Modern powder-testing methods can offer valuable insights into bulk material behavior and lead to efficient powder-processing solutions
Process simulation is a powerful tool for engineers in the chemical process industries (CPI) and one that plays an important role in commercializing new manufacturing routes and supporting process optimization. While it might be costly to construct a robust process model, once such a model is in place, it will deliver value throughout the lifetime of the process. For powder processors, however, comprehensive simulation is not yet an industrially practical option, largely because the mathematical modelling of powder behavior cannot yet be reliably achieved. Establishing a successful manufacturing solution by applying experimentation and process simulation in combination is therefore not viable, so alternative methods must be considered.
Traditionally, companies that handle and manufacture powders have relied heavily on processing experience, supported by basic forms of powder testing. However, this approach is more difficult to sustain within the lean-manufacturing environments that now prevail across all industrial sectors. Developing a knowledge base to extend powder processing expertise is seen as increasingly important, whether in the development of continuous processes for pharmaceutical products or in advancing the application of additive manufacturing (AM).
This article provides a review of the challenges associated with powder processing and characterization, and demonstrates how modern powder-testing methods provide increased insight to support efficient process development and optimization. An example case, describing work carried out by a leading global powder processor, illustrates the potential benefits of adopting a pragmatic approach to powder handling that is based on measuring and applying powder properties that are directly relevant to a specific process.
Powders are ubiquitous throughout the CPI. As raw materials and intermediates, they are used in the production of goods, ranging from processed foods and pharmaceuticals to paints, coatings and metal components for automotive and aerospace applications. Learning how to control powder behavior to meet advancing manufacturing requirements is critical, especially as markets become more competitive, and as the use of new production techniques, such as additive manufacturing, grows.
Powders are bulk assemblies (Figure 1) comprising solids, in the form of particles, gases (normally in the form of air), and liquid, (usually present in the form of moisture on the surface of the particle or within its structure). A network of complex interactions between these constituent elements dictates the bulk properties of powders, such as flowability, compressibility, permeability and the ability to aerate, which give powders their industrial versatility.
The behavior of a powder is a function of the physical properties of the particles, as well as other external variables. For example, physical properties include particle size, morphology and surface texture. Variables associated with the bulk assembly include the extent of air and the level of moisture content in the bulk. The complex, numerous and varied interactions complicate efforts toward process modeling and also make the measurement and control of a bulk powder challenging.
Modeling and powder testing
Considering modeling first, there have been many attempts to mathematically model powder behavior, and this area remains one of continuing study, particularly as computing power increases. The starting point for the mathematical modeling of powders tends to involve uniformly sized, spherical particles — typically microscopic glass beads — and interesting, important progress has been made. However, powder modeling is still a predominantly academic activity and not yet a practical tool for industrial processors. Progressing from the modeling of “ideal” particles, to a situation that more accurately represents the types of materials in routine industrial use, increases the complexity of the modeling significantly. The reality is that all of the particle properties and environmental conditions that influence powder behavior cannot yet be reliably replicated and there is still a lack of robust correlations between the physical properties of solid particles and bulk powder behavior.
These acknowledged limitations place the focus on powder characterization and invite the question of how to best use it in process-related investigations. As discussed, many variables have an impact on powder behavior, making robust characterization considerably more challenging than for a liquid or gas. For example, if a powder sample absorbs even a small quantity of moisture from its environment, then a repeat measurement of its flowability may be compromised. There are many aspects of powder behavior to investigate, and gathering reliable data requires close control of both the analytical methodology and the powder sample.
When considering the behavior of powder in a given process, it is necessary to identify robust, reproducible powder-testing methods capable of delivering data that directly correlate with in-process performance. The various unit operations in a given process subject powders to very different stress and flow regimes. These vary from unconstrained, highly aerated flow in a fluidized bed or pneumatic conveyor, to high-pressure compaction in a tablet press. Optimizing such processes relies on capturing relevant information that defines performance in each specific unit operation.
The multivariate approach
There are many methods available for measuring the properties of powders, and in particular the flowability of powders, which is a defining characteristic for many industrial applications. Simple flowability methods include flow-through-an-orifice, angle-of-repose and tapped-density techniques, such as Carr’s Compressibility Index and the Hausner Ratio. While these may provide some value in classifying powders, their ability to directly simulate the stresses and flow regimes experienced by a powder during processing is limited. This, and other limitations, restricts their use as tools for reliably predicting process performance.
More sophisticated testing techniques include shear-cell analysis and dynamic testing. Shear testing (Figure 2) involves measuring the forces required to shear one consolidated powder plane relative to another. Tests are conducted using consolidated powders and help to quantify how easily a powder transitions from a static to dynamic state following exposure to moderate- to high-stress conditions (consolidation). A good example of the application of shear testing is in the design of hoppers, where they bring considerable value. However, shear-cell analysis is much less relevant when predicting how powders will behave in low-stress dynamic environments, for example, when loosely packed or aerated.
Dynamic testing generates properties, such as basic flowability energy (BFE) and specific energy (SE), that directly quantify how a powder flows under different conditions. These properties are generated by measuring the rotational and axial forces acting on a blade, or impeller, as it is rotated through a powder sample along a fixed helical path. BFE is measured using a downward traverse of the blade, which exerts a “bull-dozing” action that compresses the powder against the confining base of the test vessel. The resulting data are indicative of flow behavior in a low-stress, confined environment. In contrast, SE is measured during an upward traverse of the blade, and quantifies the flow properties of a powder in low-stress, but unconfined, conditions. Uniquely, dynamic testing offers the ability to evaluate powders in consolidated, moderate stress, aerated and even fluidized states to investigate how a powder responds to air — a critical aspect of behavior in many applications.
The above discussion underlines the value of adopting a multifaceted approach to powder testing, because no single powder property is relevant to every process. Powder testers based on just one technique are unlikely to provide the information required to define critical operational parameters, whereas a device that provides multiple methods is able to quantify a wider range of process-relevant properties (Figure 3). For every powder, a unique combination of these properties will determine a powder’s in-process performance.
A multifaceted testing strategy should provide information on a range of properties in order to capture all characteristics of a given powder. However, this approach invites the question of how to identify the most valuable variables for predicting performance in any given powder application. This is essential for developing a streamlined testing regime that will efficiently elucidate processing behavior and enable successful control. To identify these key variables, powder processors must draw on operational observations and results, and correlate them with powder-characterization data. Operational experience often resides within a company in a subjective form and, as a result, it can be difficult to apply. Correlating such experience with reproducible powder properties converts it into more generally applicable knowledge (Figure 4).
Solid blends example
The following example offers an illustration of this approach. A powder is selected for use in additive manufacturing (AM), on the basis of trial and error, due to its ability to process well in a specific machine and to produce high-integrity products with the desired finish. To reduce waste, and associated costs, the decision is made to recycle residual powder from the process, mixing it with fresh feed before processing. Experimental data show that certain fresh-recycled blends work well while others do not.
There are two approaches to establishing some operational guidelines for successful powder re-use. One is to conduct a series of experiments, using mixtures with different fresh-to-recycled ratios, check the performance of each on the AM machine and define an operating ratio of fresh-to-recycled materials that falls somewhere within the pass/fail boundaries. The alternative is to correlate the results from trials using mixtures that have different fresh-to-recycled ratios with a database of powder properties gathered for each mixture. The output from this study might reveal that performance of a mixture within the AM machine correlates directly with, for example, BFE, permeability and shear strength. This makes it possible to define a clear and detailed specification for a powder that is suitable for processing in this machine.
Both approaches may initially be successful, but the benefit of the second is that there is now a robust specification to identify powders that are compatible with the process so, if the feed is changed again, if a new powder is used, or if waste powder is recycled not once but twice, the impact can be assessed with confidence simply by testing the powder. In contrast, the former approach provides no generally applicable guidance with which to assess a new powder, and therefore cannot support extrapolation beyond the tested experimental window.
In this way, rigorous, relevant and robust powder testing can engender a systematic approach to powder processing that moves beyond the empirical, experience-based practice that is traditionally employed. Ultimately, such a strategy pays dividends in the development of new formulations, the advancement of process design, the day-to-day operation of the plant and in-process troubleshooting. The following case study demonstrates the potential for this type of approach.
Pragmatic approach in action
AZO GmbH + Co. KG (Osterburken, Germany; www.azo.com) is a specialist supplier of bulk-material handling equipment for a range of industries. The company serves a wide variety of customers across the food, pharmaceutical, bulk chemical and polymer sectors. Clients rely on AZO to specify turnkey solutions for powders and granules that include: silos and hoppers for storage; filling stations for either intermediate bulk containers (IBCs) or sacks; dosing and weighing systems; and pneumatic-conveying plants. The company operates a full-scale test facility that can conduct trials and optimize handling solutions, but these trials are relatively costly to run. With over 500 new materials assessed each year, there is a considerable economic incentive to streamline process design and optimization for each new powder.
For a number of years, AZO relied on two powder-measurement systems to support process development and optimization: a shear cell and a basic powder tester that measured angle-of-repose and Carr’s Index. However, these methods were not ideal in terms of efficiency or applicability. In particular, neither provided detailed information on fluidization behavior, which is highly relevant to certain unit operations. Furthermore, shear-cell analysis, although valuable for silo and hopper design, was both time-consuming and highly operator-dependent (relying heavily on the skills of a single experienced technician).
In 2010, AZO made the decision to invest in new powder-testing instruments to overcome these limitations and secure more relevant information on powder characteristics. This decision was strongly influenced by the instruments’ ability to provide both automated shear testing and dynamic powder testing. Today, AZO’s approach to process design and optimization is far more efficient than before, in a number of key areas.
Storage. With automated shear analysis in place, together with integrated software that applies Jenike’s well-established hopper-design algorithms, the AZO team now has tools that enable them to design optimal storage solutions far more easily and effectively than they could when using the old shear cell. Measurement times for shear properties have been reduced by a factor of four and more people can confidently perform the analysis. Wall-friction testing, which quantifies the strength of the interactions between a construction material and a powder, is now carried out routinely to establish the best material of construction or coating for any given powder.
Jenike’s protocols are designed to generate hopper dimensions that support mass flow, but when powders are extremely cohesive, this cannot always be achieved. In these instances, the Jenike methods may fail. For example, they may generate values for outlet size that are wider than the diameter of the silo. For these powders, efficient hopper discharge depends on the use of mechanical aids, such as vibrating devices or air-injection systems. The new testing regime rapidly and robustly identifies powders that fall into this classification, thereby helping to define an optimal storage solution.
A further issue in powder storage is the potential impact of consolidation or “caking” over time. The compressing load on a powder stored under its own weight can have a significant impact on its flowability. Using dynamic powder testing, researchers at AZO have directly investigated this aspect of behavior and used the results to guide operational practice. For example, testing helped determine the frequency with which a silo must be emptied.
Powder discharge. During discharge from a storage vessel, there is potential for a powder to draw in air and become fluidized, to the point of uncontrollable flooding from the hopper. Rotary valves are an option for controlling powders that readily flood in this way, while doping screws are an alternative for more challenging materials. Deciding on which system to adopt is critical.
The AZO team has assessed whether the ability of the powder tester to directly quantify the response of a powder to air (via dynamic testing) can be exploited to classify a powder’s propensity to fluidize in this way. Aeration data have certainly proven to correlate with discharge characteristics, however, investigations have shown that other powder properties also influence this behavior, further supporting the need for a multifaceted test regime.
Pneumatic conveying. In pneumatic conveying, powders may be transported in a fluidized state — a state that can be studied directly using dynamic powder characterization. Through appropriate testing, it is possible to determine whether a material can be fluidized or not, and to measure the air velocity needed to reach fluidization. At AZO, such testing now supports the optimization of operating parameters for fluidization.
When designing pneumatic-conveying systems, two parameters are especially important: throughput and pressure drop. Tests suggest that a number of dynamic powder properties and the bulk property of permeability are all important when determining these parameters. AZO researchers are establishing closer correlation between pneumatic-conveying performance and a number of powder properties, enabling them to strengthen design capabilities in this area.
A dynamic industry
Developing, operating and optimizing powder processes introduces significant challenges, particularly in the absence of tools, such as accurate process simulation, that may now be taken for granted in other fields. However, powder-characterization techniques and technologies have advanced considerably in the last decade or so, supporting a move away from the inefficient “trial-and-error” approach to powder processing.
Experience suggests that by combining test methods — most productively dynamic, bulk and shear-property measurement — processors can characterize powders in ways that successfully elucidate, rationalize and control in-process behavior. By employing a pragmatic approach, based on the use of multiple powder properties and operational experience, powder processors can access new levels of efficiency and meet industrial requirements for more competitive, leaner processing.
Edited by Scott Jenkins
Jamie Clayton is the operations director at Freeman Technology Ltd. (1 Miller Court, Severn Drive, Tewkesbury, Gloucestershire, GL20 8DN U.K.; Phone: +44 1684 851551; Email: firstname.lastname@example.org; Web: www.freemantech.co.uk). He graduated from the University of Sheffield with a degree in control engineering and is responsible for all daily activities of the company, including overall management of the administration, production, R&D, sales and customer support teams. Clayton also works with the company’s clients to provide application-based support.
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