If you want to volunteer for a hopeless job become your company’s expert on solids blending. It’s not that you’ll always be wrong but it’s very hard to be right all of the time. There’re a host of theories and even more statistical studies but a unified theory for all situations hasn’t been developed — yet. It’s been said that in theory there’s no difference between theory and practice. In practice, however, there is!
The person who coined that phrase was talking about solids blending. What makes it so difficult? Why can’t we get every 1 gram container out of 100 lb. of blend to be the same? The answer is that we’re dealing with discrete pieces that have physical size, electrical properties, frictional differences and surface characteristics that can change with the environment. In addition, particulate solids are neither solid nor fluid but are made up of both.
The chemical industry makes a vast array of solids, especially products for the consumer (e.g., detergents, cleaning supplies, cosmetics and pharmaceuticals). After all, it’s often more cost effective and less hazardous to ship particulate solids than slurries or liquids. However, the manufacturing process can be fickle and variable in product color, size, composition and surface characteristics.
That variation requires mixing of the batches or even a continuous blending of the production stream to smooth out these variations to get a uniform blend. It’s easy to equate blending with mixing but these are quite distinct processes for solids. Mixing can mean coating of solids, either with other solids or liquids, or combining solids of different physical features (size, color, hardness or composition). Blending is a sub-set of mixing and is done to further improve on the quality and uniformity of the solids mixture, often just prior to a final manufacturing step to ensure minimal lot-to-lot variation or a narrow product specification.
Other times it’s needed to meet a tight shipping specification of a large quantity of material or a mixture of different chemicals. However, the more you handle solids the more likely there’ll be undesirable results, such as attrition, segregation, change in color (oxidation or contamination) or change in surface characteristics. Even when a product has been blended by the manufacturer, it may not arrive at the consumer in the desired form. Getting a good blend is difficult — keeping it that way can be even harder due to segregation or settling.
Barriers to uniformity
Several factors can work against a good blend of solids. A key one is the limits of randomness. The best blend would be a random mixture that retains any individual variation (e.g., color, shape) in the particles. It’s important to understand that a random mixture has variations. When it comes to solids, there are two principle measures of blend uniformity: accuracy and precision. To be well blended, the mixture must be both.
Accuracy is a measure of the average of multiple tests while precision is the reproducibility of a measurement. These often are expressed as average and standard deviation or some combination, such as a student’s t-value or coefficient of variation (CoV). Precision is limited by randomness for particulate solids. (For more on data analysis, see CP’s ongoing Dr. Gooddata series, view Part 1.
To further complicate the situation, particulate solids have multiple physical properties that limit randomness of a mixture. The most common ones are:
Size. As particles become larger, a random mixture has a wider CoV for the same sample size. This can be minimized by taking larger samples for tests or multiple samples and then averaging the results. However, there’s a lower limit to the precision of small samples as the size of the particles in the mixture increases.
Shape. Angular particles can interlock with each other to form a pseudo-larger particle. The additional void space isn’t necessarily uniformly distributed and finer particles can become concentrated in different areas of the mixture. Even spherical particles can pack in different ways under pressure and create more void space that can be filled with fine particles.
Distribution. Fine particles can coat larger particles and change the frictional characteristics so that flow of the particles over each other is impeded. They can escape the blender’s mixing surfaces (paddles, walls) and accumulate in clumps. The wider the size distribution the more likely the particles will segregate during handling operations. Also, fine particles can increase the ability of the mixture to behave like a fluid. While that would be good for uniformity, it often occurs in only a portion of the mixture, resulting in a poor blend.
In addition, electrical and chemical properties of the individual particles can alter their binding characteristics.
Consider what happens in a ribbon blender, which is one of the simplest blenders. The solids are added and the unit is run until the desired blend is reached. While this can be successful in a wide variety of cases, there’re numerous examples where the solids never reached an acceptable blend or the quality of the blend was variable. In addition, segregation can occur on discharge of the batch. Material testing is often cited as a way to get uniform results but variations in product temperature, moisture or even surface roughness can make this an expensive and time-consuming task.
A systematic approach for choosing a blender that starts with measurement of material flow properties has been known to work very well (see “Selecting an effective blender,” CP, Oct. 2001, p. 65). However, even with the material properties well-defined by flow testing, solids can be over-blended. For instance, it’s not unusual for materials in a ribbon blender to reach a “random” state after a few minutes and then slowly drift away from the perfect blend. This can be due to triboelectric effects (charging of the particle surface due to the impact of flow), attrition, fluidization or simply coating of large particles with smaller particles due to temperature gradients (thermophoresis). So, even if you use a materials testing approach for design, you still need to understand these additional factors during operation.