Screening Design of Experiment (DOE) Applied to Supercritical

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Screening Design of Experiment (DOE) Applied to Supercritical Antisolvent Process Pascale Subra* and Patrick Jestin Laboratoire d’Ingenierie des Materiaux et des Hautes Pressions, C.N.R.S., Institut Galilee, Universite Paris XIII, avenue Jean-Baptiste Clement, 93430 Villetaneuse, France

This paper proposes to approach the supercritical fluid antisolvent process (ASES) by design of experiment (DOE). As screening designs, they allow for the identification of key variables at an early stage of experimentation, with only a few experiments. Seven factors have been studied, to which two levels were assigned. A fractional factorial design of 8 experiments, plus 3 additional runs to improve the precision of the estimates was performed, instead of the 27 experiments required by a full design. The process responses are the yield, the dryness of the produced powder, and the mean particle size. The screening design tends to indicate that recovery and drying responses use the same selection of factor levels for best operation, i.e., a low temperature, high flow rates for both solvent and antisolvent, and an extensive drying time. These responses are also quite insensitive to pressure and concentration. The relative importance of process parameters on the mean particle size is different, suggesting that a compromise in operating conditions should be found to obtain simultaneously acceptable levels of yield, dryness, and particle size. 1. Introduction Crystallization processes based on the antisolvent effect are largely used in industry. They aim to produce solid particles from a mother liquor by addition of a liquid that is miscible with the solvent of the liquor and that is a nonsolvent for the solute. As the nonsolvent is added to the mother liquor, it decreases the solvating power of the solvent and causes the solute to precipitate. The solid particles are further recovered from the resulting mixture of solvent and antisolvent by filtration, washing, and drying. Supercritical fluids (SCFs) have recently been proposed as alternatives to liquid antisolvents in crystallization processes. Advantages that fluids may offer have been discussed recently through comparisons with equivalent conventional routes of crystallization.1 Interest is driven by both property considerations and process standpoints. Indeed, diffusivities of fluids within liquidssand vice versas can be 2 orders of magnitude higher than those of liquids. The supersaturation of the solution is therefore obtained on a shorter time scale, which is known to favor a narrow particle size distribution (PSD). From a process standpoint, the use of supercritical fluid reduces waste streams generated during the crystallization step and also wastes associated with posttreatment of the produced solids. Indeed, when the process is performed in a continuous mode (ASES for aerosol solvent extraction system), the solvent of the liquor is continuously transferred into the fluid phase that flows through the reactor. Crystallization and drying thus occur simultaneously and leave, in optimal conditions, the product free of solvent. Thus, no postprocessing treatments such as filtration, washing, and drying that are waste generators are required. Experimental variants of the supercritical-based processes and applications have been reviewed re* Author to whom correspondence should be adressed. E-mail: [email protected].

cently.2 There are almost two modes for the antisolvent techniques: a batch process in which the CO2 antisolvent is added gently to the mother liquor (oftenly referred as GAS or SAS) or a continuous process in which the mother liquor is sprayed into a continuous flow of CO2 (referred as ASES). Both have advantages regarding the control of nucleation, growth, agglomeration, or attrition that further condition the powder characteristics, but for industrial development, a continuous process may be preferable. Applications are now proposed at the laboratory scale for explosives, food or pharmaceutical compounds, coloring matter, superconductor precursors, and polymers. However, despite this broad field of applications, there is still little fundamental understanding of the process and, more specifically, of the ASES variant. Studies focus on the influence of selected process parameters on the product characteristics, which are, typically the particle morphology and size. The parameters whose influences are investigated are usually pressure, temperature, and solute concentration in the mother liquor. As pointed out in a recent review,3 authors have obtained contradictory results regarding the influence of a given parameter. A reduction of pressure or temperature has been found to have no influence by some authors and to produce a particle size increase or decrease by others. Particle size has also been found to be relatively insensitive to solute concentration by some authors, whereas a marked increase and a PSD enlargement with increasing concentration has been observed by others. It is probable that different process arrangements and different kinds of materials are the cause of such discrepancies in the process trends. It is also possible that the influence of the parameters is not universal, in that the effect can be relevant in some cases and less pronounced in others. These former considerations drove us to investigate the influence of parameters on the process response by the statistical approach of “design of experiments” (DOE), which has

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the advantage of considering the experimental conditions as a whole set, instead of considering the change of only one parameter at a time, while the others are maintained constant. The objective of this work is thus to propose a DOE approach for screening the pertinent factors that influence the performance of a continuous supercritical antisolvent process. The DOE is a suitable technique that allows for an estimation of the effects of process parameters (“factors”) on the process outcomes (“responses”). When performed for a screening purpose, assessment of two levels for each factor is suitable, as the objective is to understand the process behavior rather than learn everything about the process. Although some information can be lost in a screening DOE, it allows for the identification of the key variables of the process in a early stage of experimentation. A supercritical-fluid-based technology must prove its competitivity toward a more classical liquid-based technique not only in terms of the product quality but also in terms ofeconomics. To fulfil this aim, the SCF technology should be able to produce particles of desired size and PSD, which are dry so that no expensive postreatments are required, with no or “acceptable” loss of product. Therefore, there are three criteria to evaluate the process performance: size, yield, and dryness. 2. Materials and Process With the aim of optimizing a process regarding also the dryness of the product, we selected a polymeric compound as a model solute. The processing of polymers is much more complex than that of organic compounds. polymers have the ability to fuse and to form films or fibers in addition to spheres, because of the complex phase separation involved in the precipitation pathway.4-6 Polymers can also absorb dense gases, including CO2. The effect of this CO2 sorption is a swelling and a plasticization (depression of the glass temperature) that affect the agglomeration of the primary particles and yield softened or fused structures.4 Therefore, because of their complex behavior in the presence of supercritical fluids, polymers seemed good candidates for assessment of the process ability in recovering a dry product. The compound selected as a model is a polysaccharide, i.e., Dextran, MW 40 400, purchased from Pharmacia. When micro- or nanometer-sized, this compound could be used as a biopolymer in drug delivery systems, if cross-linked. That reaction will be further studied, either as posttreatment of the submicronic particles or as part of the precipitation process. The starting material consists of a powder of polydispersed hollow spheres whose diameters range from 5 to 100 µm. Dextran is highy soluble in water, quite soluble in DMSO (Csat ≈ 44 mg/mL or 4% w:w) and insoluble in supercritical carbon dioxide. Water is not soluble enough in carbon dioxide to be completely removed during the process. One can used an alcohol mixed with the CO2 to enhance the solubility of water,7 but this will add another factor to the process design, e.g., the composition of the fluid phase. Therefore, DMSO is preferred, as many examples in the literature dealt with this solvent. DMSO is from Carlo Erba (99.5%, RPE grade). Carbon dioxide from Airgaz is of 99.95% purity (technical grade). The apparatus scheme is given in Figure 1. The equipment consists of two pumps that deliver the liquid and the carbon dioxide (Thermo Separation Products Dual Minipump and Lewa EK3, respectively). A home-

Figure 1. Schematic representation of the continuous antisolvent apparatus.

made disk nozzle is used to spray the liquid solution into a high-pressure vessel of volume 490 cm3 (TOP Industrie, France; 250 × 50 mm). At the bottom of the precipitator, a stainless steel frit covered by a polyvinylidene fluoride membrane of 0.22 µm porosity (Millipore,GVWP) allows for the collection of the particles. The liquid solvent is recovered from the fluid phase after the depressurizing valve in a homemade cyclonic separator. A typical experiment begins with carbon dioxide being allowed to flow through the reactor until constant conditions of pressure (P), temperature (T), and flow rate (FCO2) are achieved. The liquid solution of dextran in DMSO is then introduced via the nozzle at a given flow rate (Fliq). An effective volume of 15 mL is sprayed. Then, the liquid flow is stopped. The CO2 flow continues to wash out the mixture formed by CO2 and DMSO and to remove the residual solvent within the powder produced. This time is called in the following “the washing time”. The precipitator is then gently depressurized. The powder is collected on the walls and the membrane. Mass balance between the sprayed quantity of solute and the collected dried part allows for a calculation of the percent yield. Particles were then characterized by scanning electron microscopy (SEM, Leica S440). 3. Designed Experiments A common method of investigating parameters’ effects on a process is to change only one factor at a time and to notice its influence on a given response. Although this method has the advantage of being simple, it requires a large number of trials and does not point out the possible interactions between several factors. The DOE method, in contrast, consists of simultaneously changing the levels of several factors, thus reducing the number of experimental trials and allowing a larger number of factors to be studied. To identify the key variables of the process, a two-level fractional factorial design was selected. Such designs are very popular in research and development of products and processes, because they require smaller sample sizes and because the associated statistical analyses are very simple. Indeed, the minimum number of experimental runs needed for a factorial experiment can increase rapidly as more factors are added to an experiment. As an example, to study factor A at three levels, factor B at two levels, and factor C at four levels, a minimum of 3 × 2 × 4 ) 24 runs are needed, one run for each different combination of factor levels. As a consequence, the costs of resources needed

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to conduct a full factorial experiment can quickly become prohibitive. One method for combating the problem of extremely large numbers of runs is to use only two levels of each factor of interest. Using this approach, to study k different factors, each having only two levels, the minimum number of experimental runs needed is 2 × 2 × ... × 2 ) 2k, which is the reason such experiments are called 2k factorial designs. As shown in the next section, we have seven factors to study. A full design would require that 27, i.e., 128, experiments be conducted. The full design, in which all the 2k tests are performed at least once, expend a large amount of resources in estimations of the interaction terms between factors. That is, as the number of factors k increases, the ratio of the number of main effects to the total number of effects shrinks rapidly.8 For instance, in a full 26 experiment with 64 test runs, only 9.5% of the effects calculated are the main effects, i.e., the average response tends to vary as a factor A changes from level A1 to level A2. The remaining 90.5% of the estimates are devoted to interaction effects, many of which are not likely to be of statistical or practical importance. To reduce this problem of estimating large numbers of possibly unimportant interaction effects, the creation of fractional factorial designs is a common route. A fractional design is created by replacing some of the high-order interactions terms by an additional experimental factor. Starting from the design matrix, high-order interactions between factors A, B, and C are created by multiplying the corresponding entries in columns A, B, and C to obtain a new column ABC. In fractional design, an additional factor D is assigned to that column ABC. D and ABC are said to be aliases of one another. The particular fractional design now created is denoted as 2k-p, where p is the number of factors aliased. The reward for using such a fractional factorial design is a substantial reduction in the required number of test runs. However, there is a price to pay. What is lost in a fractional design is the ability to clearly distinguish some of the effects from one other. In the previous example, assigning D to the ABC column implies that the D effect is confounded with the ABC effect. If the ABC effect can be considered negligible, then the observed effect can be attributed to D. Although some information can be lost, fractional factorial designs are very helpful as screening designs, because they allow for the separation of the important effects from the unimportant effects at an early stage of experimentation.8 They are well-suited for the study of a large number of factors with a small number of runs. Furthermore, for screening purposes, only one test run for each combination of factor levels can be performed, so that normal probability plots can be used to analyze the fractional design. 3.1. Factor Identification. For a solute/solvent/ antisolvent system, seven process factors have been identified: (1, 2) pressure and temperature of the precipitator, P and T, respectively; (3, 4) antisolvent and solvent flow rates, FCO2 and Fliq, respectively; (5) solute concentration in the DMSO solution, C; (6) washing time, tw; and (7) nozzle diameter, dn. Thus, a full design requires a total of 27 runs. The design is reduced to 27-4 by using an alias structure. 3.2. Level Identification. Table 1 summarizes the two levels for each factor. The high and low levels were assessed regarding the effective flow rate pump limits and the solute concentration to obtain an appreciable

Table 1. Two-Level Assessment for Each Factor factors

low level

high level

T (K) P (MPa) C (% w:w) FCO2 (cm3 min-1) Fliq (cm3 min-1) tw (min) dn (µm)

308.15 10 1 32.5 0.85 45 50

328.15 20 3.2 102.5 5.5 90 100

Figure 2. Plot of the experimental runs in the phase behavior of the system CO2/DMSO at 308.15 and 328.15 K. See Table 2 for run number identification.

quantity for the yield calculation while staying below the saturation. The pressure limits were assigned in order to keep reasonable compression costs. The upper limit on the temperature was based on previous experience with the process while aiming at reducing energy costs and similarly for the washing time. The lower temperature limit was given by the critical temperature of carbon dioxide, with an initial aim of staying within the supercritical domain. However, because the CO2 phase is enriched with solvent, it is the phase behavior of the CO2:DMSO mixture that must be considered to assess the mono- or biphasic character of the fluid phase. The bubble-point curve was thus roughly extrapolated from literature data9 using the Peng Robinson equation of state with one binary interaction parameter. Curves are shown in Figure 2. Flow rate combinations yield compositions that range between 65 and 99% mol:mol of CO2. The eight experimental trials and the trials for the variance investigation are set in the diagram. Trials are numbered by reference to their rank in the full design matrix of experiments, built according to the Yates order. Symbols refer to the temperature of the experiment. As an example, run 29, performed at 308.15 K, stays above the corresponding bubble curve. The fluid phase that left the autoclave was thus a monophasic mixture. It appears that, within the levels on pressure, temperature, and fluid-to-liquid ratio, experiments were performed either on a twophase or a one-phase system. The coding scheme used to describe the factor levels is based on the + and - signs, where + denotes the high level of a factor and - the low level. The full design matrix is created according to the Yates standard order.8 3.3. Response Identification. Three responses were studied: yield, dryness of the produced powder, and mean particle size, because these are the criteria that the supercritical antisolvent process must fulfill to

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Figure 3. SEM images of powder obtained for two sets of conditions. Upper and lower images are #113 and #29, respectively. See Table 2 for condition identification.

compete with liquid-liquid technology. Dryness is not a characteristic frequently reported in literature studies. However, some authors mentioned agglomeration or formation of networked structures, which, in a sense, can be considered a result of insufficient removal of the solvent from the species. For some biodegradable polymers, Bodmeier et al.10 obtained an agglomerated mass of polymer retained on the filter. They considered that CO2 acts as a plasticizer, resulting in swelling and agglomeration of the polymer. Agglomeration was also observed in the continuous process of the hyaluronic acid ethylester dissolved in DMSO, performed typically at 85 bar and 40 °C (Benedetti et al.11). The solid product appeared agglomerated in large blocks, although some spherical structures could be attached to the main blocks. The authors considered this swollen structure as a result of micronic particles fused together. Reverchon2 also reported formation of networked particles, as either a consequence of formation of a liquid phase or a coalescence mechanism. Because of the insufficient solubility of the liquid solvent in the supercritical antisolvent, the particles were formed by precipitation from the liquid phase at the bottom of the precipitator, during the washing step of the precipitation chamber. A coalescence mechanism of a chemical nature in which particles interact with the liquid solvent would also result in the fusion of nanoparticles in groups in which single particles do not have distinct identities. In the

case of the trials reported here, we found no correlation between the dryness of the powder and the biphasic/ monophasic behavior of the system (see Figure 2). The strong interactions between dextran and DMSO, which are evidenced by dextran’s high solubility in DMSO, cannot be overcome with carbon dioxide, so that the complete removal of the liquid from the polymer-rich solution is probably very difficult to achieve. That observation points out the necessity of ternary diagram description, especially when polymers are involved, for a full understanding of the process behavior. The dryness characteristic is expressed numerically as: 0 for insufficient drying, i.e., a sticky paste is obtained; 1 for a moderate efficiency, i.e., sticky paste and powder coexist; and 2 when primarily dry powder is obtained, but viscous droplets still exist. The residual content of DMSO in the powder was not measured. However, a qualitative test was performed to check the powder stability. Powders that have a characteristic dryness of 1 re-agglomerated as a solid block within 72 h, whereas those of grade 2, remain as a powder during the same period. The yield in percentage is calculated as the ratio of the collected amount and the processed amount. Particles are collected on both vessel walls and the vessel bottom. When sticky droplets or viscous films are obtained apart from the bulk powder, they are discarded and are not considered in the yield calculation. There

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Table 2. Design Matrix, Responses, and Contrastsa run 20 29 39 42 70 75 113 128 yield dryness size a

C

T

1 + + + + -6 0 375

P

Fliq

FCO2

tw

dn

2

3

4 ) 123

5 ) 12

6 ) 23

7 ) 13

I

yield

dryness

size

+ + + +

+ + + +

+ + + +

+ + + +

+ + + +

+ + + +

+ + + + + + + +

0 76.5 19 60.5 0 0 41 52

0 2 0 2 0 0 2 2

300 1000 70 120

-26.75 -1 -336

11.5 0 325

main effect of each column on 32.25 22.5 24.0 -15.75 1 1 1 0 403 836 97 -555

responses

62.25 2 372.5

Units: yield in weight %, particle size in nanometers, and dryness in arbitrary units.

are several reasons for low values of the yield. Obtaining a powder implies that both the crystallization and the solvent-removal steps were effective. The driving force for crystallization is the supersaturation of the solution, i.e., the gap between the local concentration and the equilibrium value. When the local concentration exceeds the equilibrium value, the system tends to regain the equilibrium concentration by forming a solid phase. Although a supersaturation above 1 should be sufficient, there is an effective supersaturation ratio that has to be reached to result in an effective appearance of crystals. In the ASES process, conditions probably exist in which the supersaturation is not high enough to induce nucleation of crystals, such as low initial concentration, high solubility in solvent/antisolvent mixture, large metastable zone,1 etc. As a result, no powder is obtained, because crystallization does not occur. A failure in solvent removal also leads to a failed process. Indeed, conditions that give an insufficient solubility of the liquid solvent in the supercritical antisolvent lead to accumulation of a liquid phase at the bottom of the reactor. If some particles have already nucleated within the sprayed droplets, they redissolve in the liquid pool. They may further precipitate during the washing time, but different morphologies such as networks, blocks, or films will probably be obtained because of the different mechanism and time scale of precipitation. The third reason for low yield is a cosolvent effect of the liquid solvent with respect to the solute. As a result of cosolvent action during precipitation, the solute is solubilized in the liquid-fluid mixture that flows at the outlet of the reactor, so that no solute or small quantities of solute will be found in the precipitator. Bleich12 reported that no particle formation occurred in the reactor when the amorphous polymer PDLA of low molecular weight was used, but precipitates were obtained in the downstream separator, thus supporting the hypothesis of precipitate extraction with the continuous phase. For another polylactic polymer, yields ranged from 20 to 90% depending on the combination of pressure and temperature used, but no trend could be concluded.13 As discussed, the yield reflects the combination of several effects that are impossible to separate in the absence of a fundamental understanding of the kinetics and thermodynamics involved in crystallization in supercritical mixtures. Particle morphology and size are the criteria usually reported in the literature. For polymeric compounds, authors underline the influence of process parameters on the morphology and the agglomeration tendency of the polymer particles, instead of the size. For the

micronization of HYAFF from a DMSO mother liquor,11 it was observed that the CO2-to-liquid flow rate ratio did not affect particle morphology, that solute concentrations act mostly on aggregation and produce less agglomerated particles when high concentration are used, that pressure is not an operating variable because it has to be kept at a minimum level to ensure nucleation and precipitation, and finally, that temperature in the permitted operating range did not appreciably influence the dimension and shape of the precipitated material. The absence of a pressure or density effect on particle size was also observed in case of a poly(L-lactide) polymer.13 Spherical particles that formed partially agglomerated were in the 30-100 µm range. It was shown that temperature acted mostly on the agglomeration of particles, depending on whether this parameter settled above or below the polymer glass transition temperature. Particles produced above the glass transition had a greater tendency to form agglomerations, so that the particles sticked together, leading to a tight connection. In this work, particles were characterized from SEM pictures. Two pictures are shown in Figure 3, corresponding to matrix runs #113 and #29, respectively, in Table 1. The mean sizes were estimated manually. 4. Results and Discussion The fractional design is a 27-4 design, meaning that among seven factors, four were aliased. Design generators and alias structures were found by referring to Devore8 and Sado.14 Results are listed in Table 2. To illustrate the choice of aliases, consider the 123 column. We chose to alias Fliq to the 123 column because we hope that the interaction of concentration/temperature/pressure would have a negligible effect compared to the flow rate effect. Among Fliq, FCO2, tw, and dn, we thought that the liquid flow rate was the factor most likely to affect process performance. Indeed, according to jet break-up theory, higher velocities favor atomization. Mass transfer at the interface of a smaller droplet is accelerated, so the time scale for solute nucleation and growth is reduced, leading, in theory, to small particles with a narrow size distribution. On the other hand, interactions between three factors are less probable than interactions between two. Therefore, by confounding the less probable interaction 123 with the liquid flow rate, we hope that the calculated effect of column 4 will mainly be due to the flow rate. In fact, the choice of aliasing Fliq to the 123 column induces more confounding effects than was first imagined, because it also

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aliased other factors. The entire set of aliases is the alias structure. Design generators that defined relations of the design and the alias structure are then found. The next step is to understand what is included in each column. As an example, we know that column 1 contains the effect of the concentration. To determine the complete signification of that column, a simple procedure involving the alias generators and contrast calculations is performed. With the hypothesis that interactions between at least three factors are negligible frequently verified, the contrast for each column is expressed as

[

h1 ) 1 + 25 + 37 + 46 h2 ) 2 + 15 + 36 + 47 h3 ) 3 + 26 + 17 + 45 h4 ) 4 + 35 + 16 + 27 h5) 5 + 12 + 34 + 67 h6 ) 6 + 23 + 14 + 57 h7 ) 7 + 13 + 24 + 56

]

Therefore, the effect issued from column 1 reflects the effect of the concentration plus the effects of various interactions. For screening purposes, the interactions between two factors are also considered negligible, so the hi values reduce to the principal factor, i.e., concentration for column 1, temperature for column 2, and so on. To verify that response is based on main effects and not on aliased interactions, a complementary set of experiments should be designed. The procedure is to choose from among the initial set of alias generators the one whose sign modification will induce the desired effect separation and then to combine the contrasts of the two DOE sets. As discussed above, the effect corresponding to each column represents the effect of the associated factor when interactions are assumed to be negligible, as in a screening design. The effect calculated for each column is reported at the bottom of Table 2. That term is the average response value for trials performed at the high level of the factor, minus the average response value for runs at the low level of the factor, i.e.

effect )

1 N+

∑y+ - N+ ∑y1

An effect graph can be plot, as in Figure 4a for the yield response. The plot shows whether the effect increases or decreases the response value as we change the factor from the low level to the high level. As an example, changing the temperature from 308.15 KK to 328.15 K appears to cause a decrease of about 6 in the yield variable, whereas an increase of the liquid solution flow rate noticeably increases the yield. The lower slopes of the lines connecting the two average response values are for concentration and pressure factors. To further assess whether the effect is significant or not, one calculates the variance of the effect from three additional runs, performed under conditions given in Table 3. A statistical analysis such as Student’s test14 shows that, within the range investigated, concentration and pressure are not significant factors. Therefore, the screening design allows for the identification of five key effects on yield and drying, which are, in order of decreasing importance, the liquid solution flow rate, the temperature, the washing time, the antisolvent flow rate, and the nozzle diameter. Similar analyses were performed for the particle size. The difficulty that arises with that response is the absence of numerical values from four trials. We should have assigned an arbitrary value of 0, but this would imply that these conditions produced the smallest particle size, which is untrue. In the main effect

Figure 4. Main effect plot of the factors on (a) yield and (b) particle size. Table 3. Experimental Conditions of the Replicate Runs factors

value

T (K) P (MPa) C (% w:w) FCO2 (cm3 min-1) Fliq (cm3 min-1) tw (min) dn (µm)

308.15 10 3.2 102.5 5.5 90 50

calculation, these trials were therefore discarded. As an example, the main effect of temperature on particle size is calculated as

1 1 effect ) (120) - (300 + 1000 + 70) ) -336 1 3 An effect graph is plotted in Figure 4b. From the relative slopes, it can be seen that the CO2 flow rate has the main influence on particle size, whereas the washing time only slightly influences the response. Concentration, temperature, pressure, liquid flow rate, and nozzle diameter act to a similar extent, but in different way. An increasing pressure and temperature give smaller size, whereas an increasing concentration tends to increase the size. The importance order of parameters for particle size is summarized as

FCO2 > C, T, P, Fliq, dn > tw whereas, for yield and dryness, it was

Fliq, T > tw, FCO2, dn > P, C Such scales help in selecting the appropriate parameters for act on a specific criterion, but compromises would

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required by the common “one factor at a time” route. To continue developing the understanding of this process, relationships between fundamentals and DOE conclusions are currently being investigated. Acknowledgment The authors thank Dr. J. P. Passarello for phase behavior calculations and P. Portes for SEM pictures. Nomenclature C ) weight fraction of dextran in the liquid solution, %w:w dn ) nozzle diameter, µm FCO2 ) flow rate of the antisolvent carbon dioxide, cm3.min-1 Fliq ) flow rate of the liquid solution, cm3.min-1 P ) pressure of the autoclave, MPa T ) temperature of the autoclave, K tw ) washing time, min Figure 5. SEM image of powder obtained in the variance study.

Literature Cited have to be found. For example, the SEM picture of powder obtained in the variance study is given in Figure 5. The experimental conditions were chosen according to the DOE analysis for dryness and yield. It is shown, however, than these appropriate conditions are not optimal for particle size, as the mean size lies within the 500-nm range. 5. Conclusion For a given solute/solvent/antisolvent system, seven potential factors likely to influence the process performance have been identified. Although we have eliminated nonlinear effects by assuming only two levels and neglected interactions between factors, we can make some preliminary conclusions from the experimental design investigated in this paper. As a screening, the DOE has shown that five key factors influence both the yield and the dryness of the product. In order of importance, they are the liquid solution flow rate, the temperature, the washing time, the antisolvent flow rate, and the nozzle diameter. Yield and drying were important responses to study in the case of the dextran polymer. Indeed, this compound interacted strongly with the solvent DMSO, which rendered its drying quite difficult to perform successfully. The response of the process to particle size was also analyzed. Caution, however, was taken in deriving deep conclusions, as the fact that some trials give no powder render the main effect calculation on particle size somewhat questionable. It was shown that the order of importance of the parameters was different than that for yield and dryness, and so were the trends. These results suggest, therefore, that a compromise in operating conditions should be found for simultaneously obtaining acceptable levels of yield, dryness and particle size. That optimization is currently under investigation. More generally, this paper proposes to approach the supercritical fluid antisolvent process in an original way and shows that design of experiments is a helpful tool in revealing the important effects in such processes. As screening designs, they allow for the identification of key variables at an early stage of experimentation, with only few experiments, compared to the numerous trials

(1) Subra, P.; Jestin, P. Powders elaboration in supercritical media: comparison with conventional routes. Powder Technol. 1999, 103, 2-9. (2) Reverchon, E. Supercritical antisolvent precipitation of micro- and nano-particles. J. Supercrit. Fluids 1999, 15, 1-21. (3) Reverchon, E. Supercritical antisolvent precipitation: its application to microparticle generation and products fractionation. In Proceedings of the 5th Meeting on Supercritical Fluids: Perrut, M., Subra, P., Eds.; INPL: Vandoeuvre, France, 1998; Vol. 1, p 221. (4) Dixon, D.; Johnston, K.; Bodmeier, R. Polymeric materials formed by precipitation with a compressed fluid antisolvent. AIChE J. 1993, 39, 127-139. (5) Luna-Barcenas, G.; Kanakia, S.; Sanchez, I.; Johnston, K. Semicrystalline microfibrils and hollow fibres by precipitation with a compressed-fluid antisolvent. Polymer 1995, 36, 3173-3182. (6) Kiran, E. Kinetics of pressure-induced phase separation (PIPS) in polymer solutions. In Proceedings of the 4th Meeting on Supercritical Fluids; Tohoku University Press: Sendai, Japan, 1997; Vol. C, pp 777-784. (7) Palakodaty, S.; York, P.; Hanna, M.; Pritchard, J. Crystallization of lactose using solution enhanced dispersion by supercritical fluids (SEDS) technique. In Proceedings of the 5th Meeting on Supercritical Fluids: Perrut, M., Subra, P., Eds.; INPL: Vandoeuvre, France, 1998; Vol. 1, p 275. (8) Devore, J.; Farnum, N. Applied statistics for engineers and scientists; Duxbury Press: Pacific Grove, CA, 1999; Chapter 10. (9) Kordikowski, A.; Schenk, A.; Van Nielen, R.; Peters, C. Volume expansion and vapor-liquid equilibria of binary mixtures of a variety of polar solvents and certain near-critical solvents. J. Supercrit. Fluids 1995, 8, 205-216. (10) Bodmeier, R.; Wang, H.; Dixon, D.; Mawson, S.; Johnston, K. Polymeric microspheres prepared by spraying into compressed carbon dioxide. Pharm. Res. 1995, 12 (8), 1211-1217. (11) Benedetti, L.; Bertucco, A.; Pallado, P. Production of micronic particles of biocompatible polymer using supercritical carbon dioxide. Biotechnol. Bioeng. 1997, 53, 232-237. (12) Bleich, J.; Muller, B.; Wasmus, W. Aerosol solvent extraction systemsa new microparticle production technique. Int. J. Pharm. 1993, 97, 111-117. (13) Bleich, J.; Kleinebudde, P.; Muller, B. Influence of gas density and pressure on microparticles produced with the ASES process. Int. J. Pharm. 1994, 106, 77-84. (14) Sado, G.; Sado, M.-C. Les plans d’experiences: de l’experimentation a` l′assurance qualite´ ; AFNOR: Paris la Defense, France, 1991.

Received for review December 30, 1999 Accepted August 24, 2000 IE990940W