Publicação
Modelling granules size distribution produced on a continuous manufacturating line with non-linear autoregressive artificial neural networks
| Resumo: | Particle size is a critical quality parameter in several pharmaceutical unit operations. An adequate particle size distribution is essential to ensure optimal manufacturability which, in turn, has an important impact on the safety, efficacy and quality of the end product. Thus, the monitoring and control of the particle size via in-process size measurements is crucial to the pharmaceutical industry. Currently, a wide range of techniques are available for the determination of particle size distribution, however a technique that enables relevant real-time process data is highly preferable, as a better understanding and control over the process is offered. The pharmaceutical industry follows the “technology-push model” as it depends on scientific and technological advances. Hence, optimization of product monitoring technologies for drug products have been receiving more attention as it helps to increase profitability. An increasing interest in the usage of virtual instruments as an alternative to physical instruments has arisen in recent years. A software sensor utilizes information collected from a process operation to estimate values of some property of interest, typically difficult to measure experimentally. One of the most significant benefits of the computational approach is the possibility to adapt the measuring system through several optimization solutions. The present thesis focuses on the development of a mathematical dynamic model capable of predicting particle size distribution in-real time. For this purpose, multivariate data coming from univariate sensors placed in multiple locations of the continuous production line, ConsiGmaTM-25, was utilized to determine the size distribution (d50) of granules evaluated at a specific site within the line. The ConsiGmaTM-25 system is a continuous granulation line developed by GEA Pharma. It consists of three modules: a continuous twin-screw granulation module, a six-segmented cell fluid bed dryer and a product control unit. In the continuous granulation module, granules are produced inside the twin-screw granulator via mixing of the powder and the granulation liquid (water) fed into the granulation barrel. Once finalized the granulation operation, the produced granules are then pneumatically transferred to the fluid bed dryer module. In the dryer module, the granules are relocated to one specific dryer cell, where drying is performed for a pre-defined period of time. The dry granules are formerly transported to the product control hopper with an integrated mill situated in the product control unit. The granules are milled, and the resulting product is gravitationally discharged and can undergo further processing steps, such as blending, tableting and coating. The size distribution (d50) of the granules to be determined in this work were assessed inside dryer cell no.4, located at the dryer module. The size distribution was measured every ten seconds by a focused beam reflectance measurement technique. A non-linear autoregressive with exogenous inputs network was developed to achieve accurate predictions of granules size distribution values. The development of the predictive model consisted of the implementation of an optimization strategy in terms of topology, inputs, delays and training methodology. The network was trained against the d50 obtained from particle size distribution collected in-situ by the focused beam reflectance measurement technique mentioned above. The model presented the ability to predict the d50 value from the beginning to the end of the several drying cycles. The accuracy of the artificial neural network was determined by a root mean squared error of prediction of 6.9%, which demonstrated the capability to produce close results to the experimental data of the cycles/runs included on the testing set. The predictive ability of the neural network, however, could not be extended to drying cycle that presented irregular fluctuations. Due to the importance of the precise monitoring of the size distribution within pharmaceutical operations, a future adjustment of the optimization strategy is of great interest. In the future, a higher number of experimental runs/cycles can be used during the training process to enable the network to identify and predict more easily atypical cases. In addition, a more realistic optimization strategy could be performed for all process parameters in simultaneous through the implementation of a genetic algorithm, for example. Changes in terms of network topology can also be considered. |
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| Autores principais: | Nunes, Diana Catherina Manaig |
| Assunto: | Particle size distribuition Granules Drying Monitoring Artificial neural network Teses de mestrado - 2018 |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | Particle size is a critical quality parameter in several pharmaceutical unit operations. An adequate particle size distribution is essential to ensure optimal manufacturability which, in turn, has an important impact on the safety, efficacy and quality of the end product. Thus, the monitoring and control of the particle size via in-process size measurements is crucial to the pharmaceutical industry. Currently, a wide range of techniques are available for the determination of particle size distribution, however a technique that enables relevant real-time process data is highly preferable, as a better understanding and control over the process is offered. The pharmaceutical industry follows the “technology-push model” as it depends on scientific and technological advances. Hence, optimization of product monitoring technologies for drug products have been receiving more attention as it helps to increase profitability. An increasing interest in the usage of virtual instruments as an alternative to physical instruments has arisen in recent years. A software sensor utilizes information collected from a process operation to estimate values of some property of interest, typically difficult to measure experimentally. One of the most significant benefits of the computational approach is the possibility to adapt the measuring system through several optimization solutions. The present thesis focuses on the development of a mathematical dynamic model capable of predicting particle size distribution in-real time. For this purpose, multivariate data coming from univariate sensors placed in multiple locations of the continuous production line, ConsiGmaTM-25, was utilized to determine the size distribution (d50) of granules evaluated at a specific site within the line. The ConsiGmaTM-25 system is a continuous granulation line developed by GEA Pharma. It consists of three modules: a continuous twin-screw granulation module, a six-segmented cell fluid bed dryer and a product control unit. In the continuous granulation module, granules are produced inside the twin-screw granulator via mixing of the powder and the granulation liquid (water) fed into the granulation barrel. Once finalized the granulation operation, the produced granules are then pneumatically transferred to the fluid bed dryer module. In the dryer module, the granules are relocated to one specific dryer cell, where drying is performed for a pre-defined period of time. The dry granules are formerly transported to the product control hopper with an integrated mill situated in the product control unit. The granules are milled, and the resulting product is gravitationally discharged and can undergo further processing steps, such as blending, tableting and coating. The size distribution (d50) of the granules to be determined in this work were assessed inside dryer cell no.4, located at the dryer module. The size distribution was measured every ten seconds by a focused beam reflectance measurement technique. A non-linear autoregressive with exogenous inputs network was developed to achieve accurate predictions of granules size distribution values. The development of the predictive model consisted of the implementation of an optimization strategy in terms of topology, inputs, delays and training methodology. The network was trained against the d50 obtained from particle size distribution collected in-situ by the focused beam reflectance measurement technique mentioned above. The model presented the ability to predict the d50 value from the beginning to the end of the several drying cycles. The accuracy of the artificial neural network was determined by a root mean squared error of prediction of 6.9%, which demonstrated the capability to produce close results to the experimental data of the cycles/runs included on the testing set. The predictive ability of the neural network, however, could not be extended to drying cycle that presented irregular fluctuations. Due to the importance of the precise monitoring of the size distribution within pharmaceutical operations, a future adjustment of the optimization strategy is of great interest. In the future, a higher number of experimental runs/cycles can be used during the training process to enable the network to identify and predict more easily atypical cases. In addition, a more realistic optimization strategy could be performed for all process parameters in simultaneous through the implementation of a genetic algorithm, for example. Changes in terms of network topology can also be considered. |
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