Integrated Ionic Liquid and Absorption Process Design for Carbon Capture: Global Optimization Using Hybrid Models

Abstract

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.

A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

“…Ionic liquids (IL). IL generally refers to a group of subset examples of molten salts fully comprised of abundant ions (anions and cations) with melting point below 100 °C (Lei et al, 2017;Zhang et al, 2021a;Zhang et al, 2021). Since the last decade when IL was found to be a promising absorbent for CO 2 capture (Blanchard et al, 1999), this group of nonvolatile and designable materials has been extensively explored for its functionalized role in related fields (Zeng et al, 2017).…”

“…However, considering the complex composition of the properties that can be designed, experimental methods based on a trial-and-error logic are extremely time-consuming and hardly leverage all key properties. To overcome this research gap and identify more selective agents for CO 2 separation, machine learning (ML) has been emerging as an efficient way recently, because it can screen tens of thousands of materials with a variety of physical and chemical properties being adjustable (Fernandez et al, 2014;Anderson et al, 2018;Zhu et al, 2020;Zhang et al, 2021a).…”