Modeling and simulation of continuous powder blending applied to a continuous direct compression process. (26th November 2018)

• Record Type: Journal Article
• Authors: Galbraith, Shaun C.; Liu, Huolong; Cha, Bumjoon; Park, Seo-Young; Huang, Zhuangrong; Yoon, Seongkyu
• Is Part Of: Pharmaceutical development and technology. Volume 23:Number 10(2018)
• Page Start: 1097
• Page End: 1107
• Publication Date: 2018-11-26
• Subjects: Continuous manufacturing -- powder blending -- residence time distribution -- blend uniformity -- flowsheet modeling; Drug delivery systems -- Periodicals; Pharmaceutical technology -- Periodicals; Drugs -- Administration -- Research -- Periodicals; Drug Delivery Systems -- Periodicals; Pharmaceutical Preparations -- Periodicals; Technology, Pharmaceutical -- Periodicals; 615
• DOI: 10.1080/10837450.2018.1425429
• Languages: English
• ISSNs: 1083-7450

Abstract

Abstract: Continuous manufacturing techniques are increasingly being adopted in the pharmaceutical industry and powder blending is a key operation for solid-dosage tablets. A modeling methodology involving axial and radial tanks-in-series flowsheet models is developed to describe the residence time distribution (RTD) and blend uniformity of a commercial powder blending system. Process data for a six-component formulation processed in a continuous direct compression line (GEA Pharma Systems) is used to test the methodology. Impulse tests were used to generate experimental RTDs which are used along with parameter estimation to determine the number of axial tanks in the flowsheet. The weighted residual from the parameter estimation was less than the χ 2 value at a 95% confidence indicating a good fit between the model and measured data. In-silico impulse tests showed the tanks-in-series modeling methodology could successfully describe the RTD behavior of the blenders along with blend uniformity through the use of radial tanks. The simulation output for both impulse weight percentage and blend uniformity were within the experimentally observed variance.