GRANULATION OF ULTRA-FINE POWDERS: EXAMINATION OF GRANULE MICROSTRUCTURE, CONSOLIDATION BEHAVIOR, AND POWDER FEEDING

Davis, Nathan B. Ph.D., Purdue University, December 2015. Granulation Behavior of Ultra-Fine Powders: Examination of Granule Microstructure, Consolidation Behavior, and Powder Feeding. Major Professor: James Litste

A Three-Dimensional Population Balance model of Granulation Processes Employing Mechanistic Kernels

Granulation is a process for agglomeration where powder material is combined with liquid binder solution to facilitate the formation of larger, free-flowing granules. Granulation has become a mainstream process amongst the industries with applicability in numerous areas, which include the pharmaceuticals, mineral processing, fertilisers and in the production of a range of commodity products. A major driVing force for the production of granules from their ungranulated counterparts arises from the economic savings Le., increased bulk density permits savings to be made in transportation. and storage. Furthermore, granules may be tailored to possess certain desirable attributes that will suit the final application of the granules. Granulation is an example of a process that exhibits complex interactions between the underlying granulation phenomena such as nucleation, consolidation, aggregation and breakage. In addition, the granUle properties are distributed heterogeneously across the entire particle population posing as a particular challenge in generating a mathematical model that is able to accurately describe the granulation behaviour. The modelling approach used in this study is different from common practices, which tend to rely on heuristics and empiricism for the operation of the granulation process. This empirical approach signifies a disconnect from our understanding of the underlying physics of the process, which poses as a impediment towards the efficient operation of granulation processes. The work presented in this thesis attempts to address this disconnect by applying a threedimensional population balance with mechanistic representations for the underlying granulation rate processes. The population balance framework is ideally suited for this particular process, as it enables the evolution of the granules to be tracked with respect to differentiating particle traits, e.g. the granule size distribution. The selection of the desired properties is influenced by the importance of these particle properties on the end granule product, and also by their influence on key process mechanisms. A novel mechanistic nucleation kernel is developed incorporating fundamental material properties pertaining to the powder substrate and the liquid binder solution. The model form of the nucleation kernel is formulated by drawing a parallel with the collision/transition state theory. There are few literature reports on the inclusion of nucleation phenomena in the population balance models of granulation processes, let alone a mechanistic nucleation model. This study is one of the first in this regard. The recent recognition of the importance of the wetting kinetics and the nucleation thermodynamics on the nucleation phenomenon has been factored into the nucleation kernel by explicitly accounting for the effects of the liquid flow rate and the physicochemical properties of the material properties (surface tension, contact angle, and spreading coefficient). Batch granulation experiments were conducted obtaining granule measurements with respect to the size distribution, porosity and fractional binder content. Preliminary results for the validation of the population balance model with the experiment-measurements showed a good agreement, providing partial albeit valuable validation of the population balance model. This is also one of the first studies to model and validate a three-dimensional population balance model for granulation. Model based analyses were also carried out under a variety of processing conditions, which included the effects of changing formulations, droplet size effects, feed size distribution and the effects of powder and binder properties. The proposed model demonstrated the interactions for a range of feed formulations in tandem with granulating operating conditions, establishing qualitative agreement with similar findings derived from past experimental studies.Imperial Users onl

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

© 2017 The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions

Multi-scale population balance modelling and controllability of granulation processes

Imperial Users onl

The effect of formulation and processing variables on granule microstructure

Imperial Users onl

Proceedings of the 11th IEA International Workshop on Beryllium Technology (BeWS-11), Barcelona, Spain, 12-13 September 2013 (KIT Scientific Reports ; 7686)

The 11th IEA Beryllium Workshop (BeWS-11) was jointly organized by the KIT, Germany and Ciemat, Spain and was held at the Technical University of Catalonia, Barcelona Tech (UCP). The International Organizing Committee (IOC) and all participants expressed their appreciation to the Local Organizing Committee for the truly excellent organization of the workshop at Ciemat. About forty scientists participated, coming mainly from Europe, U.S.A., Russia, Japan, and Kazakhstan

Audible Acoustic Emissions for Monitoring High-Shear Granulation

High-shear wet granulation is a size enlargement process commonly used by the pharmaceutical industry to improve powder properties for downstream processes, such as tabletting. Granule growth however, is difficult to predict because the final product is sensitive to raw material properties and processing conditions. Development of process analytical technologies (PATs) is recommended by regulators to improve process understanding and monitor quality online. This work investigates the robustness of AAEs as PAT for high-shear granulation. Condenser microphones suspended in the air exhaust were used to collect AAEs from granulation. Applying power spectral density (PSD) analysis, trends related to wetting and end-point were identified between 20 and 250 Hz. A consistent decrease in PSD was observed with end-point, independent of formulation, material properties and process parameters. A design of experiment (DOE) showed the decrease results from increases in granule size and density, two critical quality attributes. Multivariate analysis confirmed AAEs could be used to monitor changes in size and density online. AAEs were also sensitive to changes in impeller speed, spray rate and total binder volume, suggesting these parameters could be used to adjust processes in real-time and achieve desired product attributes. The source of granulation AAEs was investigated by rotating spheres and wet granules of known size in stainless steel beakers. The results confirmed a relationship to particle size, and revealed AAEs are generated by particle-particle interactions. Overall, the research supports adoption of AAEs as a PAT for high-shear granulation to increase process knowledge and improve product quality

Experimental and model-based analysis of twin-screw wet granulation in pharmaceutical processes

A shift from batch to continuous processing is challenging but equally rewarding for the pharmaceutical sector. This opportunity for moving beyond traditional batch processing is possible due to a change of attitude in the regulatory environment by the publication of the process analytical technology (PAT) guidance. However, in order to utilise this opportunity, detailed process understanding about the key processes in pharmaceutical manufacturing is required to turn this transformation to the continuous mode into a success. Continuous wet granulation is a crucial part of future continuous manufacturing of solid dosage forms. Continuous high shear wet granulation is performed using a twin-screw granulator (TSG) which is characterised by a modular screw profile including a sequence of different screw elements with various shapes, orientations and functions. A TSG achieves mixing and granulation by a complex interplay between the screw configuration and process settings (e.g. feed rate, screw speed, etc.) to produce granules with certain specifications in a short time. Therefore, a fundamental understanding of these complex phenomena is required to optimise and control this new technology. Analysing the twin-screw wet granulation to a satisfactory degree is only possible when sufficient information on the rheo-kinetic characteristics of the granulation mixture is available. Thus an investigation of residence time distribution (RTD), the solid-liquid mixing, and the resulting granule size distribution (GSD) evolution governed by the field conditions in the TSG contain interesting information about mixing and different granulation rate processes such as aggregation and breakage. For this purpose, a combination of experimental and mathematical techniques/approaches was applied in this work. Additionally, a single placebo formulation based on α-lactose monohydrate was granulated in the experimental studies performed to verify the hypothesis proposed in this work. The characterisation of wetted material transport and mixing inside the confined spaces of the rotating screws was performed by the experimental determination of the residence time distribution at different process conditions and screw configurations using near infrared chemical imaging. The experimental data was later compared with a conceptual model based on classical chemical engineering methods to estimate the parameters of the model and to analyse the effects of changes in number of kneading discs and their stagger angle, screw speed, material throughput and liquid-to-solid ratio (L/S) on RTD. According to this study, increased screw speed resulted in a low mean residence time mean residence time and wider RTD, i.e. more axial mixing. Increasing powder feed rate increased mean residence time by higher throughput force while increasing L/S increased mean residence time by raising the sluggishness or inertia of the material in the barrel. The material transport in the mixing zone(s) of the TSG became more plug-flow like. Thus, an increase in the number of kneading discs reduced the axial mixing in the barrel. In addition, to understand the GSD dynamics as a function of individual screw modules along the TSG barrel, the change in GSD was investigated both experimentally and mathematically. Using a TSG which allows the opening of the barrel, samples from several locations inside the TSG barrel were collected after granulation at different process conditions and screw configurations. A detailed experimental investigation was hence performed to understand the granule size and shape dynamics in the granulator. The experimental data from this study together with the residence time measurements was then used for calibrating a population balance model for each kneading disc module in the twin-screw granulator in order to obtain an improved insight into the role of the kneading discs at certain locations inside the TSG. The study established that the kneading block in the screw configuration acts as a plug-flow zone inside the granulator. It was found that a balance between the throughput force and conveying rate is required to obtain a good axial mixing inside the twin-screw granulator. Also, a high throughput can be achieved by increasing the liquid-solid ratio and screw speed. Furthermore, the study indicated that the first kneading block after wetting caused an increased aggregation rate, which was reduced after the material processing by the second kneading block. In contrast, the breakage rate in the increased successively along the length of the granulator. Such a reversion in physical phenomena indicated potential separation between the granulation regimes, which can be promising for future design and advanced control of the continuous twin-screw granulation process. In another experimental study the transport and mixing (both axial and bulk mixing of solid-liquid) was linked to the GSD of the produced granules. This study demonstrated that insufficient solid-liquid mixing due to inability of the currently used kneading discs is the reason behind the inferior performance of the TSG in terms of yield. It was shown that other factors which support mixing such as higher axial mixing at a high screw speed and a low fill ratio support an increase in the yield. However, more effort is required to explore non-conventional screw elements with modified geometries to find screws which can effectively mix the solid-liquid material. Furthermore, in order to generalise the TSG knowledge, a regime map based approach was applied. Herewith, the scale independent parameters, L/S and specific mechanical energy (SME) were correlated. It was shown that an increasing L/S strongly drives the GSD towards a larger mean granule size. However, an increasing energy input to the system can effectively be used to lower the mean granule size and also narrow the width of the size distribution. Along with this, particle-scale simulations for the characterisation of liquid distribution in the mixing zone of the granulator were performed. It was found that the agglomeration is rather a delayed process which takes place by redistribution of liquid once the excess liquid on the particle surface is transferred to the liquid bridges. Moreover, the transfer of liquid from particle surface to liquid bridges, i.e. initialisation of agglomeration, is most dominant in the intermeshing region of the kneading discs. Besides the major outcomes of this work, i.e. building fundamental knowledge on pharmaceutical twin-screw wet granulation by combining experimental and theoretical approaches to diagnose the transport, mixing and constitutive mechanisms, several gaps and potential research needs were identified as well. As the regulators have opened up to increasingly rely on the science- and risk-based holistic development of pharmaceutical processes and products for commercialisation, the opportunity as well as responsibility lies with academic and industrial partners to develop a systematic framework and scientific approach to utilise this opportunity efficiently

Uncertainty and Interpretability Studies in Soft Computing with an Application to Complex Manufacturing Systems

In systems modelling and control theory, the benefits of applying neural networks have been extensively studied. Particularly in manufacturing processes, such as the prediction of mechanical properties of heat treated steels. However, modern industrial processes usually involve large amounts of data and a range of non-linear effects and interactions that might hinder their model interpretation. For example, in steel manufacturing the understanding of complex mechanisms that lead to the mechanical properties which are generated by the heat treatment process is vital. This knowledge is not available via numerical models, therefore an experienced metallurgist estimates the model parameters to obtain the required properties. This human knowledge and perception sometimes can be imprecise leading to a kind of cognitive uncertainty such as vagueness and ambiguity when making decisions. In system classification, this may be translated into a system deficiency - for example, small input changes in system attributes may result in a sudden and inappropriate change for class assignation. In order to address this issue, practitioners and researches have developed systems that are functional equivalent to fuzzy systems and neural networks. Such systems provide a morphology that mimics the human ability of reasoning via the qualitative aspects of fuzzy information rather by its quantitative analysis. Furthermore, these models are able to learn from data sets and to describe the associated interactions and non-linearities in the data. However, in a like-manner to neural networks, a neural fuzzy system may suffer from a lost of interpretability and transparency when making decisions. This is mainly due to the application of adaptive approaches for its parameter identification. Since the RBF-NN can be treated as a fuzzy inference engine, this thesis presents several methodologies that quantify different types of uncertainty and its influence on the model interpretability and transparency of the RBF-NN during its parameter identification. Particularly, three kind of uncertainty sources in relation to the RBF-NN are studied, namely: entropy, fuzziness and ambiguity. First, a methodology based on Granular Computing (GrC), neutrosophic sets and the RBF-NN is presented. The objective of this methodology is to quantify the hesitation produced during the granular compression at the low level of interpretability of the RBF-NN via the use of neutrosophic sets. This study also aims to enhance the disitnguishability and hence the transparency of the initial fuzzy partition. The effectiveness of the proposed methodology is tested against a real case study for the prediction of the properties of heat-treated steels. Secondly, a new Interval Type-2 Radial Basis Function Neural Network (IT2-RBF-NN) is introduced as a new modelling framework. The IT2-RBF-NN takes advantage of the functional equivalence between FLSs of type-1 and the RBF-NN so as to construct an Interval Type-2 Fuzzy Logic System (IT2-FLS) that is able to deal with linguistic uncertainty and perceptions in the RBF-NN rule base. This gave raise to different combinations when optimising the IT2-RBF-NN parameters. Finally, a twofold study for uncertainty assessment at the high-level of interpretability of the RBF-NN is provided. On the one hand, the first study proposes a new methodology to quantify the a) fuzziness and the b) ambiguity at each RU, and during the formation of the rule base via the use of neutrosophic sets theory. The aim of this methodology is to calculate the associated fuzziness of each rule and then the ambiguity related to each normalised consequence of the fuzzy rules that result from the overlapping and to the choice with one-to-many decisions respectively. On the other hand, a second study proposes a new methodology to quantify the entropy and the fuzziness that come out from the redundancy phenomenon during the parameter identification. To conclude this work, the experimental results obtained through the application of the proposed methodologies for modelling two well-known benchmark data sets and for the prediction of mechanical properties of heat-treated steels conducted to publication of three articles in two peer-reviewed journals and one international conference