Dry granulation: Use of the thin layer model for predicting densities and forces during roller compaction

Powder Technology 199 (2010) 165–175

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Roller compaction/Dry granulation: Use of the thin layer model for predicting densities and forces during roller compaction Stefanie Peter a,⁎, Robert F. Lammens b, Klaus-Jürgen Steffens a a b

Department of Pharmaceutical Technology, Rheinische-Friedrich-Wilhelms-University of Bonn, Germany Technical Services Consult Lammens, Heymannstr. 50, D-51373 Leverkusen, Germany

a r t i c l e

i n f o

Article history: Received 18 June 2009 Received in revised form 6 December 2009 Accepted 7 January 2010 Available online 25 January 2010 Keywords: Roller compaction Prediction model Force-displacement measurements Elastic recovery Compression speed Ribbon density

a b s t r a c t The thin layer model is based on the assumption that the deformation of powder during tableting can be transferred to the roller compaction process, provided that it was established with sufficient accuracy in the tableting experiments. In particular, the process of compaction between the rolls is presumed to consist of three parts, a rearrangement, an “exponential” and an elastic recovery phase. The rearrangement and “exponential” phases are used to calculate the densification of the material. The forces between the rolls during elastic recovery, the third phase, proved to be essential to the prediction, because 20% to 30% of the total roller compaction force is required to counteract ribbon recovery. Four different excipients and one powder blend were tested in the model. For two materials, the density and force predictions turned out to be accurate within ± 2.5% and ± 10%, respectively. For one excipient and the model blend, the predictions deviated systematically whereas those for the remaining excipient were within the above mentioned limits in ca. 50% of the experiments. For explaining these differences, we evaluated both the influence of the course of the force–time profile, at comparable densification times, and the influence of different compression times, for comparable force–time profiles. Finally, the impact of density distributions within ribbons on the prediction was estimated. © 2010 Elsevier B.V. All rights reserved.

1. Introduction The best-known method for dry granulation is roller compaction. Thereby, a powder (blend), usually with poor flowability properties, is drawn in between two counter-rotating rolls and densified to intermediate compacts called ribbons or flakes, which are reduced in size finally so to obtain granules. This granulation method is characterized by • • • • • • • •

being a continuous process, running at small to moderate densification speeds, requiring small GMP area, no technical area, a relatively large mass throughput, being applicable to compounds sensitive to heat or hydrolysis, its ability to handle poor flowable powder and requiring low amounts of energy since no evaporation of granulation liquids is necessary.

Thus, roller compaction may be considered the most economic granulation method [1]. ⁎ Corresponding author. Tel.: +49 228 736428; fax: +49 228 73 5268. E-mail addresses: [email protected] (S. Peter), [email protected] (R.F. Lammens). 0032-5910/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.powtec.2010.01.002

The main quality parameter in roller compaction is the relative density (porosity, solid fraction) of the ribbons [2], since it governs the important granule properties flowability and recompactibility. In general, the larger the ribbon density the smaller the amount of fines and therefore, the better the flowability of the resulting granules , but, the worse the recompactibility, which might be defined as the reduction in tablet strength compared to the one obtained in direct compression of the starting material. Almost all properties of tablets like uniformity of content and mass, tablet hardness and abrasion, disintegration, dissolution and bioavailability as well as the speed with which they can be made, are dependent on either flowability or recompactibility of the dry granulate, which in turn depends on ribbon density [3,4]. On the one hand, the density of ribbons can be influenced during the process of roller compaction by the size of the gap, defined as the minimal distance between the rollers, and on the other hand, by the specific roller compaction force having the larger influence [5]. This force is generated by a hydraulic system that prevents separation of the rollers, caused by the resistance of the powder against densification, and is expressed as the force per centimetre roll width. Several models for predicting gap, compaction force/pressure, ribbon density, peak pressure etc. are described in the literature, like the Johanson's rolling theory [6–8], the slab method [9] and recent approaches using neuronal networks [10,11], discrete element method [12] and finite element method [13,14]. Reviews have been

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published by Dec et al. [13] and Gururajan [8]. All models proved to be valid in some cases whereas larger deviations occurred for other ones, but in general, they necessitate large experimental effort and/or complex calculations requiring a high degree of expert knowledge. In this study, the assumption basically is that the plug of powder between the rollers consists of thin layers each with a given density (or density distribution) which depends solely on the ratio of the layer length, being the distance between the rollers, to the gap width. In principle, the pressure exerted on the rollers by each of these layers can be obtained from tableting experiments and depends on both the density and the way such a density is achieved, the deformation properties of this powder. The validity of this simple model will be investigated with respect to the prediction of ribbon densities as a function of roll compaction forces (and vice versa) for excipients with different deformation behaviour as well as for a model powder blend. In this context, a method is developed to determine the contribution of the elastic recovery of the ribbon to the total force whilst the ribbon leaves the gap between the rollers. The prediction model might be useful in facilitating formulation development by • reducing the number of necessary roller compaction experiments, saving time and material and • enabling a process optimization e.g. by gap adaptation to maximize production rate.

2. Theoretical aspects: the thin layer model The theory of the thin layer model was also published by Busies [15] for the most part and is outlined in the following in order to facilitate understanding the assumptions of this model. For obtaining a certain degree of volume reduction of powder with a given start density, the powder conveyed to the rollers must be drawn in at a certain height hi to the gap G (Fig. 1). This height, at which this densification process starts, is indicated by the so-called nip angle α [6]. The nip area, which is confined by the angle α, is assumed to consist of thin layers that contain a certain amount of powder which remains constant. Each layer has the same width, equal to the roll width a, and the same height Δh (set to 0.01 mm, according to [15]) which is assumed to remain constant during densification. Therefore, the layers differ only in the length bi, and since the powder mass per layer is constant, the mass per volume, the density ρbi of each layer, depends only on the layer length bi.

The rolling angle φi relates to the height hi and can be calculated from Eq. (1). φi = arcsin

hi ðD: roll diameterÞ: 0:5
D

ð1Þ

For a given “at gap” density ρG and a gap G, the relationship between the rolling angle φi of each layer and its density ρbi is given in Eq. (2). The ratio of the length of each layer bi to the gap G is equal to the inverse of the ratio of the layer density ρbi to the “at gap” density ρG, defined as the densification factor Fd. Fd =

ρG b G + Dð1− cosφi Þ = : = G G ρbi

ð2Þ

The thin layer model requires the establishment of the relationship between the density ρbi of each layer and the corresponding pressure pi. This relationship will be determined in tableting experiments. An example of such a relationship, with limited validity, is given in Eq. (3) describing the density of each layer within the nip area and the resulting pressure on the roller surface: m
ρi

pi = k
e

ðk;m: constantsÞ

ð3Þ

Once the pressure pi is known, the force Fi applied to the rollers by each of these layers can be calculated according to Eq. (4). All forces Fi are summed and divided by the roll width a:  FDP

 kN 1 i=n 1 i=n =
∑ Fi =
∑ ðΔh
a
pi Þ: cm a i=0 a i=0

ð4Þ

In this way, the total force FDP (DP = densification phase) applied to the rollers due to the densification of the powder can be estimated. However after densification, the ribbon is recovering elastically and during this recovery phase a force is applied to the rollers until the contact with the roller surface is lost. Also for this recovery phase, which has not been considered by Busies [15], a relationship has to be found between the density of each layer and the corresponding force, which is exerted on the rollers during this recovery process. Finally, the elastic recovery force FER (Eq. (5)) has to be added to the force FDP, resulting in the total specific compaction force FTOT. FTOT = FDP + FER

ð5Þ

3. Materials and methods 3.1. Materials Excipients, being investigated single and used as received, were • C*PharmGel DC 93000 (Cerestar France, Cargill Excipients, France), a pregelatinized starch, • Elcema® Type G 250 (JRS Pharma, Rosenberg, Germany), a powdered cellulose, • Vivapur® Type 101 (JRS Pharma, Rosenberg, Germany), a microcrystalline cellulose, and • Emcompress® Premium (JRS Pharma; Rosenberg, Germany), a dibasic dicalciumphosphate dihydrate, which had to be blended with 0.5% of the lubricant magnesium stearate (Pharma veg, Baerlocher, Germany). The model powder blend contained

Fig. 1. The thin layer model of powder densification.

• 50% microcrystalline cellulose (Vivapur Type 101), • 25% α-lactose monohydrate (Granulac® 200, Meggle Pharma, Wasserburg, Germany),

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• 12.5% crosslinked polyvinylpyrrolidone (Kollidon CL, BASF, Ludwigshafen, Germany) and • 12.5% polyvinylacetate polyvinylpyrrolidone copolymer (Kollidon*VA 64, BASF, Ludwigshafen, Germany). 3.2. Methods 3.2.1. Preparation of mixtures All mixtures were prepared with a “Rhoenrad” mixer equipped with a 20 l mixing bowl at 20 rpm rotational speed. The filling degree of the bowl did not exceed 60% and a mixing time of 10 min. for the model powder blend and of 2 min. for the addition of the lubricant was chosen. 3.2.2. Tableting 3.2.2.1. FlexiTab®. Tableting experiments were performed on the laboratory single-punch press FlexiTab® (Röltgen, Solingen, Germany), unless stated otherwise. Its pneumo-hydraulic compression force system is fully automated and operated by a programmable controller. As tooling, flat-faced punches with 18-mm diameter were used and the tablet height was adjusted to 2 to 3 mm whilst metal surfaces were lubricated externally. This procedure was chosen because frictional forces may have a significant influence on the results of tableting, whereas they are considered to be negligible for roller compaction. Therefore, only the forces acting on the lower punch were included in the data evaluation. Since the FlexiTab® enables the setting of the desired compression force independent of the powder mass in the die, the powder was filled in by hand and after ejection, tablets were weighed on an analytical balance. All data were recorded by the data acquisition system DAQ4 (Hucke Software, Solingen, Germany) at a sample rate of 10,000 data points per second. The FlexiTab® is equipped with a calibrated upper and lower punch force measurement system. 3.2.2.2. Stylcam 200 R. Furthermore, some experiments (see Section 4.3) were performed on the compression simulator Stylcam 200 R (Medelpharm, France) designed for simulating the compressional behaviour of different rotary tablet presses. For this study, tablets were made with main compression only in the so-called “direct cam” mode. 10-mm diameter flat-faced punches were mounted, the powder was filled in by hand after lubrication of the die and tablets were weighed after ejection. The compression load was calculated as the arithmetic mean of upper and lower punch pressure since both punches are involved in the compression. The same data acquisition software as for the FlexiTab® was used at a sample rate of 4,000 data points per second. 3.2.2.3. Compression profiles. Different compression pressure vs. time profiles were used. At the FlexiTab® the so-called profiles “Default” and “Smooth 300” and at the Stylcam “Stylcam 3 rpm” and “Stylcam 20 rpm” were used (see Fig. 2). The Flexitab® profiles were generated whilst the Stylcam profiles differ only in compaction speed as indicated. The data, necessary to describe the densification phase of a material, were acquired with the profile “Default”. To gain input data for the elastic recovery phase of a ribbon, a method was developed using the profile “Default”, too. However, the descending pressure line of “Default” contains a second pressure maximum. Therefore, the profile “Smooth 300” was used to establish the equations describing the elastic recovery behaviour. The irregular course of “Default” is caused by switching between pneumatic and hydraulic load system as well as by stepwise changes in the aperture of the hydraulic control valves, which enable different loading rates. Depending on the setting of the rotational speed, the data produced with the Stylcam tablet press referred to as “Stylcam 20 rpm” or “Stylcam 3 rpm” (see Fig. 2). For some of the figures the

Fig. 2. Different compression time vs. pressure profiles.

displayed data points had to be reduced to enhance the distinguishability between various data sets. 3.2.3. Displacement measurement system Both tablet presses were equipped with a displacement measurement system having similar properties regarding mount, transducer characterization and calibration as follows. 3.2.3.1. Measurement system. Two inductive displacement transducers (Schreiber SM Types 200, 210 or 260, Germany) are mounted with a clamp directly to the upper and the lower punch [16], respectively. The transducers were characterized by a statical method using a micrometer screw (Type 164–151, Mitutoyo, Germany). The relationship obtained between displacement and voltage output of the transducer was fitted by polynomial regression analysis with a stepwise increase of the polynomial degree until a random distribution of residuals was achieved [17]. All data were corrected for the elastic deformation of the punches which was established both by calculation, based on Hooke´s law, and from displacement measurements during punch-to-punch experiments. Both results turned out to be identical. Mounting two transducers per punch increases the accuracy of the method on the one hand, and enables the detection and correction of punch tilting on the other hand. The precision and accuracy of FlexiTab® and Stylcam displacement measurement systems is better than ±10 μm. 3.2.3.2. Calibration. The calibration of the punch positions was performed before and after each set of measurements. For this purpose, a cylindrical gauge, with an exactly known height, was placed in the clean and empty die and compressed at a low speed to a maximum compression force of approximately 200 N, ensuring full contact according to literature [18]. The thus established reference position of each transducer was calculated as the mean voltage of ten compressions and served to convert values of the displacement transducers into “in die” heights with respect to the gauge height.

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3.2.3.3. Pressure–density curves. Punch displacement measurements offer the opportunity to collect so-called “in die” or “at pressure” data, not only for determining the minimum tablet height during one compression cycle but also for recording the progression of a single compression continuously and to plot these data as force–displacement curves. For this study, these curves are standardized by plotting the compaction pressure against the apparent “at pressure” density, in the following referred to as pressure–density curves. 3.2.4. Roller compaction All roller compaction experiments were performed on a production scale compactor (Macro-Pactor® 250/100/3, Gerteis Maschinen + Processengineering AG, Jona, Switzerland) equipped with smooth rollers with 250 mm diameter and 100 mm width and the standard feed and tamping screws. For sealing the compaction area between the rollers, a rim-roll assembly was mounted. The Macro-Pactor® is equipped with a calibrated force, gap and rotational speed measurement system. In general, precision and accuracy of force is 1 to 2% approximately, of gap over the possible range ± 0.02 mm and of the rotational speed better than 0.5% (for more details refer to the qualification documentation of the Macro-Pactor®). All experiments were conducted in the automatic mode in which the gap, the force and the rotational roll speed are controlled by a PLC (programmable logic control). Gap control is achieved by adjusting the speed of the augers of the powder feed in case a deviation between set and actual gap value exists [1,19]. An overview of experimental settings for the different materials is shown in Table 1. 3.2.5. Determination of ribbon density: throughput method Upon roller compaction, the released ribbons were collected during a defined period of time. After weighing the collected ribbons, the density was calculated from the mass mr and the ribbon volume Vr over time t, the so-called throughput ρG as shown in Eq. (6). ρG =

mr
t : t
Vr k

ð6Þ

The ribbon volume Vr was calculated according to Eq. (7) in which the roll circumference, corrected for half the gap G [20], is multiplied by the rotational speed Sr and the gap surface, the product of gap G and roll width a. Vr =

   G
π
ðSr Þ
ðG
aÞ: D+ 2

ð7Þ

For every setting, a triple determination was performed; mean, standard deviation (SD) and relative standard deviation (RSD) were calculated. This measurement does not include the correction for the

elastic recovery of the ribbons. Therefore, it results in a so-called “at gap” density ρG. 3.2.6. Density distributions of ribbons For determining density distributions within ribbons, data reported by Busies [15] were used. In his thesis, the hardness at different positions across the ribbon width was measured with a so-called tablet mill [15,32]. For the investigated powder blend, which we also used in this study, he also established the relationship between the hardness and the apparent density of the compacts. From this hardness–density relationship, we converted a ribbon hardness profile (measured by Busies) in a density distribution. Table 2 represents positions on the ribbon across the width and the corresponding deviations of density from the average density, which is the one measured by the throughput method. This density distribution will be used for estimating its influence on the prediction of roller compaction forces and densities according to the thin layer model. 4. Results and discussion 4.1. Determining pressure–density data and modelling 4.1.1. Pressure–density relationship For the prediction of roller compaction forces, the relationship between density and pressure is required. As obvious from Fig. 3a, the pressure at a certain density cannot be obtained from “out of die” measurements, which would cause considerably less effort. For an apparent density of 1.1 g/cm³, corresponding to a solid fraction (SF) of 0.73, the “in die” pressure (40 MPa) is 1.8 times larger than the “out of die” pressure (72 MPa), clearly showing the necessity of determining the pressure–density relationship “in die”. Typical examples of these “in die” relationships are shown in Fig. 3b. These data were obtained by compressing a number of tablets (n) at different pressures and evaluating the maximum “in die” densities of these tablets related to the maximum (lower punch) pressures. Within a series of measurements, the data points can be described by an exponential relationship with a correlation coefficient (CC) of 0.997 indicating the excellent reproducibility of the “in die” measurements. Even a second series of experiments conducted at a different time and after new mounting and calibration of the displacement measurement system could be described by the same exponential relationship which of course is specific for a given substance. As obvious from Fig. 3b, the data points of the individual series coincide completely. In Fig. 3c, continuously recorded pressure–density curves at different maximum loads are represented in comparison to the exponential equation, obtained from the data shown in Fig. 3a for CPharmGel. The ascending pressure line does not coincide with the exponential equation based on the maximum values obtained from

Table 1 Overview of roller compaction settings; often used gap and roll speed settings are shown in underlined letters; auger ratio is defined as the ratio of the rotational speed of the tamping to the feed auger in percent. Setting/Material

CPharmGel

Elcema

Vivapur

Emcompress

Powder blend

Specific compaction force [kN/cm] Roll gap [mm] Roll speed [rpm] Auger ratio [%] Gap control

5, 6, 8, 10 and 13 2, 2.5, 3 and 4 1, 2, 3 and 5 180–250 on

3, 4, 5.5, 7, 9, 12 and 15 3, 3.5 and 4 2 and 3 175–250 on

1.5, 3, 5 and 10 3 and 4 3 150–200 on

6, 9 and 12 3 2 and 3 200 on

1.5, 3, 4.5, 6, 7.5, 9, 11, 12, 15 2.5, 3 and 4 1, 2 and 3 180–250 on

Table 2 Density distribution across the ribbon width when using rim-roll sealing (percental deviation of the local density to the mean density). Position [cm] Deviation [%]

0.5 13.06

1.5 6.00

2.5 −1.06

3.5 −4.59

4.5 −7.24

5.5 −6.35

6.5 −4.59

7.5 −2.82

8.5 0.71

9.5 6.88

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Fig. 3. (a) Comparison of “in die” and “out of die” densities vs. maximum lower punch pressures of n compressions for CPharmGel (n = 45 “in die”, n = 27 “out of die”). (b) Maximum “in die” densities vs. maximum lower punch pressures of n compressions (n = 84 for Elcema; n = 89 for Emcompress). (c) CPharmGel pressure–density curves at different maximum loads in comparison to the exponential equation obtained for “in die” densities vs. pressures data in (a). (d) Three coincident CPharmGel pressure–density curves at the same maximum load.

numerous individual compressions. This is due to relaxation phenomena [21]. The positions of the maximum of pressure and density are indicated by arrows in Fig. 3c. They do not occur at the same point in time; the density maximum is delayed to the one of the pressure maximum. Viscoelasticity is a time-dependent phenomenon, resulting in a decrease in the pressure at a constant volume due to a decreasing resistance of the material against the applied volume reduction. During tableting, at the moment the maximum punch displacement is nearly reached, the punch velocity is approaching zero and hence, relaxation within the powder bed leads effectively to a decrease in force although the maximum punch displacements are not yet achieved. From the above, it may be concluded that pressure–density data should be obtained from measurements during a continuous densification process. Pressure–density curves obtained at various final densities coincide during the main densification part completely (Fig. 3c) and only differ in the final stage of densification because of relaxation. In spite of these differences, it is decided to determine our data from measurements at a large degree of densification (=large

pressure) including all relevant powder compaction densities (three different measurement results coincide within 2 MPa; refer to Fig. 3d). In principle, this will introduce an error to our roller compaction force predictions, since relaxation phenomena at the final stage of densification will not be taken into account.

4.1.2. Modelling of the densification phase The volume reduction and the corresponding pressure of a material can be described by an exponential relationship for a limited part of the pressure–density curve, further called the “exponential” phase. Fig. 4a shows a pressure–density curve with its corresponding exponential regression curve. If the exponential equation is valid, plotting density vs. the natural logarithm of pressure results in a linear relationship. The linear part of the curve in Fig. 4b corresponds to the socalled second part of the Heckel plot [22–24] which can be used to establish the yield pressure as a parameter for plastic properties. In Fig. 4b, only data with densities larger than 1.0 g/cm³ can be described by an exponential relationship.

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occur but for simplification the term “rearrangement” is further used to describe this initial part of the densification process. So, the ascending pressure line is subdivided into the rearrangement and the “exponential” phase, each requiring its separate fit as shown in Fig. 4c. The rearrangement phase will be fitted only to polynomial equations whilst the “exponential” phase is fitted either to exponential or polynomial equations. As the criterion of fit, the CC had to exceed a value of 0.999. Below a certain density, the change value in Fig. 4c,

Fig. 4. (a) Pressure–density curve of CPharmGel with a fitted exponential equation. (b) Semi-logarithmic plot of the curve and fit equation shown in (a); used to identify the exponential phase as linear part of this curve. (c) Ascending pressure line of (a), subdivided in two parts with different fit functions.

Considering Fig. 4a, it is obvious that the exponential equation is not suited to model the whole process of a ribbon formation because of its large deviations at small pressures. The first step of tablet formation is known as the rearrangement phase. This particle rearrangement and slippage occurs up to a certain density after which further particle movements become difficult or even impossible [24]. At the same time, also elastic deformation and fragmentation may

Fig. 5. (a) Double compression pressure–density curves for Elcema. (b) Ascending pressure lines of second compressions at different maximum loads for Elcema. (c) Standardization of (b) as percentage of values at the maximum density of the 1st compression cycle; shows coincident lines for different maximum loads.

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the rearrangement equation will be used to perform our model calculations. Additionally, the start value is defined as the density at which the pressure exceeds 0.2 MPa during tableting. 4.1.3. Modelling of the recovery phase The recovery phase is investigated by compressing each die filling twice [25]. Thereby, the tablet is not ejected and, after a delay time of approximately 3 seconds, compressed a second time. In Fig. 5a, corresponding pressure–density curves of Elcema G 250 are shown. The ascending pressure line of the second compression is used to establish an equation for the recovery of a material. The descending pressure line of the first compression is shifted to larger densities, because the speed of the (elastic) recovery of the material is slower as the speed of the withdrawing punch. As obvious, after the upper punch has lost contact with the tablet surface, the fast part of this (elastic) recovery is completed. The recovery of a material is considered to consist of two steps, the fast recovery occurs immediately and has a large effect on the density within a rather short time range whilst the slow recovery results in slight changes and is considered to be completed at earliest 24 h after compact formation [26]. The deformation of the largely recovered tablet during the second compression can be considered as mainly elastic as long as the maximum density of the first compression is not yet reached. Since the lower punch pressure is used, no frictional forces are included [27]. Unlike for the densification phase, the pressure–density curve for the elastic recovery cannot be represented by a single equation, because this relationship strongly depends on the applied maximum loads (Fig. 5b). Therefore, these measurement data were standardized in the following way: the “at pressure” density is plotted as a percentage of the maximum density achieved during the first compression. The pressure is standardized not as a percentage of the maximum pressure of the first compression but as a percentage of the pressure corresponding to the maximum density (=minimum tablet height), since these two values are clearly different. From Fig. 5c, it can be concluded that this standardization procedure results in coinciding recovery curves for a given product which enables the fit of a single recovery equation, valid for all applied maximum loads. This procedure results in pressures larger than 100% which cannot be accounted for physically. 4.1.4. Summarization of model input As stated in the section above, the data necessary for the prediction model can only be acquired by continuously recording the “at pres-

171

sure” density as a function of the compaction pressure. In this way, the following input data can be obtained: • Start density: the density of the first layer in the model corresponding to a pressure exceeding 0.2 MPa • Rearrangement phase: polynomial equation describing the rearrangement part of the ascending pressure line of the pressure– density curve (fit requirement CC > 0,999) • “Exponential” phase: polynomial or exponential fit to the “exponential” part of the ascending pressure line of the pressure–density curves (fit requirement CC > 0,999) • Elastic recovery (according to the double compression method): polynomial fit of the ascending pressure line of the second compression after standardization as percentage of values at the maximum density of the first compression. In Fig. 6a, a comparison of the ascending lines of pressure–density curves is shown used to establish the rearrangement and exponential phase of all materials in this study. For the purpose of comparison, the “at pressure” density was converted into SF. CPharmGel has the largest compressibility, as defined by Leuenberger et al. [28], since the volume decreases fast with an increase of pressure although this is only valid for SF larger than 0.75. At low pressures, Emcompress is the better compressible substance probably due to the breakage of soft granules, but at large pressures, compressibility is poor because a small change in SF requires a large change in pressure. Vivapur, Elcema and the powder blend have similarly shaped curves of pressure vs. SF. Only at low pressures up to 50 MPa differences are visible. All established fit equations are available upon request. The differences in the elastic behaviour of the materials used are shown in Fig. 6b, representing the standardized ascending pressure lines of the second compression. CPharmGel has the largest fast elastic recovery as obvious from the intercept with the abscissa; a SF of 89% corresponds to 11% fast elastic recovery. The fast elastic recovery of Elcema amounts to 9%, of the blend to 7%, of Vivapur to 6% and of Emcompress to approximately 3% in density. As shown in Fig. 6b, elastic recovery pressures larger than 100% are possible. Upon calculating according to the thin layer model, this would cause a jump in the pressure when changing from the densification to the elastic recovery process. At the same “at gap” density, the pressure from which on the elastic recovery starts would be larger than the one at which the densification process stops. In this case, the first part of the recovery is calculated using the equations for the densification process which implies purely elastic deformation at these densities. This is done as long as the pressure according to the elastic recovery

Fig. 6. (a) Comparison of input data for the rearrangement and exponential phase for all used materials; the “at pressure” density was converted to solid fractions. (b) Comparison of standardized ascending pressure lines of 2nd compressions for all used materials; input data for the elastic recovery phase.

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Fig. 7. (a) Prediction plot: established “at gap” density (by throughput measurement) vs. predicted density (calculated for the gap and force setting of each experiment), data points with SD. (b) Measured solid fraction vs. percental deviation of prediction to measurement of the specific compaction force.

equation is larger than the one from the densification equations. After that, the elastic recovery equation is used. 4.2. Output of the prediction model 4.2.1. Comparison between calculation and measurement The “at gap” ribbon density was determined for different roller compaction settings (see overview in Table1) by the throughput method (see Section 3.2.5). In general, the RSD of the measurements were smaller than 0.8%. These “at gap” ribbon densities are compared to the calculated densities resulting in the prediction plot (Fig. 7a). An exact correlation between predicted and measured densities coincides with the bold line. Deviations of ±10% are represented by the dashed lines. As obvious, predictions deviate less than 10%. The mean deviation between predicted and measured densities is for CPharmGel −0.3%, for Vivapur 1.2%, for Elcema 2.3%, for the powder blend 4.8% and for Emcompress 5.5%. The results of force predictions are summarized in Fig. 7b. The throughput densities, after conversion into SF, are plotted against the deviations of the predicted forces. As well known from tableting, a small change in density in general causes a large change in force in a ratio ranging from 1:5 to 1:10. Therefore, the analysis of force deviations is more sensitive with respect to the validity of the model and deviations less than ±10% can be used for practical applications without any doubt. A positive deviation corresponds to predicted forces larger than the applied ones. From the data in Fig. 7b can be concluded that there is no systematic influence of the SF on the prediction of forces. The best predictions are obtained for CPharmGel showing a random scatter around zero within the 10% deviation interval, except for one data point. Also for Vivapur, all predictions are within ±10% but the predictions tend to be smaller than the measured values. This also holds for Elcema showing deviations up to −22%. However, ca. 50% of the conducted experiments result in deviations smaller than 10%. For the powder blend, the prediction clearly deviates systematically with an average error of −20%. For the inorganic material Emcompress, having the largest deviations, the quality of the prediction is moderate. Obviously, this quality seems to depend on the densification properties of the material, but there is no relation with the degree of densification. A systematic underestimation of compaction force is present for the brittle deforming material and the blend which contains 25% of the partially brittle deforming lactose. Sufficient compliance is obtained for the (mainly) plastic deforming materials.

One possible explanation for these differences might be the presence of density distributions within ribbons. It is well known that the ribbon density varies across the width. For the rim-roll assembly, larger densities are found at the edges [15]. Since the relationship between density and pressure is not linear but exponential, including density distributions into the model might result in larger total compaction forces FTOT. Moreover, although the pressure–density relationships are obtained from tableting experiments, performed at overall densification times comparable to roller compaction, the profiles of volume reduction vs. time are not exactly identical. And additionally, the FlexiTab® force–time profiles are suffering from a couple of irregularities with an unknown relevance for the prediction quality. 4.2.2. Calculated roll pressure profiles In Fig. 8, examples of calculated roll pressure profiles are shown for all materials. In this plot, negative angles correspond to layers above the gap, the angle 0° relates to the gap and at positive angles, pressures are exerted by the recovering ribbon. The data required for this plot, are directly obtained from the model since each point represents the pressure acting on one layer at a given angle. Although these profiles can be measured with a roll instrumented with a pressure

Fig. 8. Examples of calculated rolling angle vs. roll pressure profiles for an assumed “at gap” SF of 0.75 (number of shown data points is reduced).

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transducer embedded in the roll surface, as obvious from many examples in the literature [7,10,14], a quantitative validation of our prediction model requires the roll pressure measurement across the width and over the length of the roll surface. This, because of corresponding density and thus pressure distributions across the ribbons. In Fig. 8, for all materials the modelling was carried out for the same “at gap” SF of 0.75 (for 3 mm gap width). Accidentally, at this degree of densification the maximum pressure of all materials appear to be the same (refer to Fig. 6a). The specific compaction forces range from 4.36 kN/cm for Emcompress to 6.76 kN/cm for Vivapur. These differences can also be deduced from Fig. 8 since at the same maximum pressure the area under the curve for Vivapur is larger than for Emcompress. The percentage of the elastic recovery force FER (calculated as described in Section 4.1.3) to the total force FTOT varies from 23.6% for the brittle Emcompress to 30.4% for CPharmGel. The magnitude of this percentage demonstrates how important the implementation of the elastic recovery force FER to the model is. Several authors report that the maximum pressure is achieved before the maximum density (corresponding to the gap). This completely corresponds to tableting experience where the maximum pressure does not coincide with the maximum density as discussed in Section 4.1.1. In tableting, this is due to a low speed of densification close to the minimum distance between the punches. This also applies to the roller compaction process. In our calculations, relaxation phenomena of this kind are not included because predictions are based on a single pressure–density curve (Fig. 4c).The influence of force relaxation is not investigated further since this would require measurement data from a roller compaction simulator. Furthermore, it is questionable whether this would improve the precision of prediction considerably regarding the very fact that the quality for plastically deforming materials seems to be sufficient. 4.3. Influence of the compression profile The prediction in this study is based on a pressure–density relationship determined at a given tablet press and speed. Both have an influence on this relation [29]. Moreover considering Fig. 3b, our “Default” profile is suffering from irregularities. Therefore, different compression profiles were investigated. The densification time of the “Default” profile is comparable to the majority of the conducted roller compaction experiments with estimated densification times from 400 to 550 ms at 3 rpm roll speed. The “Stylcam 20 rpm” profile with 40 ms densification time corresponds to a roller compaction process at 30 rpm roll speed with an estimated 40 to 55 ms densification time. The “Smooth 300” and “Default” profile differ with respect to the shape of the profile and the irregularities, but hardly concerning the densification time (Fig. 2). The “Stylcam 3 rpm” profile resembles the “Smooth 300” profile largely, but Stylcam pressure data include frictional forces (symmetrical two-sided compaction). The densification behaviour of Emcompress is hardly dependent on speed. Although “Stylcam 20 rpm” is almost 7 times faster than “Stylcam 3 rpm”, it only causes a negligible shift in the pressure–density curve (Fig. 9a). No difference between the “Default” and “Smooth 300” profile is found in spite of differences with respect to irregularities and profile. So the difference between the data obtained with both tablet presses cannot be accounted for by speed but only by the frictional forces contained in the Stylcam pressure data. In Fig. 9, the pressure–density curves are used for the corresponding predictions plots. For Emcompress (Fig. 9b), no difference exists between “Default” and “Smooth 300” whereas use of the “Stylcam 20 rpm” profile causes a slight improvement with respect to prediction correctness. The pressure–density curves of CPharmGel in Fig. 9c show a clear influence of speed (“Stylcam 3 rpm” vs. “Stylcam 20 rpm”) as well as a slight influence of irregularities. The prediction based on the fast compression results in smaller predicted densities (Fig. 9d). At 3 rpm

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roll speed and an assumed nip angles of 7.5° to 10°, the densification time upon roller compaction is estimated to range from 400 to 550 ms. Since “Default” and “Smooth 300” almost meet this magnitude, it is reasonable to expect that these profiles result in the better prediction. The pressure–density curves of Vivapur reveal surprising findings (Fig. 9e). Even though “Default” is the slowest compression profile, and an influence of type of tablet press was shown before, it coincides with the 10 times faster “Stylcam 20 rpm” profile (Fig. 2). As for Emcompress and CPharmGel, the ranking of the pressure–density curves correlates with the ranking of the predicted densities in Fig. 9f. Both, the “Stylcam 20 rpm” and “Default” result in almost identical predictions whilst the use of “Smooth 300” as input profile causes too large predicted densities. Similar results were obtained for Elcema and the powder blend. The fact that “Default” appears considerably ‘faster’ may be explained with the irregularities in the profile. Every change in compression speed comes along with a hold or decrease of force (Fig. 2) and, depending on the material deformation properties, this may cause force relaxation and partially elastic recovery. Upon a further increase of force, elastic recovery energy has to be overcome again causing an increase of the slope of the pressure–density curve. In summary, when using “Smooth 300” the systematic deviation of the predicted density is 6.3% in average for all materials except for CPharmGel. The irregularities of the “Default” profile have a clear material-dependent influence on the pressure–density relationship and the correctness of the prediction. For investigating the influence of densification speed, the “Smooth 300” profile must therefore be compared with the “Stylcam 20 rpm” profile. Based on these speed differences, an increase from a low to high roll speed value causes density changes of up to 5% if the density is predicted according to the thin layer model. The Stylcam pressure data contain frictional forces, which are estimated to be responsible for about 50% of the differences of these pressure–density curves and so the effect of a change in roll speed is reduced to 2.5%. The pressure–density relationship proved to be strongly dependent on the type of tablet press, the compression speed as well as on the course of the force–time profile. By determining this relationship with a roller compaction profile on a compaction simulator [2], the correctness of the predictions could be investigated more clearly. Additionally, the comparison of calculated and measured pressure profiles, obtained from an instrumented roller, might be helpful to explain for the systematic deviation of the predicted forces and densities. With such an instrumented roller, the pressure increase and decrease within the gap area can be analyzed efficiently including the establishment of characteristic parameters like nip angle, pressure maximum and the ratio of FDP to FER. For the thin layer model, the densification process upon roller compaction is assumed to be continuously and one-dimensionally. If the powder avoids volume reduction during draw in by an upward powder flow within the gap area, the roll pressure profiles will likely deviate from the predicted ones with a corresponding impact on the calculated forces. The impact of such deviations is obvious from our tableting results concerning the influence of disturbances (irregularities) in the “Default” densification process showing an increase of pressure required for obtaining a given density. Bicane [5] observed this kind of upward powder movement upon roller compaction of coloured powder layers on a Micro-Pactor®.Since the prediction model was tested for numerous roller compaction settings, it can be concluded that the observed deviations of predicted forces and densities are clearly systematic and therefore, can be corrected for once established experimentally. 4.4. Influence of density distributions within ribbons For the model powder blend, all prediction calculations are repeated whilst taking into account density distributions within

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Fig. 9. Pressure–density curves of (a) Emcompress, (c) CPharmGel and (e) Vivapur, obtained with different compression time vs. time profiles in Fig. 2; corresponding prediction plots of “at gap” density vs. predicted density for (b) Emcompress, (d) CPharmGel and (f) Vivapur whilst different compression time vs. pressure profiles were used to establish input data.

ribbons. To this end, a ribbon is subdivided into ten strips of 1 cm width, each having its own density (Table 2). The calculated forces necessary for the compaction of each strip are averaged to the total compaction force. In Fig. 10, the deviation of the predicted to the actual specific compaction force is plotted against the SF of the produced ribbons, once with and once without consideration of density distributions. The implementation of density distributions causes a slight improvement of the prediction in the order of magnitude of 3%. Consequently, for the powder blend the systematic deviation of the predictions can hardly be explained by correcting for density distributions.The influence of density distributions for the rim-roll design is low, but might be more important for the modelling if a cheek-plate sealing is used [31]. The density of cheek-plate ribbons is large in the middle and approaches tap density at the edges whereby no areas of constant density are found [15]. In the literature, studies are found which report two to three times larger pressures in the middle than at the edges clearly indicating the importance of density distributions in cheek plate (side plate) ribbons [14,30].

the pressure–density curve. The reproducibility of the method enables the use of only three compressions each at a rather high load for establishing the fit functions for the rearrangement and “exponential” phase of powder densification. Furthermore, the correction for the forces during the elastic recovery of the ribbon is determined from

5. Conclusion In this study, the practical and theoretical aspects of the thin layer model for predicting the specific compaction force for a given density and vice versa have been linked to each other successfully. The precision and accuracy of the force displacement measurement system is sufficient for continuously recording a compression cycle,

Fig. 10. Measured solid fraction of ribbons vs. percental deviation of prediction to measurement of the specific compaction force—with and without consideration of density distributions across the ribbon width (Table 2)—for the model powder blend.

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tableting experiments with double compression of one die filling. By plotting the measurement data as a percentage of the values corresponding to the maximum density of the first compression, the ascending line of the second compression can be standardized successfully. In this way, one fit function describes the recovery for different degrees of densification and load, respectively. In conclusion, the data collection features the advantage of requiring a small amount of powder at moderate time consumption. When using the “Default” profile, the deviations of prediction from measurement turned out to be small for the majority of the tested materials. All predictions of ribbon density for a chosen gap and force setting proved to be accurate with errors smaller than 6%. For CPharmGel, Vivapur and ca. 50% of the experiments with Elcema, the deviation is smaller than 2.5%. For the powder blend and Emcompress, the magnitude of the systematic deviation cannot be neglected. The force predictions deviate more from the experimental setting, since density deviations of 1% correspond to force deviations of 4% to 7%, depending on the material properties. The contribution of the elastic recovery force FER to the total specific compaction force FTOT was determined to be in average 25%. Therefore, a proper correction for the elastic recovery forces is indispensable to the prediction model. The exact shape of compression profile is also found to be essential since the “Default” profile results in smaller deviations compared to the “Smooth 300” profile. At almost identical densification times, the latter lacks irregularities in the force–time course. Therefore, the systematic deviation might be larger as described above. Except for CPharmGel, in average a density deviation of +6.3% is found. For a closer investigation, the use of a compaction simulator for the correct establishment of the pressure–density relationship is proposed. Although it is desirable to improve the prediction accuracy, this model can be used in practice and will give acceptable results if the magnitude of the error is established from, in principle, one measurement (constant percental error). This will reduce the amount of material in early experiments compared to the usual trial-and-error technique, because the presented model enables the reduction of necessary roller compaction experiments as well as a reasonable choice of settings. Besides the evaluation of recompactibility properties of a new material based on tableting experiments, the prediction model presented in this study is a further step to a micro-scale development of roller compaction formulations. Acknowledgements The authors wish to thank the Gerteis company for their willingness to support scientific progress by providing the roller compactor and their positive attitude against innovations. Furthermore, we like to thank Röltgen and Medelpharm for their cooperation with our department as well as the companies Rettenmayer JRS Pharma, Meggle Pharma and BASF for the generous provision of the excipients used in this study. References [1] G. Shlieout, R.F. Lammens, P. Kleinebudde, Dry granulation with a roller compactor— part I: the functional units and operation modes, Pharm. Tech. Europe 12 (2000) 24–35. [2] A.V. Zinchuk, M.P. Mullarney, B.C. Hancock, Simulation of roller compaction using a laboratory scale compaction simulator, Int. J. Pharm. 269 (2004) 403–415. [3] F. Freitag, P. Kleinebudde, How do roll compaction/dry granulation affect the tableting behaviour of inorganic materials? Comparison of four magnesium carbonates, Eur. J. Pharm. Sci. 19 (2003) 281–289.

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[4] F. Freitag, P. Kleinebudde, How do roll compaction/dry granulation affect the tableting behaviour of inorganic materials? Microhardness of ribbons and mercury porosimetry measurements of tablets, Eur. J. Pharm. Sci. 22 (2004) 325–333. [5] F. Bicane, Trockengranulation mit Hilfe des Micropactor, PhD thesis, University of Bonn, Germany (2003). [6] J.R. Johanson, A rolling theory for granular solids, J. Appl. Mech., Trans. ASME 32 (1965) 842–848. [7] G. Bindhumadhavan, J.P.K. Seville, M.J. Adams, R.W. Greenwood, S. Fitzpatrick, Roll compaction of a pharmaceutical excipient: experimental validation of rolling theory for granular solids, Chem. Eng. Sci. 60 (2005) 3891–3897. [8] Bindhumadhavan Gururajan, Roll Compaction of pharmaceutical powders, PhD thesis, University of Birmingham, United Kingdom (2004). [9] V.P. Katashinskii, Analytical determination of specific pressure during the rolling of metal powders (in Russian), Sov. Powder Metal Ceram. 10 (6) (1986) 765–772. [10] R.F. Mansa, R.H. Bridson, R.W. Greenwood, H. Barker, J.P.K. Seville, Using intelligent software to predict the effects of formulation and processing parameters on roller compaction, Powder Technol. 181 (2) (2008) 217–225. [11] S. Inghelbrecht, J.-P. Remon, et al., Instrumentation of a roll compactor and evaluation of the parameter settings by neural networks, Int. J. Pharm. 148 (1997) 103–115. [12] K. Odagi, T. Tanaka, Y. Tsuji, Compressive flow property of powder in roll-type presses—Numerical simulation by discrete element method (in Japanese), J. Soc. Powder Technol., Japan 38 (2001) 150–159. [13] R.T. Dec, A. Zavaliangos, J.C. Cunningham, Comparison of various modelling methods for analysis of powder compaction in roller press, Powder Technol. 130 (2003) 265–271. [14] J. Cunningham, Experimental Studies and Modeling of the Roller Compaction of Pharmaceutical Powders, PhD thesis, Drexel University, Philadelphia, PA, USA (2005). [15] H. Busies, Dichteverteilungen in Schülpen, PhD thesis, University of Bonn, Germany (2004). [16] A. Ho, J.F. Barker, J. Spence, T.M. Jones, A comparison of three methods of mounting a linear variable displacement transducer on an instrumented tablet machine, J. Pharm. Pharmacol. 31 (1979) 471. [17] L.E. Holman, K. Marshall, Calibration of a compaction simulator for the measurement of tablet thickness during compression, Pharm. Research 6 (10) (1993) 816–822. [18] P.M. Belda, J.B. Mielck, The tableting machine as an analytical instrument: qualification of the tableting machine and the instrumentation with respect to the determination of punch separation and validation of the calibration procedures, Eur. J. Pharm. Biopharm. 47 (1999) 231–245. [19] R.F. Lammens, C.Pörtner, Control of Production Quality During Dry Granulation with Roller Compactors, Proceedings of 3rd World Meeting APV/APGI, 3rd to 6th of April 2000, Berlin, Germany. [20] S. Körschgen, Porositätsberechnung der Schülpe während der Kompaktierung mit einer 3-W-Polygran Walzenpresse und Qualifizierung/Kalibrierung dieses Kompaktors, Diploma thesis, University of Applied Sciences of Düsseldorf, Germany (1996). [21] U. Caspar, Viskoelastische Phänomene bei der Tablettierung, PhD thesis, University of Bonn, Germany (1983). [22] R.W. Heckel, Pressure–density relationships in powder compaction, Trans. Metall. Soc. of AIME. 221 (1961) 671–675. [23] R.W. Heckel, An analysis of powder compaction phenomena, Trans. Metall. Soc. of AIME 221 (1961) 1001–1008. [24] M. Duberg, C. Nyström, Studies in direct compression of tablets—XVII. porosity– pressure curves for the characterisation of volume reduction mechanism in powder compression, Powder Technol. 46 (1986) 67–75. [25] C.J. de Blaey, J. Polderman, Compression of pharmaceuticals I. The quantitative Interpretation of force–displacement curves, Pharm. Weekblad 105 (1970) 241–250. [26] S. Erling, Trockengranulation: Entwicklung einer Basisrezeptur für die Walzenkompaktierung mit Hilfe von Exzenterpresse, Mikropaktor und Produktionskompaktor, Diploma thesis, University of Bonn, Germany (2001). [27] R.F. Lammens, The evaluation of force-displacement measurements during onesided powder compaction in cylindrical dies, PhD thesis, University of Leiden, Netherlands (1980). [28] H. Leuenberger, B.D. Rohera, Fundamentals of powder compression. I. The compactibility and compressibility of pharmaceutical powders, Pharm. Res. 3 (1) (1986) 12–22. [29] J.E. Rees, P.J. Rue, Time-dependent deformation of some direct compression excipients, J Pharm. Pharmacol. 30 (1978) 601–607. [30] Michel, B., Contribution à l'étude de l'agglomération des poudres en press à rouleaux lisses, Thèse de doctorat (PhD thesis), Université de Technologie de Compiègne, France, (1994). [31] Y. Funakoshi, T. Asogawa, E. Satake, The use of a novel roller compactor with a concavo-convex roller pair to obtain uniform compacting pressure, Drug Dev. Ind. Pharm. 3 (6) (1977) 555–573.