Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442643
Title: Roll compaction of pharmaceutical excipients and prediction using intelligent software
Author: Mansa, Rachel Fran
ISNI:       0000 0001 3618 3484
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2007
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Abstract:
Roll compaction is a dry granulation method. In the pharmaceutical industry it assists in binding tablet ingredients together to form a larger mass. This is conducted to ease subsequent processing, decrease dust, improve flowability, improve material distribution, more suitable for moisture and heat sensitive materials than wet granulation methods, minimises operating space and suited for a continuous manufacturing set-up. In pharmaceutical roll compaction various types of powder material mixtures are compacted into ribbon that are subsequently milled and tableted. The aim of this research is to investigate the use of intelligent software (FormRules and INForm software) for predicting the effects of the roll compaction process and formulation characteristics on final ribbon quality. Firstly, the tablet formulations were characterised in terms of their particle size distribution, densities, compressibility, compactibility, effective angle of friction and angle of wall friction. These tablet formulations were then roll compacted. The tablet formulation characteristics and roll compaction results formed 64 datasets, which were then used in FormRules and INForm software training. FormRules software highlighted the key input variables (i.e. tablet formulations, characteristics and roll compaction process parameters). Next these key input variables were used as input variables in the model development training of INForm. The INForm software produced models which were successful in predicting experimental results. The predicted nip angle values of the INForm models were found to be within 5%, which was more accurate to those derived from Johanson’s model prediction. The Johanson’s model was not successful in predicting nip angle above the roll speed of 1 rpm due to air entrainment. It also over-predicted the experimental nip angle of DCPA and MCC by 200%, while the approximation using Johanson’s pressure profile under-predicted the experimental nip angle of DCPA by 5-20% and MCC by 20%.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.442643  DOI: Not available
Keywords: TP Chemical technology
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