Datensatz

Zusammenfassung

The simulated moving bed process (SMB) is a well established separation technology basing on continuous chromatography process, which has been implemented successfully in the petrochemical, biochemical and fine chemical industries. The isocratic SMB process is presently well understood. In the last years innovations of this process has been developed e.g., VARICOL, POWERFEED or solvent gradient SMB process, which have been proved to improve the standard process efficiency with respect to the productivity of the separation and the eluent consumption. Design and optimization of new complex processes in an industrial can be done by the use of an optimization procedure coupled with an adequate mathematical model of the process dynamics. Recently, stochastic optimization algorithms have been successfully applied for the optimization of isocratic SMB process e.g. a genetic algorithm was implemented in the multiobjective optimization of a reactive SMB process, SMB and the Varicol process a random search algorithm of Luus and Jaakola was used for the optimization of mobile phase composition in the isocratic and gradient SMB process. In this work random search strategy basing on the Luus Jakola algorithm has been used for the optimization of the SMB process. Both the standard isocratic and gradient mode of SMB process has been considered. In order to improve the efficiency of calculations a modification of the original numerical procedure has been proposed. For predictions of the objective functions i.e. the minimum of eluent consumption and/or the maximum of the process productivity a mathematical model of the process dynamics was employed and implemented in the optimization procedure. Four-dimensional space of decision variables corresponding to the flowrates in the SMB zones has been searched in order to find the optimal set of the process parameters. The optimization was constrained to the purity demand in the outlet streams withdrawn in the raffinate and extract ports. The obtained set of random decision variables fulfilling purity constraints was used to construct the operating window of parameters guaranteeing successful separation. For feasible operating points the sensitivity of the purity constraints with respect to the operating parameters was calculated. Low absolute values of sensitivity are related to high maintainability i.e., ability of the unit to be retained in the state, in which it can perform required purity demand. The efficiency of the optimization procedures and the results of the sensitivity analysis will be presented.