Applications of RFID in the pharmaceutical supply chain

L’obiettivo di questa tesi è principalmente quello di mostrare le possibili applicazioni della tecnologia RFID (Radio Frequency Identification) all’interno della supply chain farmaceutica, cercando di comprendere il campo di applicazione di questa tecnologia in un’industria così altamente regolata e complessa come quella farmaceutica. La tesi è organizzata in tre sezioni; nel primo capitolo si trattano i principi alla base della tecnologia RFID e l’attuale stato dell’arte nella standardizzazione della banda di frequenza utilizzata e nell’hardware. In questa sezione sono poi mostrate le principali applicazioni e nella parte finale i problemi affrontati da un’azienda nell’applicare l’RFID al proprio business. Il secondo capitolo descrive le parti coinvolte nell’industria farmaceutica, dal produttore al consumatore finale, poi esamina le caratteristiche essenziali della supply chain farmaceutica e gli aspetti chiave e le criticità da affrontare in questo campo per essere efficiente e per consegnare il prodotto al cliente in maniera sicura e consistente. Infine nell’ultima sezione i due argomenti centrali sono fusi assieme, cercando di esaminare come la tecnologia RFID sia in grado di risolvere i problemi affrontati da tale industria. Il lavoro non vuole mostrare la tecnologia RFID come una panacea di tutte le problematiche presentate in questa industria, ma vuole cercare di colmare il gap presente in letteratura riguardo le possibili applicazioni di questa tecnologia nell’industria specificata.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Characterization Of Single Use Reactor Used For The Manufacturing Of Formulated Products Within The Pharmaceutical Industry

Mixing is a fundamental unit operation in the pharmaceutical industry to ensure consistent product quality across different batches. It is usually carried out in mechanically stirred tanks, with a large variety of designs according to the process requirements. A key aspect of pharmaceutical manufacturing is the extensive and meticulous cleaning of the vessels between runs to prevent the risk of contamination. Single-use reactors represent an increasing trend in the industry since they do not require cleaning and sterilization, reducing the need for utilities such as steam to sterilize equipment and the time between production batches. In contrast to traditional stainless steel vessels, single-use reactors consist of a plastic bag used as a vessel and disposed of after use. This thesis aims to characterize the fluid dynamics features and the mixing performance of a commercially available single-use reactor. The characterization employs a combination of various experimental techniques. The analysis starts with the visual observation of the liquid behavior inside the vessel, focusing on the vortex shape evolution at different impeller speeds. The power consumption is then measured using a torque meter to quantify the power number. Particle Image Velocimetry (PIV) is employed to investigate local fluid dynamics properties such as mean flow field and mean and rms velocity profiles. The same experimental setup of PIV is exploited for another optical measurement technique, the Planar Laser-Induced Fluorescence (PLIF). The PLIF measurements complete the characterization of the reactor with the qualitative visualization of the turbulent flow and the quantitative assessment of the system performance through the mixing time. The results confirm good mixing performances for the single-use reactor over the investigated impeller speeds and reveal that the filling volume plays a significant role in the fluid dynamics of the system.