To
obtain aromatic compounds from a crude mixture such as reformate
or pyrolysis gasoline, three different processes are simulated with
the realistic composition of reformate and product specification.
Simulations were performed by Aspen Plus supported with COSMO-RS method
to predict the physical and thermodynamic properties of ionic liquid.
Furthermore, utility analysis and economic evaluation are presented.
Conventionally, aromatic compounds are extracted from a crude mixture
either by extraction or by extractive distillation using a solvent
such as sulfolane and separated by a series of distillation columns.
In this study, the sulfolane-based commercial process is first introduced,
and two novel processes that use 4-methyl-N-butylpyridinium
tetrafluoroborate ionic liquid as solvent are proposed. The second
process shows that energy consumption has successfully decreased,
but the high price of ionic liquid offset the cost advantage. The
third process is therefore proposed to reduce the amount of ionic
liquid using two extractions. A similar level of energy saving is
achieved with reduced costs.
In the present paper, iterative learning control (ILC) is integrated with a model predictive control (MPC) technique to reject real-time disturbances. The proposed scheme is called iterative learning model predictive control (ILMPC). ILC is an e ective control technique for batch processes, but it is not a real-time feedback controller. Thus, it should be combined with MPC for real-time disturbance rejection. The existing ILMPC techniques make the error converge to zero. However, if the error converges to zero, an impractical input trajectory may be calculated. We use a generalized objective function to independently tune weighting factors of manipulated variable change with respect to both the time index and batch horizons. If the generalized objective function is used, output error converges to non-zero values. We provide convergence analysis for both cases of zero convergence and non-zero convergence.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.