Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Abstract

Amino acid salt (AAs) aqueous solutions have recently exhibited a great potential in CO 2 absorption from various gas mixtures. In this work, four hybrid machine learning methods were developed to evaluate 626 CO 2 and AAs equilibrium data for different aqueous solutions of AAs (potassium sarcosinate, potassium l -asparaginate, potassium l -glutaminate, sodium l -phenylalanine, sodium glycinate, and potassium lysinate) gathered from reliable references. The models are the hybrids of the least squares support vector machine and coupled simulated annealing optimization algorithm, radial basis function neural network (RBF-NN), particle swarm optimization–adaptive neuro-fuzzy inference system, and hybrid adaptive neuro-fuzzy inference system. The inputs of the models are the CO 2 partial pressure, temperature, mass concentration in the aqueous solution, molecular weight of AAs, hydrogen bond donor count, hydrogen bond acceptor count, rotatable bond count, heavy atom count, and complexity, and the CO 2 loading capacity of AAs aqueous solution is considered as the output of the models. The accuracies of the models’ results were verified through graphical and statistical analyses. RBF-NN performance is promising and surpassed that of other models in estimating the CO 2 loading capacities of AAs aqueous solutions.

Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio–Temporal Parameters

“…The principal part of the RBF neural network is the hidden layer, which transmits data from the input space to the hidden space with higher dimensions. 36 , 40 Each point of the hidden layer with a particular radius is located at a given space, and in each neuron, the distance between the input vector and its center is calculated. 41 Euclidean distance is used to measure the distance between centers and inputs, which is calculated from the following equation …”

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.…”

“…FIGURE 12Regression plot for the absorption of CO 2 prognostication by (A) LSSVM, (B) RBF-NN, (C) PSO-ANFIS, and (D) ANFIS(Dashti et al, 2021a).…”

Estimation of CO2 Absorption by a Hybrid Aqueous Solution of Amino Acid Salt with Amine

“…In this absorption process, alkanolamines are the conventional solvents to absorb the gas. However, amino acid salt (AAS) solutions have recently shown that they could be potential alternatives as chemical absorbents due to remarkable chemical properties they possess such as high chemical reactivity, high stability against oxidation, and low viscosities [9]. In a chemical absorption process, the challenge is focused on the experimental methods for gas separation, which could be risky, tedious, and hard to carry out.…”