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.