Is hydroxypyridonate 3,4,3-LI(1,2-HOPO) a good competitor of fetuin for uranyl metabolism?

Metallomics ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 496-507 ◽  
Author(s):  
Ali Younes ◽  
Gaëlle Creff ◽  
Maria Rosa Beccia ◽  
Philippe Moisy ◽  
Jérôme Roques ◽  
...  
Keyword(s):  
Chromatographic Separation ◽  
Ternary Complexes ◽  
Separation Process

Identification of stable HOPO–UO22+–fetuin ternary complexes after a chromatographic separation process.

scholarly journals A comparison of the parameters of the chromatographic separation of the mixture of dyes obtained by caLC on the adsorbents type RP-8 and RP-18

2017 ◽  
Vol 35 (7-8) ◽  
pp. 684-691
Author(s):  
Andrzej Gierak ◽  
Agata Skorupa
Keyword(s):  
Acetic Acid ◽  
Liquid Chromatography ◽  
Chromatographic Separation ◽  
Separation Process ◽  
Retention Data ◽  
Capillary Action ◽  
Food Dyes ◽  
Mode Of Development

The aim of the presented research was to compare the chromatographic separation process, a mixture of food dyes obtained in capillary action liquid chromatography (caLC) columns packed with adsorbent type RP-8 and RP-18 and using as the eluent: methanol-water-acetic acid (4:5:1, v/v). The obtained retention data were compared with separation in caLC columns with different internal diameters, different distance of migration and the different mode of development the chromatograms.

Dynamic Optimization of a Simulated Moving Bed (SMB) Chromatographic Separation Process

2006 ◽  
Vol 45 (26) ◽  
pp. 9033-9041 ◽  
Author(s):  
María-Sonia G. García ◽  
Eva Balsa-Canto ◽  
Julio R. Banga ◽  
Alain Vande Wouwer
Keyword(s):  
Dynamic Optimization ◽  
Chromatographic Separation ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed

scholarly journals Soft Sensing Modeling of the SMB Chromatographic Separation Process Based on the Adaptive Neural Fuzzy Inference System

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Dan Wang ◽  
Jie-Sheng Wang ◽  
Shao-Yan Wang ◽  
Shou-Jiang Li ◽  
Zhen Yan ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Chromatographic Separation ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Soft Sensor ◽  
Separation Process ◽  
Inference System ◽  
Separation Technology ◽  
Sensor Models ◽  
Neural Fuzzy

Simulated moving bed (SMB) chromatographic separation technology is a new adsorption separation technology with strong separation ability. Based on the principle of the adaptive neural fuzzy inference system (ANFIS), a soft sensing modeling method was proposed for realizing the prediction of the purity of the extract and raffinate components in the SMB chromatographic separation process. The input data space of the established soft sensor model is divided, and the premise parameters are determined by utilizing the meshing partition method, subtractive clustering algorithm, and fuzzy C-means (FCM) clustering algorithm. The gradient, Kalman, Kaczmarz, and PseudoInv algorithms were used to optimize the conclusion parameters of ANFIS soft sensor models so as to predict the purity of the extract and raffinate components in the SMB chromatographic separation process. The simulation results indicate that the proposed ANFIS soft sensor models can effectively predict the key economic and technical indicators of the SMB chromatographic separation process.

Digitized Simulation of the Moving Bed

2020 ◽  
Vol 987 ◽  
pp. 157-161
Author(s):  
Chao Fan Xie ◽  
Rey Chue Huang ◽  
Lin Xu ◽  
Fu Quan Zhang ◽  
Lu Xiong Xu
Keyword(s):  
Stationary Phase ◽  
Chromatographic Separation ◽  
Manufacturing Process ◽  
Theoretical Basis ◽  
Mobile Phase ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed ◽  
Simulated Moving Bed Chromatography ◽  
Chemical Products

Chromatographic separation is an indispensable and important technology in the manufacturing process of chemical products and biomedicine. It uses the distribution differences of a compound in the stationary phase and mobile phase to achieve the separation of the mixture. It is of great value to study the separation process of substances by simulated moving bed chromatography. By digitally simulating the process of moving bed, we can observe the influence of parameter changes on substance analysis by chromatography, and then find out the law of substance separation, which can provide theoretical basis for scientific research of biopharmaceuticals.

scholarly journals Model Predictive Control Method of Simulated Moving Bed Chromatographic Separation Process Based on Subspace System Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Zhen Yan ◽  
Jie-Sheng Wang ◽  
Shao-Yan Wang ◽  
Shou-Jiang Li ◽  
Dan Wang ◽  
...  
Keyword(s):  
System Identification ◽  
State Space ◽  
Predictive Control ◽  
Chromatographic Separation ◽  
Control Method ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed ◽  
Separation Technology ◽  
Subspace System Identification

Simulated moving bed (SMB) chromatographic separation is a new type of separation technology based on traditional fixed bed adsorption operation and true moving bed (TMB) chromatographic separation technology, which includes inlet-outlet liquid, liquid circulation, and feed liquid separation. The input-output data matrices were constructed based on SMB chromatographic separation process data. The SMB chromatographic separation process was modeled by utilizing two subspace system identification algorithms: multivariable output-error state-space (MOESP) identification algorithm and numerical algorithms for subspace state-space system identification (N4SID), so as to obtain the 3rd-order and 4th-order state-space yield models of the SMB chromatographic separation process, respectively. The model predictive control method based on the established state-space models is used in the SMB chromatographic separation process. The influence of different control indicators on the predictive control system response performance is discussed. The output response curves of the yield models were obtained by changing the related parameters so that the yield model parameters are optimized set meanwhile. Finally, the simulation results showed that the yield models are successfully controlled based on the each control period and given yield range.

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