| Polynomial regression |
The effects of operational parameters, such as impeller speed, dosing speed, chopper speed and wet massing time, on granule size were quantified |
57
|
| The effects of process parameters, including granulation time, impeller, and formulation variables, on packing coefficient and strength of granules were investigated |
58
|
| The relationship between granulation variables and the specific energy of the granules was determined |
59
|
| The best-fit equation was used to accurately predict the Carr's index for granules under different formulation factors |
60
|
| Polynomial, MLR |
The impact of formulation variables on granule properties like flowability and size was assessed, which was beneficial for selecting the desired formulation |
61
|
| Combined with DoE, models which correlated the process parameters with granule properties, were developed. This provided the basis for adjusting process parameters according to the product quality attributes |
62
|
| Using DoE techniques, the effects of amount of water and massing time on the key quality attributes of granules were investigated |
63
|
| PLS |
The relationship between impeller speed and total power spectral densities (TPSDs) was developed. The research demonstrated that audible acoustic emissions could monitor process changes in real time |
64
|
| Gene expression programing model |
Impeller power can be predicted according to the impeller diameter, impeller speed, the percentage of the liquid and mean torque |
65
|
| PCA, MLR |
The relationship between process variables on granule hardness and Carr's index was developed. Based on the PCA model, it was shown that there was a strong correlation between the impeller speed and wet massing time with the granule attributes |
66
|
| Polynomial, MLR, PLS, ANNs |
Based on various MVA models, the relationship between three process parameters and CQAs of granules such as mean size and flowability was quantified |
67
|
| PLS, MBPLS, OPLS |
Various MVA models were developed to investigate the effects of HSWG process variables and granule properties on tablet quality |
68
|
