Deep Learning Combined with IAST to Screen Thermodynamically Feasible MOFs for Adsorption-Based Separation of Multiple Binary Mixtures

This study demonstrates the coupling of a multipurpose multilayer perceptron (MLP) model that predicts single-component adsorption for a various molecules with ideal adsorption solution theory (IAST). The resulting computational framework predicts MOF separations properties for various binary mixtures at various compositions and pressures. The accuracy of the MLP+IAST framework was sufficiently high so that, for a given separation, MOFs in the 90th percentile from MLP+IAST-based screening contain ~87% of MOFs in the 95th percentile one would obtain from molecular simulation-based screening. Clustering algorithms were shown effective to identify so-called "privileged" MOFs that were high-performing for multiple separations. Free energy calculations were performed to determine privileged MOFs that were likely to be accesses synthetically, at least from a thermodynamic perspective.

Deep Learning Combined with IAST to Screen Thermodynamically Feasible MOFs for Adsorption-Based Separation of Multiple Binary Mixtures

This study demonstrates the coupling of a multipurpose multilayer perceptron (MLP) model that predicts single-component adsorption for a various molecules with ideal adsorption solution theory (IAST). The resulting computational framework predicts MOF separations properties for various binary mixtures at various compositions and pressures. The accuracy of the MLP+IAST framework was sufficiently high so that, for a given separation, MOFs in the 90th percentile from MLP+IAST-based screening contain ~87% of MOFs in the 95th percentile one would obtain from molecular simulation-based screening. Clustering algorithms were shown effective to identify so-called "privileged" MOFs that were high-performing for multiple separations. Free energy calculations were performed to determine privileged MOFs that were likely to be accesses synthetically, at least from a thermodynamic perspective.

Manufacturing the Renewable Fuel of Butanol by Adsorption Separation Processes with the Novel Starch Sorbent

Bio-based butanol has superior properties when compared to ethanol to be the gasohol and is gradually considered to be an important biofuel from the biomass fermentation of ABE solution. The potential sorbents for acetone (A), 1-butanol (B), and ethanol (E) recovery process will be analyzed by the isotherm data and the sorbents, such as potato starch sorbent and ZSM-5 will be selected. The above sorbents to adsorb acetone, 1-butanol, and ethanol for single-component adsorption using adsorption equilibrium apparatus will be conducted and the isotherm data will be obtained.

Analytical Heterogeneous Extended Langmuir Model: Effect of Unilan Parameters

Analytical Heterogeneous Extended Langmuir (AHEL) model is explicit and computationally simple for predicting gas mixture adsorption based on parameters of pure component isotherm. An example of ethane and ethylene adsorption on molecular sieve revels that the prediction of gas mixture adsorption is related to the parameters of the maximum adsorption energies for different pure gas. The parameters are not required to be the same. But their significant difference generates big deviation in prediction of mixed gas adsorption for the system studied in this work., even though they agree well with experimental data for single component adsorption and the saturation amount adsorbed of individual component is nearly the same.