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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.

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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.

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