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The MCSGP process consists of several, at least two, chromatographic columns which are switched in position opposite to the flow direction. Most of the columns are equipped with a gradient pump to adjust the modifier concentration at the column inlet. Some columns are connected directly, so that non pure product streams are internally recycled.
The introduction of gradient pumps resulted in quicker separations and less solvent usage. In expanded bed adsorption , a fluidized bed is used, rather than a solid phase made by a packed bed. This allows omission of initial clearing steps such as centrifugation and filtration, for culture broths or slurries of broken cells.
Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate function.
A sample is injected into a flowing carrier solution stream that is forced by a peristaltic pump. The injection of the sample is done under controlled dispersion in known volumes. The carrier solution and sample then meet at mixing points with reagents and react. The reaction time is controlled by a pump and reaction coil.
In manufacturing, the simulated moving bed (SMB) process is a highly engineered process for implementing chromatographic separation. It is used to separate one chemical compound or one class of chemical compounds from one or more other chemical compounds to provide significant quantities of the purified or enriched material at a lower cost than could be obtained using simple (batch ...
SEQSU – sequential survey; SF – Self Flowing; SFERAE – global association for the use of knowledge on fractured rock in a state of stress, in the field of energy, culture and environment [26] SFL – steel flying lead; SG – static gradient, specific gravity; SGR – shale gouge ratio; SGS – steel gravity structure
SGLD can be applied to the optimization of non-convex objective functions, shown here to be a sum of Gaussians. Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins–Monro optimization algorithm, and Langevin dynamics, a mathematical extension of molecular dynamics models.
Stochastic gradient descent competes with the L-BFGS algorithm, [citation needed] which is also widely used. Stochastic gradient descent has been used since at least 1960 for training linear regression models, originally under the name ADALINE. [25] Another stochastic gradient descent algorithm is the least mean squares (LMS) adaptive filter.