Blending powder and granular constituents plays a vital role in the production of a wide array of consumer and industrial products, including ceramics, plastics, foodstuffs, and pharmaceuticals. Among the available equipment for powder mixing, tumbling blenders remain the most prevalent. A number of different geometries are available from blender manufacturers, including V-blenders, cube blenders, and double cones. However, a more recent addition to tumbling blender geometries is the bin blender, which is also known as an intermediate bulk container (see Figure 1). The bin blender was originally designed so that after blending is completed the container can be removed from the drive and transported to the next process of operation without discharging its contents into a secondary vessel (i.e., hopper, barrel, etc.). This added functionality eliminates the need for additional transport containers and avoids tying up the production line. Furthermore, this design minimizes operator contact with the blender contents, which can be hazardous. This series of three articles provides an overview of recent computational and experimental findings regarding the performance of bin blenders over a range of processing conditions and mixture types.

Figure 1: Two variations of the bin blender. (a) is a rectangular design by Gallay (Birmingham, UK), and (b) is a cylindrical design by L.B. Bohle (Ennigerloh, Germany).			

 The simplest function of a tumbling blender is to blend all the constituents of a given mixture in a single processing step. In this function, each ingredient is loaded separately into the blender, and the blender rotates until a homogenous mixture has been formed. The tumbling blender also may be used to blend lubricants into an already homogenous powder mixture. In addition, tumbling blenders can be used as preblenders for mixing a low-dose active ingredient (often cohesive) with a portion of the excipients. Once this preblending step is completed, the mixture then is transferred to a larger blender (tumbling, convective, pneumatic, etc.) and mixed with the rest of the excipients before further processing.  			

Many experimental investigations regarding the performance of tumbling blenders have appeared in the literature over the past few decades. Some studies have used broad comparisons of the utility of different blender types (2 or more at a time) for one or two particular mixtures (1-3). Other studies have investigated the mixing efficiency of one or two blenders for multiple mixtures to compare efficiency (4-6). Only a few studies have used a single blender with a single mixture to determine the effects of various operational parameters on blender efficiency (7-10). 

Figure 2: The cross section of the groove sampler when the sampler is open and empty (a), open and full (b), closed and full (c). The sample collection procedure (discharge into trays) is illustrated in (d).			

Bin blenders have only recently been specifically examined (11-13). However, these studies found bin blenders to be similar in geometry and functionality to double cone blenders, which have been more extensively covered (8, 14-17). Essentially, a bin blender is a "single cone blender"—a double cone blender cut in half. In the investigations of double cones, radial mixing (i.e., perpendicular to the axis of rotation) has been found to be more than an order of magnitude faster than axial mixing (parallel to the axis of rotation). Furthermore, inserting baffles to increase axial displacement has been shown to markedly increase mixing rates. These generic characterizations of double cone blender performance are similar to the mixing performance in bin blenders for some materials and processing conditions. However, for other mixtures, certain baffle configurations, and processing conditions, differences in blending performance occur, which will be discussed in detail throughout this article.  

The opacity of granular materials often requires the extraction of spot samples for compositional analysis to determine mixture quality. Currently, the characterization of granular mixtures is limited by the errors and biases associated with most available means of sample extraction. The most commonly used devices for sample retrieval are thief probes. End-sampling and side-sampling thieves have been shown to produce erroneous information regarding spot sample compositions (18, 19). A major problem with most thieves is that the retrieved sample is not representative of the true concentration at the location from which the sample was supposed to be obtained. These sampling errors are caused by contamination with material from other locations in the mixture during probe insertion. Also, nonuniform flow of different components into the sampling cavity can skew the sample concentrations (which is common when different size particles are present). Recent work has shown that two samplers—the groove sampler and the core sampler—which are used exclusively in this article, are more effective, accurate, and reliable than typical side-sampling or end-sampling thieves (19, 20). 

Figure 3: A sketch of a core sampler is shown in (a). The sample collection procedure is shown in (b). The metal rod is pushed by hand or by a threaded rod, and sample size is controlled by placing a scale under the sample collector. As the powder emerges from the sampler, a spatula is used to scrape powder into the collection vial, which enables better control of sample weights.						

The groove thief consists of a hollow sleeve (1 in. in diameter) surrounding a solid inner steel rod with a groove bored along most of the length of the pipe (19). The inner pipe has a sampling cavity that is ~1/2 in. deep and wide along the middle 80% of the rod. Rotating the inner pipe relative to the outer pipe opens and closes the sampler. The sampler is inserted into the powder bed while open; rotating the inner tube traps material within the sampler (see Figure 2a-c). After being removed from the powder bin, the sampler is then placed horizontally on a stand while open, and the entire device is rotated to discharge the collected material into a series of small trays (see Figure 2d). Sample size can vary depending on the size of the sampler or the width of the containers into which the material is discharged.

The other sampling technique uses a core sampler (a hollow tube filed to a thin edge at one end) to gather samples. The tube is thrust into the mixture and retrieved, leaving a core of material in the sampler that is held in place by static friction forces, and is then extruded in a last-in-first-out manner (see Figure 3). The use and accuracy of this sampler has been described extensively (20). This sampler has proven to give more-accurate representations than typical thief probes of mixture distributions while simultaneously causing less disturbance of the powder bed.  

Avoiding contamination during sample collection is vital, but determining the location and number of samples to extract from the mixture is equally important. Often, samples are taken from throughout the bed to ensure complete coverage of the entire mixture. Although this approach guarantees thoroughness, it can lead to wasted time, effort, and material if more efficient means are available. 

Figure 4: Segregation patterns in a 14-L bin blender for a mixture of ~1800 gold and ~800 purple glass beads.  Different patterns are noted when the blender is run at 5 rpm (a), 15 rpm (b), and 25 rpm (c).			

Mixing in tumbling blenders is often limited by the axial transfer of material or by segregation of the components (usually caused by variations in particle characteristics such as size or shape). Previous work has shown that segregation of mixtures in some tumbling blenders creates axial gradients in concentration (16, 21). Similar results occur in a bin blender when a binary-distributed mixture of glass beads is run at constant rotation rate. Figure 4 shows the three types of segregation patterns that form in a binary mixture of 1.6mm and 600μ glass beads when run at different rotation rates. These segregation patterns correspond exactly to those seen in double cone blenders over a wide range of rotation rates and particle sizes. The mechanisms and effects of particle size and particle size ratio have been discussed in detail (16).  

These data imply that axial sampling of the blender is vital whereas 

 sampling (sampling at multiple locations on the same line perpendicular to the axis of rotation) may be superfluous. This concept has been tested in a 56-L bin blender. Figure 5a shows a typical total sampling scheme for a circular opening using 14 core sampler locations. In Figure 5b, the variance measured using only the axial samples (i.e., cores 1, 8, 9, 12, 13) is compared to the results obtained from using all the probes. Although the number of samples has been reduced by almost a factor of 3, the resulting variance versus revolutions data show very good agreement, indicating that limited axial sampling gives information equivalent to that obtained from total sampling (19).

Figure 5: (a) A typical sampling scheme for a blender with a round opening on the top. 			

Typically, a measure based on total mixture variance has been used as the means to track the evolution of mixture quality in tumbling blenders. When generating total mixture variance, all of the samples from a given time point are used to generate variance (σ

), standard deviation (σ), or relative standard deviation (RSD = σ/M where 

 = mean), which can be inputted into a suitable mixing index and tracked over time. In laboratory-scale experiments, the blender is commonly loaded anew for each time point, and at larger scales, the blender often is periodically stopped and sampled. This experimental/statistical approach provides broad insight into the rate at which different materials will mix in a single blender, or can distinguish one blender from another for similar parameter settings (fill, rotation rate, etc.). The drawback is that using a single measure of mixture quality does not give much insight into mixing mechanisms within a specific blender. For instance, a poor mixture can result from different regions of the blender containing material with moderately different concentrations or from a few wayward samples (very high or very low) caused by agglomeration of the active substance or dead zones in the blender. A single measure of variance may not differentiate between these situations, which is important because radically different approaches are needed to fix these different classes of mixing problems. 

Figure 6: Two mixture distributions are shown corresponding to high radial variance (a) and high axial variance (b). In mixture (a), each core has the same average concentration, but the sample concentrations within the core vary considerably. In mixture (b), each core has a different mean concentration, but every sample within each core is uniform in concentration.								

One way to gather more information without the need for more sampling is to split up the total variance measurement into separate dependent measurements of axial variance and radial variance. Rather than use all obtained samples to generate a single measure of mixture quality (i.e., total variance), samples are grouped together in such a way to generate individual measurements of axial variance and radial variance. Radial variance can be interpreted as WL (within location) variance and axial variance as BL (between location). The use of core or groove sampling greatly aids this differentiation because the average value of entire cores (grooves) can be used to determine axial variances whereas the variance of samples taken from a single core (groove) can determine radial variances (22). Figure 6 shows two mixtures that would give similar results for total sampling but very different results when the variance is split into axial and radial components. For Figure 6a, high radial variance and low axial variance would be measured, whereas for Figure 6b, the opposite, low axial and high radial variances would be detected. The extra information supplied from axial and radial variance measurements would give important clues on the best means for approaching a specific mixing problem. 

The splitting of variance is established by first defining an axial zone 

 is the number of samples within that zone/core. The standard definition of variance is 

  is the mean composition. Substituting equation [1] into equation [2] and rearranging, leads to 

In equation [3], the first term is a measure of axial variance and the second term radial variance.  

To quantitatively compare mixing efficiency, it is useful to determine the rate of variance (or RSD) decrease at different processing conditions. A quantitative measure of the rate of variance decrease can be obtained by assuming exponential decay (7, 8) and defining a mixing constant k such that 

) is the elapsed number of revolutions (RSD can be substituted for σ

 yields a quantitative measure that can be used to evaluate how changes in process parameters or mixture composition affect mixing rates in the blender.  

The use of variance-related measurements is the most common way to assess mixture quality. In some cases, however, other variables can be used to aid in the assessment of mixing phenomena in the blender. For example, tracking the sample mean (average of all the extracted samples) can indicate that the blend is not uniform if the sample mean deviates significantly from the expected mean. Another strategy is to track the change in minimum and maximum samples over time. If the minimum and maximum samples are highly aberrant from the mean, this could indicate agglomeration or the presence of dead zones in the blender, both of which can be extremely detrimental to overall blender performance. When analyzing mixing performance in a blender, it is important to use all available information to build the best possible understanding of the dynamics within the blender. 

Figure 7: Sketches of the visible flow patterns on the surface of mixtures in rotating cylinders for mixtures of free-flowing (a) and cohesive particles (b). For free-flowing mixtures, the flow is straight, regular, and downstream. For cohesive mixtures, groups of particles avalanche down the cascade in multiple directions and these failures can start from almost any point on the surface of the mixture.			

			For mixing in bin blenders (or any tumbling blender), perhaps the most important aspects to assess are the effects of the basic parameters (i.e., those that can be varied for a single blender using one or many mixtures) on the mixing process. Two parameters, the loading method (how the constituent materials are put into the blender) and the fill level (the percentage of total blender capacity occupied by the material) are always variable, while in some cases, the rotation rate of the blender also can be adjusted. General guidelines and caveats regarding the effects of fill, loading, and rotation rate are described below.  							

 Lower fill levels induce faster mixing rates.

 When the amount of material in the blender is reduced, mixing should be faster. However, very low fill levels (<~25%) interfere with natural mixing mechanisms and hinder mixing rates. Filling the blender to more than 60% of its capacity can lead to dead zones in the middle of the mixture that do not interact with the rest of the mixture. 

 Symmetrical top-to-bottom loading will stress radial mixing and, hence, mix faster than loading the blender left-to-right, which emphasizes slower axial mixing. 

Figure 8: The RSD is plotted against the number of revolutions for simulations and experiments of 8-mm spherical particles, which ran at 10 rpm in a 14-L bin blender. The mixing rates show good agreement between simulation and experiment.			

 Changing the rotation rate has been shown to have no effect on mixing rates for free-flowing materials at moderate rotation rates (<25 rpm) using relatively small blenders (7, 8). However, these findings have not been adequately tested using cohesive mixtures. For very cohesive mixtures, shear becomes the dominant factor and rotation rates may play a decisive role in determining mixing rates. 

Before discussing specific mixing mechanisms for bin blenders, it is useful to examine some common rules that apply to tumbling blender performance. Perhaps the most obvious (and most important) observation is that radial mixing has been found to be more than one order of magnitude faster than axial mixing (8). Radial mixing is believed to occur as the mixture winds around a central point at the interface between the cascading layer and material undergoing solid-body rotation, forming striations and layers that become finer with increasing rotations (23). Axial mixing is believed to be dependent on random axial fluctuations in particle velocities as the particles flow down the cascade, generating a slow dispersive mixing process (24). However, these generic mixing mechanisms fail to provide any insight into approaches for improving mixing performance beyond preference for radial mixing over axial mixing. Another feature that has not been addressed is the critical role of mixture properties (especially cohesion) in generic mixing mechanisms. A major running theme throughout this series of articles is that mixture characteristics (however poorly defined they may be) can be much more important for determining mixing rates and overall performance than blender or operational specifics. This observation indicates that the development of effective methods for defining and measuring critical material properties will be crucial for a more comprehensive understanding of tumbling blender operation. 

Figure 9: Mixing performance of Benadryl in a 56-L bin blender is assessed by examining total variance decrease (a), variation in maximum and minimum sample concentration (b), radial variance decrease (c), and the time evolution of the sample mean (d). The grey line is the mixture mean.						

 Effects of mixture characteristics on mixing mechanisms.

 The major obstacle plaguing the definition of mixture characteristics is the lack of available means for meaningful comparisons of mixture cohesion. The inability to usefully and quantitatively define particle and/or mixture characteristics means that mixtures become qualitatively characterized as 

mixture is one for which flow is determined by the dynamics of individual particles. In essence, each particle can be tracked and accounted for separately and mixing mechanisms arise from the time-averaged flow of these individual particles. A 

mixture is one for which single particles do not flow independently; rather, groups of particles act in concert when force is applied to the entire mass (avalanching). Hence, mixing mechanisms derive from the motions of groups of particles rather than single particles and there may not be any meaningful time-averaged flow field.  

Outside of spherical glass beads, very few materials are truly free-flowing. Granulated lactose and sand both exhibit some avalanching tendencies but are still considered free-flowing. Other typical pharmaceutical materials such as microcrystalline cellulose and micronized lactose clearly are cohesive and exhibit strong avalanching dynamics. These differences in mixture characteristics have a significant effect on the resulting flow characteristics and mixing mechanisms. 

Articles

In this series of articles, bin blender performance is comprehensively reviewed using both free-flowing and cohesive mixtures. In part 1, an introduction to tools and techniques is presented, followed by an examination of parameter effects, mixing mechanisms, and the effects of cohesion on mixing.

Characterization of the Performance of Bin Blenders Part 1 of 3: Methodology

The author describes a separation method for two active ingredients in the contraceptive pill with liquid chromatography UV detection.

An LC Method for the Separation and Determination of Ethynodiol Diacetate and Ethinyl Estradiol in Tablets

Pharmaceutical Science & Technology Innovations

In The Spotlight-May 2004

New technologies, along with changes in FDA oversight, may provide a critical path to make more new drugs available more quickly to patients.

FDA Eyes Manufacturing Innovation to Boost Drug Development

Drug makers and their suppliers will need good planning and organization to meet the new bar coding requirements for drug products used in hospitals.

Prepare for Bar Coding

From data acquisition to enterprise resource planning, software systems operating at all levels of pharmaceutical manufacturing prepare for the seemingly inevitable implementation of process anlytical technology.

Validation and Compliance: Software Systems Prepare Manufacturers for Com(PAT)ibility

Sponsors who have been lax in performing financial due diligence on their contractors have been given a wake-up call by the recent developments at aaiPharma.

aaiPharma Situation Underscores The Need for Due Diligence

The author suggests that an excipient's functionality can only be determined in the context of a specific formulation and manufacturing process.

Excipient Functionality

Bin blender performance is comprehensively reviewed using both free-flowing and cohesive mixtures.

Pharmaceutical Technology-05-02-2004

Characterization of the Performance of Bin Blenders Part 1 of 3: Methodology

An LC Method for the Separation and Determination of Ethynodiol Diacetate and Ethinyl Estradiol in Tablets

In The Spotlight-May 2004

FDA Eyes Manufacturing Innovation to Boost Drug Development

Prepare for Bar Coding

Validation and Compliance: Software Systems Prepare Manufacturers for Com(PAT)ibility

aaiPharma Situation Underscores The Need for Due Diligence

Excipient Functionality

Characterization of the Performance of Bin Blenders Part 1 of 3: Methodology

May 2004