Characterizing CO2 capture with aqueous solutions of LysK and the mixture of MAPA + DEEA using soft computing methods
Introduction
Climate change has attracted much attention in the recent years. One major factor contributing to climate change is greenhouse gas emissions among which CO2 has a much more contribution. For example, CO2 emissions constitute 82% of the U.S [1]. Greenhouse gas emissions in 2015. Although CO2 is a natural part of the Earth's carbon cycle, its excessive amount generated by human has altered the cycle. The main source of CO2 emissions is the combustion of fossil fuels such as oil, natural gas, and coal which are used for heat, transportation, and various industries.
The best way to reduce CO2 emission is eliminating fossil fuel consumption which is proven to be impracticable with the current technologies [2,3]. An alternative is to use the available technologies to capture CO2 emission from the fossil fuel consumption. The captured CO2 can then be used in biological processes such as algae production [[4], [5], [6], [7], [8]], processed further using various methods such as CO2 geosequestration [7,[9], [10], [11], [12]], or converted to different chemicals or fuels [7,[13], [14], [15], [16], [17], [18], [19], [20], [21], [22]].
The main CO2 capture processes are absorption, where CO2 is absorbed selectively by a chemical solvent; adsorption, where the gas is passed through a packed bed, and CO2 is captured by the solid adsorbents; and permeation, where the gas is passed through a membrane, and CO2 is separated [[23], [24], [25], [26]]. The less common methods include cryogenics separation, where CO2 is captured by condensation; and gas hydrate crystallization, where CO2 is trapped in light hydrocarbon cage-like structures [[27], [28], [29], [30], [31], [32]]. Hybrid methods are also investigated in the literature [[33], [34], [35], [36], [37]].
CO2 capture by chemical absorption is one of the oldest of the aforementioned techniques. Many solvents such as amine solutions including monoethanolamine (MEA), diethanolamine (DEA), methylidiethanolamine (MDEA), piperazine (PZ), 2-amino-2-methyl-1-propanol (AMP), 2-(diethylamino)ethanol (DEEA), triisopropanolamine (TIPA), and 4-(diethylamino)-2-butanol (DEAB) [[38], [39], [40], [41], [42]], and ionic liquids including Imidazolium, Pyrollidinium, Pyridinium, Guanidinium, Phosphonium, Morpholinium, Piperidinium, Sulfonium, Ammonium, Hexafluorophosphate, Tetrafluoroborate, Alkylsulphate, and Triflate, Dicyanamide [[43], [44], [45], [46], [47], [48], [49], [50], [51]] are investigated theoretically and experimentally for this process among which MEA, DEA, and MDEA have been used for many decades [[27], [28], [29],[31], [32], [33], [34], [35], [36], [37],52].
A new generation of mixed amine solvent system, an aqueous mixture of 2-(diethylamino) ethanol (DEEA) and 3-(methylamino)-propylamine (MAPA), has recently received a large amount of attention thanks to having characteristics of biphasic/phase-change solvents. Earlier research has revealed that the aqueous blend of DEEA + MAPA results in two liquid phases upon CO2 absorption, an upper phase very low in CO2, and a lower phase rich in CO2. If only the lower phase is regenerated, owing to low liquid circulation rate in the stripping section, the energy needed for the solvent regeneration can be decreased. Also, this will result in decreased capital and operational costs due to the decrease in the size of the desorption column [53]. DEEA is a tertiary alkanol amine whereas MAPA is diamine, which has primary and secondary amine functional groups.
Moreover, aqueous potassium lysinate (LysK), which is an amino acid salt (AAS) is investigated recently as a new absorbent and has shown to have a high potential in capturing CO2 [[54], [55], [56], [57]]. AASs in general have low volatility and have similar structure to alkanolamines. Shen et al. [54,56,57] measured the solubility of CO2 in LysK solutions at various temperatures and pressures and observed a higher CO2 loading capacity than MEA solutions. They recommended Lysk as a new absorbent with fast kinetics for CO2 Capture.
The knowledge of vapor-liquid equilibrium (VLE) of CO2 capture solvent systems is required for the design and modeling of gas treating processes. As performing experimental study is in general costly and time consuming as well as being difficult at times, mathematical models and computational methods are employed to simulate conditions that are hardly possible by experiments and to facilitate process analysis. Numerous methods are developed to model or analyze CO2 capture with chemical solvents. These methods usually fall into four categories: empirical models, activity-based models, equation of state (EOS) models, and soft computing techniques. Kent–Eisenberg [58], modified UNIQUAC [59], electrolyte NRTL [60], and Deshmukh–Mather [61] are among the thermodynamic models that are able to predict the equilibrium solubility of CO2 in different solvents. These models often suffer from a limited applicable process condition range that affects their accuracy [62]. Soft computing techniques, alternatively, are convenient and yet powerful tools that enable detailed and complicated modeling and analysis. Artificial neural network (ANN) [63,64], support vector machine (SVM) [65,66], adaptive neuro-fuzzy inference system (ANFIS) [66], genetic programming [67], and decision tree based algorithms [7,68] are among these techniques which are proven to be useful for study of VLE of sour gas absorption modeling.
In this direction, herein, a novel decision-tree-based approach named Stochastic Gradient Boosting (SGB) and genetic programming (GP) schemes are used for modeling the solubility of CO2 in systems of LysK and MAPA + DEEA blend solutions. To the best knowledge of the authors, there is no published study demonstrating the application of SGB and GP methodologies for the modeling of CO2 loading of these new capture solvents. In addition, the accuracy of GP and SGB modeling techniques are compared to the modified Kent–Eisenberg model using statistical assessment.
Section snippets
Data
The required data for developing and analyzing the models are collected from the open literature [38,57], where the CO2 loading capacity of the capture solutions is represented as a function of temperature, pressure, and the concentration of capture solvents. In other words, CO2 loading capacity is a dependent variable and temperature, pressure, and the concentration of solvents are independent variables. There, the mathematical relations can be expressed using following equations:
Stochastic Gradient Boosting (SGB) tree
Boosting algorithm was chiefly evolved by Schapiro and Freund [69]. Friedman continued their work and provided the foundation for a recent generation of boosting algorithm called gradient boosting machine (GBM) [70,71]. In GBM, the main concept is to create additive regression models thru successively fitting a weak learner to up-to-date “pseudo”-residuals thru least squares at every step of the loop. Ergo, boosting can be defined as a way of fitting an additive spreading out in a set of
Results and discussions
Different statistical parameters including Correlation coefficient (R2), average absolute relative deviation (AARD), Mean-square error (MSE), Root-mean-square error (RMSE), Mean absolute error (MAE), and Absolute error (AE) as well-known commonly used criteria were measured in this study to assess the performance of the SGB and GP models. These statistical parameters are as follows:
Conclusion
In this work, two accurate and reliable modeling approaches namely SGB and GP are proposed for predicting the solubility of CO2 in aqueous LysK and MAPA + DEEA solutions based on 237 and 313 experimental data points collected from the literature. The input parameters of the developed models for predicting the solubility of CO2 in aqueous LysK solutions are temperature, partial pressure of CO2, the mass fraction of LysK, temperature, partial pressure of CO2, concentration of MAPA, and
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