H2020Индивидуална стипендия2019–2021

MODSIM · Modelling of Twin Screw Granulation on Micro-Macro Scales

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2019-05-01 → 2021-04-30
Финансиране от ЕС: 184 591 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Математически модели анализират как се образуват гранули в двушпилков гранулатор, като следят времето за престой на праха и скоростта на шпицките. Това помага за по-прецизен контрол върху качеството на фармацевтичните продукти и намалява разхиляването на суровини.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Modelling of Twin Screw Granulation on Micro-Macro Scales

Objective 1 was to develop a method for PBM that is computationally efficient and compute the results with higher precision. This allows the researcher to have an advanced prediction of how the system behaves when the process parameters are varied, hence further leads to less powder wastage which was the main target of the project. The mathematical model developed for continuous manufacturing unit (twin-screw granulator) using population balance model (PBM) approach by incorporating the process parameters in PBM. For solving these complex models, an accurate and efficient numerical technique is required. Therefore, we developed two finite volume methods to solve a simultaneous aggregation-breakage PBM. The second objective was to extract the mean residence time (MRT) so that during the granulation we have more control over the process and desired quality granules can be manufactured. This highly depends on the knowledge of MRT which describes the stay of powder in a specific part of the screw. Different properties granules can be prepared by correlating the MRT with process parameters such as liquid to solid ratio (L/S), powder feed rate, and screw speed. This gives a better understanding of the behavior of granules prepared during the granulation process. In addition, the most important parameter while using the wet TSG is the MRT of powder inside the barrel. Process parameters including feed flow rate, screw speed, and L/S are correlated with the obtained values of MRT to build a predictive tool. Artificial neural network (ANN) modeling is implemented to predict the MRT of pharmaceutical formulation in a wet TSG. Next, we develop a model that has the ability to predict mechanistically the behaviour of particles inside the TSG. Experimental data is collected for Microcrystalline cellulose (MCC-101, Avicel pH 101) was granulated with water in a TSG. A five compartmental population balance model (CPBM) is developed and 10 parameters related to aggregation and breakage PBM is optimized. In addition, kriging interpolation is used to interpolate for new values of empirical parameters at different L/S and screw speeds. This model has the tendency to extract new data and further assists in reducing the waste of the powder in the pharmaceutical industry. Five CPBM is developed for the TSG. Moreover, Kriging interpolation is used to interpolate for new values of empirical parameters at different L/S and screw speeds. Finally, the CPBM model is calibrated and validated using the experimental data.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Pharmaceutical manufacturing companies use various powders to prepare granules/tablets with stable attributes. However, wastage costs in the manufacture of solid dose drugs are of the order $50 billion. Much of this is due to a lack of fundamental knowledge of the physical processes occurring inside the particulate processes. In this project, common manufacturing systems such as are twin screw granulator (TSG) to produce suitable granules/formulations will be studied. A fully mechanistic understanding of these processes will be undertaken with the objective of reducing the manufacturing wastage and handling cost of the TSG using the skills and knowledge of the experienced researcher (ER) and the host supervisor. Improving this efficiency will play a key role in ensuring a more sustainable production system through less wastage into the future, a societal challenge which will be met by the H2020 programme.MODSIM is a multidisciplinary project aimed at exploiting my background in mathematical modelling of particulate processes using population balances in an area of pharmaceutical solid drug manufacturing. Apart from its manufacturing potential, this research will broaden current understanding of micro scale processes inside the TSG using MC method. The project forms a collaboration between the world leading Bernal Institute for Process Engineering research laboratories and an SME and exploits the multidisciplinary nature of their studies. The resulting knowledge will help EU manufacturing enterprises, in particular SMEs, to adapt to global competitive pressures by improving the technological base of EU manufacturing from pharma and importantly potential translation into high-value bioprocessing and med-tech sectors. The proposed research will provide outstanding research-led training and falls within the H2020 excellence science remit.

Оригинален текст от CORDIS (на английски).

Участници

UNIVERSITY OF LIMERICK · LimerickКоординаторИрландия