In order to enhance process understanding and to develop predictive process models in high shear granulation, there is an ongoing search for simulation tools and experimental methods to model and measure the velocity and shear fields in the mixer.
The results pave the way for improved process models for high-throughput manufacturing processes such as Automated Tape Placement.
Initially, the analytic process model required a high number of variant simulations.
These drawbacks are among others the lack of direct measurement of the key variables, the impossibility of setting a desired equilibrium point without a perfect process model, and the high laboratory effort for the preparation and analysis of the experiments.
Model-based analysis, design and testing methods require precise mathematical process models of high quality.
The experimental results reported here, underscore the importance of substrate-scale uniformity measurements in developing mechanistic ALD process models with high predictability of the dynamic evolution of the spatially dependent film thickness profile.
Based on the model, ascending processes of a high altitude balloon under different initial launch conditions were simulated.
In this research paper, a new integrated network based on Artificial Intelligence (AI) is proposed to model a high shear granulation (HSG) process.
This study aims to investigate the variable selection process in a prognostic model for high risk patients using merged data from three different studies [ 1- 3].
In order to better understand the factors involved in this process, a whole animal model with high throughput screening possibilities and markers for studying the host response to S. epidermidis infection are required.
