Real time steady-state data reconciliation and gross error detection in continuous pharmaceutical manufacturing
Abstract
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.
Degree
M.S.Ch.E.
Advisors
Reklaitis, Purdue University.
Subject Area
Chemical engineering|Pharmacy sciences
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