Facile and Accurate Calculation of the Density of Amino Acid Salt Solutions: A Simple and General Correlation vs Artificial Neural Networks

Abstract

The extensive application of amino acid-based solvents has led to a growing demand for their thermophysical properties. A breakthrough in green carbon capture is anticipated by amino acid salt solutions (AASs), the properties of which should be calculated beforehand. This study develops facile and accurate models for the density calculation of AASs, which would be useful in further process simulations. A general, simple, and easy-to-use correlation for the densities, which is capable of predicting a vast variety of AASs precisely, is first developed. Then, artificial neural networks (ANNs) are assessed to train and test these systems. The developed correlation and ANNs are based on an extensive density databank (2007 data points of 28 AASs) that was prepared from various amino acid and alkaline compounds at different solution concentrations and temperatures. Both calculation procedures take into account the impact of temperature, concentration (wt %), and the molecular weights of amino acid and alkaline compounds as the input variables. To develop the correlation, 68% of the collected data was employed in model training, while 32% was utilized to assess the regressed parameters. The acquired errors of density calculations, in terms of the average absolute relative deviation percent (AARD%), were 1.210, 0.910, and 1.113% in the train, test, and total datasets, respectively, which confirms the excellent performance of the correlation. The developed general correlation was then compared to the ones from the literature that benefit from component-specific parameters. ANNs are also capable of calculating densities precisely. The best results of ANNs modeling were an error of 0.002% when the radial basis function (RBF) network was employed with the maximum number of neurons. It is inferred that the proposed correlation can work as a global equation to precisely estimate the densities of different AASs. In addition, ANNs offer a great performance to calculate the densities of AASs.

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

“…An optimized operation condition was obtained to be flow rate = 4.01 mol s −1 , temperature = 319.7 K, the pressure of flue gas = 103.5 kPa, reboiler pressure = 160.0 kPa, reboiler duty = 153,600 W, and condenser duty = 8600 W. It could be found that the optimal reaction temperature as obtained under a relatively complex condition did not deviate from that obtained under simple conditions. Besides, the CO 2 capture performance of a novel group of amine-based absorbents, that is, amino acid salt (AAs), were also recently explored by MLbased methods (Pakzad et al, 2020;Dashti et al, 2021a;Mehrabi et al, 2022). In these studies, the intrinsic features of amines, covering the molecular weight of AAs, hydrogen bond donor count, hydrogen bond acceptor count, rotatable bond count, and heavy atom count, were for the first time co-considered with the external influential factors.…”

“…To realize a highly selective CO 2 capture from industrial flue gas or ambient environment, advanced materials including modified amine absorbents (Pakzad et al, 2020;Gaikwad et al, 2021;Li et al, 2021;Mehrabi et al, 2022), MOFs (Ding et al, 2019), metal salts (Hu et al, 2019;Hu et al, 2021), zeolites (Khoramzadeh et al, 2019;Bakhtyari et al, 2020), deep eutectic solvents (Ali et al, 2016;Zarei et al, 2020), etc., have been proposed to be alternatives to traditional CO 2 capture agents. The adjustment of physical and chemical properties of absorbents or adsorbents is the most crucial process to make them purposefully functionalized (Rahimi et al, 2021).…”