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The study for optimization of chromatographic condition by means of artificial neural networks
1Department of Chemistry, Hangzhou University, Hangzhou, 310028, People's Republic of China.
Talanta
|October 31, 2008
Abstract:
In this paper, the optimization of gas chromatographic experimental parameters is investigated using a three layer feed-forward neural network with the back-propagating. The design, development, and testing of the neural network are described in detail. The chosen structure is 4-6-2 system with a learning rate eta of 0.6 and a momentum constant mu of 0.4. The results of several simulations are very satisfactory. Network results are compared with the results obtained by the orthogonal method.
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01:15Optimizing Chromatographic Separations
Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
01:12Chromatographic Methods: Classification
Chromatographic techniques are classified in three ways: the classification is based on the physical state of the stationary and mobile phases, how the mobile phase and the stationary phase contact each other, or through the chemical or physical processes that isolate the components of the sample. Typically, the mobile phase is either a liquid or gas, while the stationary phase is either a solid or a liquid layer applied to a solid surface.
Chromatographic techniques are typically named by...
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