A two-step integrated MOF and pressure/vacuum swing adsorption (P/VSA) process design has been recently established for gas separation. In the first step, selected MOF descriptors and process operating conditions are simultaneously optimized to maximize the process performance. Based on the obtained results, the second step (i.e., MOF matching) is addressed and exemplified by propene/propane separation in this work. Computational MOF synthesis and screening are carried out to find new advanced material candidates for enhancing the separation process efficiency. First, model-based property-performance relationships are developed for fast MOF screening. Then, MOF building blocks are extracted from 471 CoRE MOFs. With these building blocks, 45472 hypothetical MOFs are created. After model-based and molecular simulation-based screening, six candidates are left and sent to P/VSA process optimization. Finally, three candidates are found to meet the pre-defined separation specifications and one candidate shows a better process performance than the best out of the 471 MOFs.

An integrated metal-organic framework (MOF) and pressure/vacuum swing adsorption (P/VSA) process design framework is presented for gas separation. It consists of two steps: descriptor optimization and MOF matching. In the first step, MOFs are represented as a large set of chemical and geometric descriptors from which the most influential ones are selected and treated as design variables. The valid design space is confined using a tailored classifier model and logic constraints. Based on collected adsorption isotherms of 471 different MOFs, data-driven isotherm models are developed. Combining the design space, isotherms, and process models, an integrated MOF and P/VSA process design problem is formulated. MOF descriptors and process operating conditions are optimized to maximize the process performance. The obtained optimal descriptors and isotherms can be used to guide the discovery of high-performance MOFs in a subsequent MOF matching step. This article addresses the first descriptor optimization step exemplified by propene/propane separation.

A new method for integrated ionic liquid (IL) and absorption process design is proposed where a rigorous rate-based process model is used to incorporate absorption thermodynamics and kinetics. Different types of models including group contribution models and thermodynamic models are employed to predict the process-relevant physical, kinetic, and thermodynamic (gas solubility) properties of ILs. Combining the property models with process models, the integrated IL and process design problem is formulated as an MINLP optimization problem. Unfortunately, due to the model complexity, the problem is prone to convergence failure. To lower the computational difficulty, tractable surrogate models are used to replace the complex thermodynamic models while maintaining the prediction accuracy. This provides an opportunity to find the global optimum for the integrated design problem. A pre-combustion carbon capture case study is provided to demonstrate the applicability of the method. The obtained global optimum saves 14.8% cost compared to the Selexol process.