AIMC Topic: Chromatography
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A parameter estimation method for chromatographic separation process based on physics-informed neural network.
Journal of chromatography. A
Jun 12, 2024
Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computat...
Physics-informed neural networks to solve lumped kinetic model for chromatography process.
Journal of chromatography. A
Sep 9, 2023
Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neura...
Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity.
Critical reviews in analytical chemistry
Sep 6, 2023
Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed c...
Using artificial neural networks to accelerate flowsheet optimization for downstream process development.
Biotechnology and bioengineering
May 31, 2023
An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches ...
Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration.
Biotechnology and bioengineering
Apr 22, 2023
Chromatographic data processing has garnered attention due to multiple Food and Drug Administration 483 citations and warning letters, highlighting the need for a robust technological solution. The healthcare industry has the potential to greatly ben...
Retention time prediction for small samples based on integrating molecular representations and adaptive network.
Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Feb 4, 2023
Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data...
Editorial: special issue machine learning and other tools for data handling in chromatography.
Journal of chromatography. A
Oct 18, 2022
Can a computer "learn" nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes.
Journal of chromatography. A
Apr 9, 2022
The design and optimization of chromatographic processes is essential for enabling efficient separations. To this end, hyperbolic partial differential equations (PDEs) along with nonlinear adsorption isotherms must be solved using computationally exp...
Smart process development: Application of machine-learning and integrated process modeling for inclusion body purification processes.
Biotechnology progress
Mar 16, 2022
The development of a biopharmaceutical production process usually occurs sequentially, and tedious optimization of each individual unit operation is very time-consuming. Here, the conditions established as optimal for one-step serve as input for the ...
Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks.
Journal of chromatography. A
Nov 5, 2021
Screening of physicochemical properties should be considered one of the essential steps in the drug discovery pipeline. Among the available methods, biomimetic chromatography with an immobilized artificial membrane is a powerful tool for simulating i...
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