Optimisation and control of chromatography. Sebastian Engell Abdelaziz Toumi Laboratory of Process Control Biochemical and Chemical Engineering Department Universität Dortmund. Contents. Introduction Preparative chromatography S imulated M oving B ed technology Reactive chromatography
Abdelaziz Toumi Laboratory of Process ControlBiochemical and Chemical Engineering Department
intensification by gradient elution
expensive in large scale
highly diluted products
= Chromatography for production, not analytical chemistry
True Moving Bed (TMB)
Practical implementation as a
simulated moving bed process:
Universal Oil Products (USA), US Patent (Brougthon und Gerhold 1961), 120 units sold (Sarex, Molex , Parex etc..)
Institut Francais du Pétrole(France), largest SMB-Plant in the world implemented in South Korea (Eluxyl)
Amalgamated Sugar Co. (USA) operates SMB-plants with a total capacity of 24.500 tonn HFCS (2001)
Cultor Corporation (Finland) patented new operating modes which includes ,,Sequential-’’ and ,,Multistage’’ SMB (FAST)
Appelxion has installed more than 90 ,,Improved’’ SMB-Plants, 3 of them in Europe (in Spain for the production of Pinitol)
….Industrial applications of SMB I
800 Millimeters SMB-PlantAerojet Fine Chemicals (Sacramento, USA)
Fraction of installed units
In-situ adsorption drives the reaction beyond the equilibrium.
Conversion of badly separable components
Loss of degrees of freedom and flexibility
Complex dynamics, narrow range of operationReactive chromatography
feedRSMB for glucose isomerisation (Fricke and Schmidt-Traub)
Online optimisation: nested approach (Dünnebier & Klatt)
General Rate Model
Numerical Scheme by Gu
Parabolic pde system
Specifications of the experiment:
Disadvantage of the purity control scheme:
Optimality is lost!
Measurement-based online optimisation
Redesigned ISOPE algorithm
Combines the measurement information and the model to construct a modified optimisation problem.
Iteratively converging to the real optimum although model mismatch exists.
Can handle constraints with model mismatch.
Gao & Engell: Measurement-based online optimisation with model-mismatch, ESCAPE 14.
Purity specification: 98%
Recovery limit: 80%
Flow rate: ≤ 0.42 cm/s
Production rate surfaces:
Node Model (change in flow rates and concentration inputs)
Synchronuous switching (new initialization of the state)
Continuous chromatographic model (General Rate Model)
Numerical approach (Gu, 1995, Toumi)
simulation until cyclic steady state is reached
cyclic steady state is included as an additional constraint
MUSCOD-II (Bock et. al.)DFG project (EN 152/34-1)Model-based Optimisation I
cyclic steady state
SMBOpt (Toumi et. al.)
VARICOL is more efficient than SMB
VARICOL result gives clue for the choice of the distribution of the columns over the zones.