Hybrid Mechanistic-Empirical Approach to the Modeling of Twin Screw Feeders for Continuous Tablet Manufacturing-Reference-Cited by-同舟云学术

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Hybrid Mechanistic-Empirical Approach to the Modeling of Twin Screw Feeders for Continuous Tablet Manufacturing

Published:2020-03-11 Issue:14 Volume:59 Page:6650-6661
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Bascone Davide¹^ORCID,Galvanin Federico²^ORCID,Shah Nilay¹,Garcia-Munoz Salvador³

Affiliation:

1. Centre for Process System Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom

2. Centre for Process System Engineering, Department of Chemical Engineering, University College London (UCL), London WC1E 6BT, United Kingdom

3. Eli Lilly and Company, Lilly Research Laboratories, Indianapolis, Indiana 46285, United States

Funder

Eli Lilly and Company

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.0c00420

Reference57 articles.

1. Design Space of Pharmaceutical Processes Using Data-Driven-Based Methods

2. An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process

3. Perspectives on the continuous manufacturing of powder-based pharmaceutical processes

4. Process analysis and optimization of continuous pharmaceutical manufacturing using flowsheet models

5. Predicting feeder performance based on material flow properties

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder;International Journal of Pharmaceutics: X;2024-06

2. Accelerated DEM simulation of the hopper-screw feeder and tablet-press feeder using the multi-level coarse-graining technique;Powder Technology;2024-01

3. Loss-in-Weight feeder performance prediction using Machine Learning;Computer Aided Chemical Engineering;2024

4. Predicting powder feedability: A workflow for assessing the risk of flow stagnation and defining the operating space for different powder-feeder combinations;International Journal of Pharmaceutics;2022-12

5. Linked experimental and modelling approaches for tablet property predictions;International Journal of Pharmaceutics;2022-10

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