Real time steady-state data reconciliation and gross error detection in continuous pharmaceutical manufacturing

The area of particulate processing is mainly a batch operated system. The area of pharmaceuticals that deal with particulate processes is in the process of switching to a continuous operation. The continuous operation of particulates has not been widely researched especially in the area of process systems engineering. One area of process systems engineering is that of control and optimization of processes. In order to efficiently control and/or optimize a process, accurate measurements for the current state of the process is required. Data reconciliation and gross error detection are two techniques that help to improve the quality of the process measurements. Methods of data reconciliation and gross error detection have not yet been explored in the area of continuous particulate manufacturing. Therefore, the context of this research is focused on the implementation of data reconciliation and gross error detection in the continuous particulate processing industry - where previous there has been a lack of research. In this research, we will explore the use of data reconciliation and gross error detection on a continuous particulate processing system - specifically the continuous manufacturing of tablets. A tool will be constructed to reconcile the data provided from the process, along with tools to detect gross errors within the system. We will also develop some process constraint models of the particulate system for the use in data reconciliation and gross error detection

Contact between swabs and surfaces during explosives detection

The ability to remove residual explosives from surfaces is crucial to detecting the presence of explosives in a variety of settings. Trace amounts adhere to the hands and equipment of those handling explosives and are subsequently transferred to clothing, parcels, vehicles, or other surfaces. Improved methods to remove detectable amounts of these explosives from surfaces will allow more effective detection of explosives in a range of environments. Current collection methods can be classified as either contact or non-contact. Contact sampling dislodges explosive particles through physical contact with a swab and collects particles that adhere more strongly to the swab than to the original surface. Non-contact sampling relies on momentum transfer between a moving fluid (typically air) and the explosive residue to dislodge the particles from the surface. This research focuses on contact sampling between an explosives detection swab and a variety of surfaces often encountered by transportation security. The adhesion forces between selected explosives and surfaces are first measured to determine the explosives\u27 adhesion ability. Then the contact between selected swabs and surfaces is simulated and analyzed to determine the percentage of the surface in which a particle of an approximate size could remain undetected when the swab is placed against the surface. This value can be used as a first-generation ranking tool to describe the swab\u27s ability to interrogate the surface