A quasi-virtual online analyser based on an artificial neural networks and offline measurements to predict purities of raffinate/extract in simulated moving bed processes

Semi-continuous and continuous processes for enantiomeric separation

“…Several studies on the comparison of operation strategies for enantiomers separation by SMB chromatography have been carried out and demonstrated numerically. 263,[280][281][282][283][284] Including temperature gradient, 285 solvent gradient 265,[286][287][288] time variability 289 known as PowerFeed mode and modulation of the feed concentration during the switching cycles (ModiCon). 290 Also, different types of SMB apparatus were patented by Vroon et al 291 and Michel Anton.…”

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

“…In this way, the split of test and training data that gives the lowest error rate were successfully determined. We adopted MSE as a performance measurement technique in different studies based on ANN [ [113] , [114] , [115] , [116] , [117] ]. Then, the dataset is divided into training and test sets.…”

Recent advances in modelling and control of liquid chromatography

“…Simulated Moving Bed (SMB) is challenging to operate and accurate cycle-to-cycle adaptive control is needed, generally implemented via a parameter estimator and a controller [52]. Also of interest is the use of Artificial Neural Networks, for instance working simultaneously with an offline measurement system such as Quasi-Virtual Analyser (Q-VOA), for the separation of a bi-naphthol enantiomer mixture in a SMB process [61]. Other control strategies have also been applied for the control of continuous chromatographic processes, for instance, based on multi-objective optimisation to find optimal open-loop control parameters for the separation of human growth hormone (hGH) from its dimer [62].…”