Integrated ionic liquid and rate‐based absorption process design for gas separation: 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.

Reviewing and screening ionic liquids and deep eutectic solvents for effective CO2 capture

“…Theoretical models have been developed to predict properties of ILs/DESs qualitatively or quantitively, and the models include Quantitative Structure Property Relationship (QSPR) method ( Eike et al, 2004 ), regular solution theory ( Kilaru et al, 2008 ), Molecular dynamics ( Kerlé et al, 2009 ), Monte Carlo ( Shah and Maginn, 2005 ), group contribution method ( Kim et al, 2005 ; Zhang et al, 2021b ; Zhou et al, 2021 ), Conductor-like Screening Model for Real Solvents (COSMO-RS) method ( Liu et al, 2021 ), statistical associating fluid theory (SAFT)-based equation of state ( Ji et al, 2012 ; Ji and Zhu, 2013 ; Ji et al, 2014 ; Shen et al, 2015 ; Sun et al, 2019 ; Alkhatib et al, 2020 ; Sun, 2020 ), etc. Based on the properties, ILs and DESs are screened with different criteria, for example, thermodynamic CO 2 absorption capacity (CO 2 solubility and Henry’s constant) ( Liu et al, 2021 ) and (Henry’s constant, selectivity, relative polarity, and molar volumes) ( Sumon and Henni, 2011 ), thermodynamic and kinetic performances for CO 2 absorption (CO 2 absorption capacity, viscosity, melting point, and Henry’s constant) ( Farahipour et al, 2016 ; Zhang et al, 2021a ) as well as CO 2 chemical absorption properties (chemical equilibrium constants, Henry’s constants, and reaction enthalpies) ( Moya et al, 2020 ), etc. Also, based on the thermodynamic analysis under specific operation conditions, screening has been conducted for both physical-based ILs and DESs ( Zhang et al, 2016 ).…”

Reliable and robust f(T,P,I)-QSPR models for ionic liquids enabled by balancing data distribution and LOIO-CV

“…The definitions of these parameters are listed in Table S1 of Supporting Information (Statistical parameters.docx). Through external validation, the predicting ability of these models was fully evaluated byR 2 training of the training set and R 2 testing of the testing set. To avoid cations and anions in the testing set reappearing in the training set in the meantime, the dataset was divided into training set (80%) and testing set (20%) by the ion structures and the proportion of data points.…”

“…Ionic liquids (ILs), composed of organic cations and organic/inorganic anions, have been diffusely utilized in absorption and separation 1,2 , synthesis 3 , catalysis 4,5 and electrochemistry 6,7 owing to their superior properties as gas solubility, thermal stability and low volatility. Density(ρ ) and viscosity (η ) are key process parameters required in a significant amount of applications such as chemical process simulation, equipment sizing, lubrication and refrigeration 8,9 .…”

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

“…The authors in [69] also presented a generic design methodology as well as the current limitations and future opportunities. Similarly, the authors in [71] combined the ML-based solubility model with first-principle absorption process models to perform integrated ionic liquid and process design for CO 2 capture.…”