Flowsheet modeling of the compression step of a continuous wet granulation production line

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A coupled 3D isogeometric and discrete element approach for modelling interactions between structures and granular matters

A three-dimensional (3D) isogeometric/discrete-element coupling method is presented for modelling contact/impact between structures and particles. This method takes advantages of the geometry smoothness and exactness of isogeometric analysis (IGA) for continuous solid media and the effectiveness and flexibility of the discrete element method (DEM) for particulate matters. The coupling procedure for handling interactions between IGA elements and discrete elements (DEs) includes global search, local search and interaction calculation. In the global search, the CGRID method is modified to detect potential contact pairs between IGA elements and DEs based on their bounding box representations. The strong convex hull property of a NURBS control mesh plays an important part in the bounding box representation of IGA elements. In the local search, the proposed approach treats each spherical DE centroid as a slave node and the contact surface of each IGA element as the master surface. The projection of a DE centroid onto an IGA element contact surface is solved by modifying the simplex method and Brent iterations. The contact force between an IGA element and a DE is determined from their penetration by using a (nonlinear) penalty function based method. The whole coupled system is solved by the explicit time integration within a updated Lagrangian scheme. Finally, three impact examples, including the impact of two symmetric bars, a tube onto a footing strip, and an assembly of granular particles to a tailor rolled blank, are simulated in elastic regime to assess the accuracy and applicability of the proposed method

Phenomenological and residence time distribution models for unit operations in a continuous pharmaceutical manufacturing process

Interest in continuous pharmaceutical manufacturing (CPM) technology is rapidly growing, with all major pharmaceutical companies developing products in their pipelines using this technology. As it has been extensively reported, CPM can deliver enormous advantages including faster product development, less material use, reduced capital cost due to small equipment size, superior process control, optimized performance, and more reliable quality manufacturing. Nevertheless, given the novel and complex nature of the technology, CPM systems require further study compared to traditional batch processes. CPM studies must be carefully designed, optimized, validated, and controlled as holistic system in order to operate robustly, efficiently, and provide the aforementioned advantages. To achieve CPM’s advantages in full, it is necessary to develop and implement a framework wherein the processes can be evaluated and studied as integrated systems. In this work, tools established in the process systems engineering (PSE) methodology were implemented to develop models that can aid CPM process design, evaluation, control, and optimization. The focus of this work included the development and implementation of computationally efficient phenomenological and residence time distribution models for systems in a CPM system. In the first two chapters of this work, a thorough review of the current implementation of models in the pharmaceutical industry is presented. Within the review, the different types of models currently implemented in the industry are enumerated followed by the challenges of their implementation. Among some of the most difficult challenges for modeling CPM powder-based systems is the ability to determine relationships between critical process inputs and outputs, and the ability capture the impact of material properties on the process. To overcome these challenges a framework for developing predictive phenomenological (i.e., engineering) models that include the effect of material properties on the process was developed. The third and fourth chapters of this work are devoted to describing the model development framework and provide an example case study of the methodology when it was successfully applied to a tablet compaction process. The successful integration of material property effects into the modeling of the pharmaceutical unit operation led to the development of a material property library that collected a wide array of property measurements for a number of pharmaceutically relevant materials. The material property library, described in the fifth chapter of this work, was used as a tool to determine the impact of material properties on: (1) residence time distribution experiments and (2) the operation of continuous powder feeding units. Residence time distribution (RTD) methods and models were studied in this work, as their application to characterize CPM systems has become standard. The effect of material properties on RTD methods were evaluated in the sixth chapter to provide recommendations for using the RTD methodology to characterize CPM units. Ultimately, the unit operation characterization and modeling framework presented in this work along with the recommendations offered for RTD experimentation and modeling were applied to the development of a dynamic phenomenological and RTD model for a continuous powder feeding unit. The model, described in the seventh chapter of this work, was used to predict the behavior of the CPM-specific unit over a wide range of material property and process inputs.Ph.D.Includes bibliographical referencesby Manuel Escotet Espinoz

Development and Use of a Residence Time Distribution (RTD) Model Control Strategy for a Continuous Manufacturing Drug Product Pharmaceutical Process

Residence-time-distribution (RTD)-based models are key to understanding the mixing dynamics of continuous manufacturing systems. Such models can allow for material traceability throughout the process and can provide the ability for removal of non-conforming material from the finished product. These models have been implemented in continuous pharmaceutical manufacturing mainly for monitoring purposes, not as an integral part of the control strategy and in-process specifications. This paper discusses the steps taken to develop an RTD model design space and how the model was statistically incorporated into the product’s control strategy. To develop the model, experiments were conducted at a range of blender impeller speeds and total system mass flow rates. RTD parameters were optimized for each condition tested using a tank-in-series-type model with a delay. Using the experimental RTD parameters, an equation was derived relating the mean residence time to the operating conditions (i.e., blender impeller speed and mass flow rate). The RTD parameters were used in combination with real-time upstream process data to predict downstream API concentration, where these predictions allowed validation across the entire operating range of the process by comparison to measured tablet assay. The standard in-process control limits for the product were statistically tightened using the validation acceptance criteria. Ultimately, this model and strategy were accepted by regulatory authorities