Thesis (Ph. D.)--Brigham Young University. Dept. of Mechanical Engineering, 2007. / Includes bibliographical references (p. 107-110).

<p></p><p>Granular material blending plays an important role in many industries ranging from those that manufacture pharmaceuticals to those producing agrochemicals. The ability to create homogeneous powder blends can be critical to the final product quality. For example, insufficient blending of a pharmaceutical formulation may have serious consequences on product efficacy and safety. Unfortunately, tools for quantitatively predicting particulate blending processes are lacking. Most often, parameters that produce an acceptable degree of blending are determined empirically.</p> <p> </p> <p>The objective of this work was to develop a validated model for predicting the magnitude and rate of granular material mixing and segregation for binary mixtures of granular material in systems of industrial interest. The model utilizes finite element method simulations to determine the bulk-level granular velocity field, which is then combined with particle-level diffusion and segregation correlations using the advection-diffusion-segregation equation. </p> <p> </p> <p>An important factor to the success of the finite element method simulation used in the current work is the material constitutive model used to represent the granular flow behavior. In this work, the Mohr-Coulomb elastoplastic (MCEP) model was used. The MCEP model parameters were calibrated both numerically and experimentally and the procedure is described in the current work. Additionally, the particle-level diffusion and segregation correlations are important to the accurate prediction of mixing and segregation rates. The current work derived the diffusion and segregation correlations from published literature and determined a methodology for obtaining the particle diffusion and segregation parameters from experiments.</p> <p> </p> <p>The utility of this modelling approach is demonstrated by predicting mixing patterns in a rotating drum and Tote blender as well as segregation patterns in a rotating drum and during the discharge of conical hoppers. Indeed, a significant advantage of the current modeling approach compared to previously published models is that arbitrary system geometries can be modeled.</p> <p> </p> <p>The model predictions were compared with both DEM simulation and experiment results. The model is able to quantitatively predict the magnitude and rate of powder mixing and segregation in two- and three-dimensional geometries and is computationally faster than DEM simulations. Since the numerical approach does not directly model individual particles, this new modeling approach is well suited for predicting mixing and segregation in large industrial-scale systems.</p><br><p></p>

abstract: Rotary drums are commonly used for their high heat and mass transfer rates in the manufacture of cement, pharmaceuticals, food, and other particulate products. These processes are difficult to model because the particulate behavior is governed by the process conditions such as particle size, particle size distribution, shape, composition, and operating parameters, such as fill level and rotation rate. More research on heat transfer in rotary drums will increase operating efficiency, leading to significant energy savings on a global scale. This research utilizes infrared imaging to investigate the effects of fill level and rotation rate on the particle bed hydrodynamics and the average wall-particle heat transfer coefficient. 3 mm silica beads and a stainless steel rotary drum with a diameter of 6 in and a length of 3 in were used at fill levels of 10 %, 17.5 %, and 25 %, and rotation rates of 2 rpm, 6 rpm, and 10 rpm. Two full factorial designs of experiments were completed to understand the effects of these factors in the presence of conduction only (Case 1) and conduction with forced convection (Case 2). Particle-particle friction caused the particle bed to stagnate at elevated temperatures in Case 1, while the inlet air velocity in Case 2 dominated the particle friction effects to maintain the flow profile. The maximum heat transfer coefficient was achieved at a high rotation rate and low fill level in Case 1, and at a high rotation rate and high fill level in Case 2. Heat losses from the system were dominated by natural convection between the hot air in the drum and the external surroundings. / Dissertation/Thesis / Masters Thesis Chemical Engineering 2019

Many industrial processes involve powders in some form when making products, and the behavior of the powders processed is impacted by the adhesion of the individual particles which comprise it. This adhesion behavior, in turn, is critically influenced by the complementarity between the topography of a surface and the shape and roughness of the particles that adhere to that surface. Problems such as poor flowability, dust hazards, and equipment wear arise due to uncontrolled particle adhesion and can lead to production challenges. Computational models have been developed to predict the behavior of highly idealized powders (i.e., powders comprised of simple geometries such as spheres) under various processes but are limited in their ability to model and optimize the manufacturing and handling of powders comprised of many complex particles. This work focuses on further developing an experimental and modeling framework, called the Enhanced Centrifuge Method (ECM), that maps particle-scale and surface properties onto experimentally-validated ‘effective’ adhesion distributions that describe the adhesion between particles in powders. These distributions represent an engineering approach that allows powders comprised of particles of complex shape and roughness, which are challenging to model, to be described as if they were perfect, smooth spheres, which are comparatively simple to model. The complexity associated with the shape and size distributions of the individual particles is captured by the ‘effective’ adhesion parameters. These ‘effective’ adhesion parameter distributions provide a quantitative guide as to how the specific particle properties are interacting with the surface topography which directly impacts the overall powder adhesion. The initial framework of the ECM is constructed around characterizing the van der Waals adhesion of silica and polystyrene powders. The impact of the surface topography and the particle properties of each of the powders is captured in ‘effective’ Hamaker constant distributions. These distributions provide a quantitative guide for specifically how the particles interact with the surface topography based on the respective scales of the particle and surface features. The ECM framework is further adapted here to investigate the effects of topographical changes of stainless steel due to polishing on the adhesion properties of three different pharmaceutical powders to the stainless steel. In this adaptation of the ECM framework, the force of adhesion was described by modifying the Johnson, Kendall, and Roberts (JKR) model describing elastic-like particle contact to a flat plate. Within the modified JKR adhesion description, the work of adhesion is tuned to be an ‘effective’ work of adhesion parameter. These size-dependent ‘effective’ work of adhesion distributions provide a quantifiable measure of the change in the powder and surface adhesion that reflects the size, shape, and topographical features on the powder and surface with which the powder interacts. To investigate environmental effects on the adhesion properties, the ECM framework is also extended to characterize the effect humidity has on altering surface and particle interactions of the three pharmaceutical powders to stainless steel. In addition to the work with the pharmaceutical powders, the investigation of the effect of humidity on the powder’s adhesion includes a study of the influence of water on the interactions between silica particles and a silica substrate. In all cases, the ‘effective’ adhesion force distributions developed through the ECM provide the ability to quickly determine quantitatively how environmental and process conditions alter particle and surface properties, and overall powder behavior.

Particle technology affects the entire human population. It is involved in the manufacture of agriculture chemicals, asphalt, paint, and pharmaceuticals, just to name a few. The size and shape of the particles play an important role in the manufacturing processes. A change in size or shape can change the product produced. Experiments were run to test the effects of agitation rate, magma density, and residence time on adipic acid and sodium chloride crystals. Experiments were executed in a one-liter, double-jacketed, stirred vessel. Digital images of the broken crystals were taken with a microscope/digital camera combo. ImagePro Plus was used to analyze the size of the broken crystals. The greatest change was seen between the two magma densities. It can be determined that change in size and shape based on the variables does exist. A specific set of variables should be introduced for each process in industry to achieve the desired results.

abstract: Granular material can be found in many industries and undergo process steps like drying, transportation, coating, chemical, and physical conversions. Understanding and optimizing such processes can save energy as well as material costs, leading to improved products. Silica beads are one such granular material encountered in many industries as a catalyst support material. The present research aims to obtain a fundamental understanding of the hydrodynamics and heat transfer mechanisms in silica beads. Studies are carried out using a hopper discharge bin and a rotary drum, which are some of the most common process equipment found in various industries. Two types of micro-glass beads with distinct size distributions are used to fill the hopper in two possible packing arrangements with varying mass ratios. For the well-mixed configuration, the fine particles clustered at the hopper bottom towards the end of the discharge. For the layered configuration, the coarse particles packed at the hopper bottom discharge first, opening a channel for the fine particles on the top. Also, parameters such as wall roughness (WR) and particle roughness (PR) are studied by etching the particles. The discharge rate is found to increase with WR, and found to be proportional to (Root mean square of PR)^(-0.58). Furthermore, the drum is used to study the conduction and convection heat transfer behavior of the particle bed with varying process conditions. A new non-invasive temperature measurement technique is developed using infrared thermography, which replaced the traditional thermocouples, to record the temperatures of the particles and the drum wall. This setup is used to understand the flow regimes of the particle bed inside the drum and the heat transfer mechanisms with varying process conditions. The conduction heat transfer rate is found to increase with decreasing particle size, decreasing fill level, and increasing rotation speed. The convection heat transfer rate increased with increasing fill level and decreasing particle size, and rotation speed had no significant effect. Due to the complexities in these systems, it is not always possible to conduct experiments, therefore, heat transfer models in Discrete Element Method codes (MFIX-DEM: open-source code, and EDEM: commercial code) are adopted, validated, and the effects of model parameters are studied using these codes. / Dissertation/Thesis / Doctoral Dissertation Chemical Engineering 2020

Confined particulate systems, particularly powder compacts, are widely used in various applications in industries such as pharmaceutical, automotive, agriculture, and energy production. Due to their extensive applications, characterization of these materials is of great importance for optimizing their performance and manufacturing processes. Modeling approaches capable of capturing the heterogeneity and complex behavior are effective at predicting the macroscopic behavior of granular systems. These modeling approaches utilize information about the microstructure evolution of these materials during compaction processes at the mesoscale (particle-scale). Using these types of modeling depend on accurate contact formulation between inter-particle contacts. The challenge comes in formulating these contact models that accurately predict force-area-deformation relationships. In this work, contact laws are presented for elastic-ideally plastic particles and plastic particles with power-law hardening under unconfined (simple compression) and confined (die and hydrostatic compaction) compression. First, material properties for a set of finite element simulations are obtained using space-filling design. The finite element simulations are used for verification and building an analytical framework of the contact radius and contact pressure which allows for efficient determination of the contact force. Semi-mechanistic contact laws are built for elastic-ideally plastic spherical particles that depend on material properties and loading configuration. Then, rigid-plastic assumption is used to modify the contact laws to consider power-law hardening effects while keeping loading configuration dependency. Finally, after building and verifying the contact laws, they are used to estimate hardening properties, contact radius evolution, and stress response of micro-crystalline cellulose particles under different loading configurations using experimental data from simple compression.

<p>Controllable continuous feeding of biomass feedstock in a biorefinery is critical to upscaling current ethanol conversion techniques to a commercial scale. Mechanical pretreatment of biomass feedstock performed using a compression feed screw (CFS) improves the ethanol yield but is subject to flowability issues, especially the plugging of biomass. The mechanical behavior, and hence, the flowability of biomass feedstock, is strongly affected by several factors, including preparation method, moisture content, physical composition, and particle size distribution. In addition, the current design of CFS is guided by limited experimentation and even fewer theoretical correlations. This thesis aims at developing computational methods to model the flow of densified feedstock in a CFS and experimental techniques to characterize the mechanical properties required for the model. We adopted a modified Drucker-Prager Cap constitutive (mDPC) law for milled corn stover (a widely used feedstock for bioethanol production) to model the material’s rate-independent bulk behavior in a CFS. The mDPC elastoplastic law captures the frictional shear and permanent volumetric changes in corn stover using a continuous porosity-dependent yield surface. The parameters of the mDPC model are calibrated using a unified set of single-ended die compaction and multiple shear failure tests. In addition, we quantified the changes in the mDPC parameters with moisture content up to the water-holding capacity of corn stover particles. A Coupled Eulerian-Lagrangian Finite Element Method model developed for the CFS geometry predicts the deformation of the material using the calibrated mDPC parameters. We model the interaction between the material and the CFS surface using a Coulomb wall friction coefficient calibrated using the Janssen-Walker method for a punch and die system. A laboratory-scale compression feed screw is designed and fabricated to characterize the flow of dense granular materials in collaboration with undergraduate students in the School of Mechanical Engineering. FEM model predictions of feeding torque and mass flow rate are validated against the laboratory-scale feeder for microcrystalline cellulose Avicel PH-200 and milled corn stover. The model predictions agree with the experiments for Avicel PH-200 but have a higher error in the case of corn stover. Some physical effects, such as shear hardening and particle erosion observed in milled corn stover, are not captured using the current implementation of the mDPC model, which explains the different model accuracies for both materials. The continuum model is used to uncover material density distribution, torque, and pressure inside the CFS, otherwise challenging through experiments. The FEM model showed a significantly higher sensitivity of the feeder performance to two material properties, namely the hydrostatic yield stress and the wall friction coefficient. The characterized variation of material properties with moisture content and the effect of each material property on the feeder performance provide strategies to engineer the feedstock for better flowability. Further, the continuum model offers a method to study design changes before manufacturing the equipment. Finally, we propose the possibility of a reduced-order analytical model based on the critical material properties and the material deformation mechanism demonstrated by the FEM model.</p>

<p>  </p> <p>Crystallization is a purity and particle control unit operation commonly used in industries such as pharmaceuticals, agrochemicals, and energetics. Often, the active ingredient’s crystal mean size, polymorphic form, morphology, and distribution can impact the critical quality attributes of the final product. The active ingredient typically goes through a series of process development iterations to optimize and scale-up production to reach production scale. Guided by the FDA, the paradigm shift towards continuous processing and crystallization has shown benefits in introducing cheaper and greener technologies and relieving drawbacks of batch processing. To achieve successful batch scale-up or robust continuous crystallization design, process intensification of unit operations, crystallization techniques, and utilizing data driven approaches are effective in designing optimal process parameters and achieving target quality attributes. </p> <p>In this thesis, a collection or toolbox of various process intensification techniques was developed to aid in control, optimization, and digitalization of crystallization processes. The first technique involves developing a novel control algorithm to control agrochemical crystals of high aspect ratio to improve the efficiency of downstream processes (filtration, washing, and drying). The second technique involves the further improvement of the first technique through digitalization of the crystallization process to perform simulated optimization and obtain a more nominal operating profile while reducing material consumption and experimentation time. The third method involves developing a calibration procedure and framework for in-line video microscopy. After a quick calibration, the in-line video microscopy can provide accurate real-time measurements to allow for future control capabilities and improve data scarcity in crystallization processes. The last technique addresses the need for polymorphic control and process longevity for continuous tubular crystallizers. Through a sequential stirred tank and tubular crystallizer experimental setup, the control of polymorphism, particle mean size, and size distribution were characterized. Each part of this thesis highlights the importance and benefits of process intensification by creating a wholistic process intensification framework coupled with novel equipment, array of PAT tools, feedback control, and model-based digital design.</p>

<p>Crystallization is a key solid-liquid separation and purification technique used in pharmaceutical industry. Some of the critical quality attributes (CQAs) of a product from crystallization process include crystal size distribution (CSD), purity, polymorphic form, morphology, etc. Different size and polymorphs of a drug substance may have different dissolution profiles and different bioavailability, which can have adverse effect on human health. Therefore, it is important to design and control crystallization process to meet product CQAs. In recent years, drug substances are becoming more complex, often being heat sensitive, which may limit the temperature that can be used in the crystallization step. Consequently, the traditional cooling only crystallization may not be well suited to recover the high value drug substances. For these systems, antisolvent crystallization is typically employed to improve the yield. On the other hand, the solvent composition can significantly impact the polymorphic outcome. Therefore, designing combined cooling and antisolvent crystallization (CCAC) processes to solve the challenges of active pharmaceutical ingredient (API) crystallization in a highly regulated environment is a complex engineering problem. </p> <p>With rising energy costs and intense price competition from generic pharmaceutical companies, the pharmaceutical industry is looking for ways to reduce the cost of manufacturing via process intensification (PI). This thesis focuses on different PI techniques for CCAC of drug substances. Continuous or smart manufacturing is gaining popularity due to its potential to lower the cost of manufacturing while maintaining consistent quality. Continuous crystallization is an important link in the continuous manufacturing process. The first part of the thesis shows PI of a commercial drug substance, Atorvastatin calcium (ASC) for target polymorph development via continuous CCAC using an oscillatory baffled crystallizer (OBC). An existing batch CCAC process for ASC was compared with the continuous CCAC in OBC and it was found the continuous process 30-fold more productive compared to the batch process. An array of process analytical technology (PAT) tools was used in this work to assess key process parameters that affect the polymorphic outcome and CSD. The desired narrower CSD product was obtained in the OBC compared to that from a batch crystallizer.</p> <p>The next part of the thesis focused on model-based PI technique for efficient determination of crystallization kinetics of a polymorphic system in CCAC. A novel experimental design was proposed which significantly reduced the number of experiments required to determine crystallization kinetics in a CCAC process. The kinetic parameters were validated, and a validated polymorphic model was used to perform an in-silico design of experiment (DoE) to develop a design space that can be used to identify operating conditions to achieve a desired crystal size and polymorphic form. </p> <p>The final part of the thesis combines the experimental and model-based approach for designing a continuous CCAC process for ASC in a cascade of Coflore agitated cell reactor (ACR) and three-stage mixed suspension mixed product removal (MSMPR). A combined artificial neural network (ANN) and principal component analysis (PCA) method was used to calibrate an ultraviolet (UV) probe which was used to monitor ASC solute concentration in the cascade process. The crystallization kinetic parameters were estimated in ACR and MSMPR which was used to build a digital model of the cascade process. The digital model was then used to obtain a design space with different temperature profile in the three-stage MSMPR that yielded narrow CSD of ASC form I. Overall, this thesis demonstrates the benefits of applying PI in the CCAC of drug substances using a holistic approach including novel equipment, application of an array of PAT tools, and model-based digital design to achieve desired CQAs of the product.</p>