposed to the nitrogen flow and reduced pressure becomes dry and the solvent vapor concentration decreases until fresh sample is uncovered by rotation giving a quick release of solvent vapor.
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The last drying analysis was performed on sample B with a three-component solvent: water, hexane, and methylene chloride. This calibration, as in the above cases, included the three pure response patterns for the solvents, and the predicted concentration profiles are presented in Figure 6. In this analysis the solvent-sample interaction effect is evident as well as the distillation effect mentioned above. By examination of the solvent content in the sample, Table IC, the trend of drying follows closely with the drying profiles. Methylene chloride, since its boiling point is much less than hexane and water, evaporates rapidly and completely (analogous to acetone in the first sample) by 13 min. After 13 min the sample uses the heat flux input to start evaporating the hexane and water. This is exactly the same process that occurs in a simple distillation. The hexane and water concentrations follow a similar drying course corresponding to the degree of dryness listed in Table IC. The water content is relatively constant for 15 min, denoting no evaporation until the methylene chloride was released. When 63 min is reached, the concentrations of the two solvents in the sample are relatively constant indicating the drying process is complete. Because of the high degree of collinearity (similarity) in the sensor response patterns of the five components, there is a lack of fit at some portions of the drying profiles. These areas exist where one of the components is dried, and another component is evaporating. Examples of this lack of fit are acetone in the first sample, hexane in the second sample, and methylene chloride in third sample, all showing slight negative concentration values. This is due to calibration error in creating a model that does not exactly represent the actual
gas-phase exhaust. This is partly due to the absence of the sample material being present when the pure solvents are analyzed for the calibration. There is also error present in the sensor responses that occurs from slight thermal drift due to changes in vapor heat capacities (the sensors were not corrected for temperature changes), random noise, and interferences from possible unknown vapors coming off of the samples themselves. Further study with the sensor array should uncover solutions to these problems. The lifetime of the individual sensors in this analysis was approximately 3 weeks of operation on a daily basis. The coatings were changed between each of the samples used. The most important device for upholding the integrity of the sensors is the nylon filter. Small particles can be released and carried from the sample during the drying process and the sampling directly to the sensors. This effect is significantly increased with the fine powder of sample B in the above analyses. This study has provided evidence that an array of coated piezoelectric crystals can be used to monitor the drying process of organic samples. There exists a correlation between sample dryness and predicted exhaust vapor concentration as shown in the three cases examined. Although the actual evaluation of the array for process monitoring can not be proven until the actual application to the industrial process, the feasibility for the application is positive.
ACKNOWLEDGMENT The authors acknowledge the support of Michael Fogarty and Frank Akey of Eli Lilly and Co. for the supply of samples and materials and the effort of Nancy Mar in the laboratory experiments. Registry No. Quartz, 14808-60-7.
LITERATURE CITED Clevett, K. J. Process Analyzer Technology; Wiley: New York, 1986. Carey, W. P.; Kowalski, B. R. Anal. Chem. 1988. 5 8 , 3077-3084. Sauerbrey, G. 2 . Phys. 1959, 155, 206-222. King, W. J.. Jr. Anal. Chem. 1964, 3 6 , 1735. Hlavay, J.; Guiibault. G. G. Anal. Chem. 1977, 4 9 , 1890. Karmaikar, K. H.; Guilbault, G. G. Anal. Chim. Acta 1974, 7 1 , 419. Carey, W. P.; Beebe, K. R.; Kowalski, B. R. Anal. Chem. 1987, 5 9 , 1529-1 534. (8) Carey, W. P.; Beebe, K. R.; Illman, D. L.;Hirschfeld, T.; Kowalski, B. R. Anal. Chem. 1986, 58, 149-153. (9) Mandel, J. J . Res. Natl. Bur. Stand. ( U . S . ) 1985, 9 0 , 465-476.
(1) (2) (3) (4) (5) (6) (7)
RECEIVED for review July 28, 1987. Accepted November 4, 1987. This work was supported in part by Eli Lilly and Co. and the Center for Process Analytical Chemistry, a National Science Foundation University-Industry Research Center.
