It is revealed that the linear SCN− anion dictates critical restriction on the constituent ions of its derived 2D (PbX4(SCN)2) framework.

There is industrial incentive to extract aromatics from ethylene cracker feeds, but the conventional sulfolane solvent was found not economical by Meindersma and coworkers. Ionic liquids (ILs) have long been considered alternative aromatic extraction solvents. This work develops energy‐optimum aromatic extraction processes for an ethylene cracker feed using IL solvents. We avoid pitfalls of using simplified feeds and a priori thermodynamic property estimates, with the largest set of experimentally regressed UNIQUAC binary parameters for the IL, 1‐ethyl‐3‐methylimidazolium bis([trifluoromethyl]sulfonyl)imide ([EMIM][NTf2]). We screen process energy and operating conditions for [EMIM][NTf2] and sulfolane at varying aromatic feed contents and find [EMIM][NTf2] favorable at low aromatic feed contents. Adding light and heavy components of the ethylene cracker feed necessitates process modifications. Our novel steam‐assisted extractive distillation developed for [EMIM][NTf2] is also suitable for sulfolane. We show that the [EMIM][NTf2] solvent can reduce 10.7% of energy consumption compared to sulfolane using the same novel process.

Ionic liquids (ILs) are promising alternatives to conventional solvents for selective separation of aromatics from hydrocarbon mixtures, and their implementations depend on economic feasibility demonstrated by process simulation. Prior process modeling studies typically assume simplified hydrocarbon feeds or use the COSMO‐SAC predictive model. Our goal is to evaluate how feed simplifications and COSMO‐SAC predictions impact process modeling. We collect experimental data for 1‐Ethyl‐3‐methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIM][NTf2]) from the ILThermo database to regress UNIQUAC model binary interaction parameters for 17 hydrocarbons. We find that feed simplifications tend to significantly underpredict process energy requirements and fail to reveal important details in the extractive distillation section of the process. COSMO‐SAC predictions underpredict activity coefficient of aliphatics in [EMIM][NTf2] by a large margin, which leads to lower aromatic‐aliphatic selectivities and overprediction of process energy requirements. It is significant enough to lead to the conclusion of process infeasibility in the case of [EMIM][NTf2].

We evaluate the predictive power of the PR + COSMOSAC Equation of State (EOS) for vapor pressure using a large dataset of 19,081 compounds. The PR + COSMOSAC EOS uses results of quantum mechanical solvation calculations to determine the energy and molecular volume parameters in the Peng-Robinson EOS and thus does not require experimental critical temperature (T c ), pressure (P c ), and acentric factor (ω) as in the conventional approach. The prediction accuracy (average absolute relative deviation) is 141%, about seven times that of the PR EOS. A new approach is developed to improve the prediction accuracy from PR + COSMOSAC EOS by incorporating experimental normal boiling temperature. The prediction accuracy improves to 58%, about three times that of the PR EOS. The PR + COSMOSAC EOS is an effective method for vapor pressure prediction when no or just some experimental data is available.

Thermodynamic properties and fluid phase equilibria are crucial for the design and development of a chemical process. However, such data may not always be available, particularly for fine or specialty chemicals. In this work, we evaluate the reliability of using modern computational chemistry combined with recently developed predictive thermodynamic models to provide all the thermodynamic properties required in process design with ASPEN PLUS. Specifically, the G3 method is used for the ideal gas heat capacities and properties of formation, and the PR+COSMOSAC equation of state and COSMO‐SAC activity coefficient model are utilized for the properties and phase behaviors of pure and mixture fluids. These methods are chosen because they do not require any species‐dependent parameters and can, in principle, be applied to any chemical species. For a set of 972 chemicals, it is found that most properties can be predicted with a satisfactory accuracy (less than 10%: critical temperature [5%], critical pressure [10%], critical volume [5%], constant pressure ideal gas heat capacity [5%], and heat of vaporization [10%], except for the acentric factor [33%] and vapor pressure [73%]). Furthermore, the predicted results show little bias suggesting that these theoretically based methods are reliable for new chemicals for which experimental data are not yet available. Our analyses show that better accuracy in the prediction of vapor pressure and formation enthalpy and free energy is necessary for the design of chemical processes without relying on any experimental input. Nonetheless, these methods often provide reliable relative property values (e.g., relative value of normal boiling temperature can be predicted with 94% accuracy), making it possible to screen for new chemicals for improving existing processes.

Ionic liquids (ILs) are promising solvents for the aromatic extraction process. An attractive characteristic is the existence of hundreds of ILs that exhibit different properties. To identify key properties of IL solvents for an energy-optimum aromatic extraction, we use process simulation to generate the process datasets for multivariate data analytics with partial least squares, and use science-guided fundamentals to develop an IL heat load variable (HLV). We consider 16 well-studied ILs and correlate process steam duty and process variables affecting equipment size to the HLV for ethylene cracker feeds of low aromatic content. For such feeds in an IL aromatic extraction process, 11 of 16 ILs show energy advantage compared with sulfolane solvent with the lowest energy IL process requiring 57% of total energy required for an equivalent sulfolane process. Our results facilitate the IL solvent selection for pilot tests and subsequent commercialization of an IL aromatic extraction process.

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