On the estimation of viscosities and densities of CO2-loaded MDEA, MDEA + AMP, MDEA + DIPA, MDEA + MEA, and MDEA + DEA aqueous solutions

Haratipour, P ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.molliq.2017.06.123
  3. Abstract:
  4. As noteworthy properties of amine aqueous solutions, the densities and viscosities of aqueous N-Methyldiethanolamine (MDEA) solutions and mixtures of MDEA with 2-Amino-2-methyl-1-propanol (AMP), Diisopropanolamine (DIPA), Monoethanolamine (MEA), and Diethanolamine (DEA) were estimated under CO2 gas loading using Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Artificial Neural Network (MLPANN), Support Vector Machine (SVM), and Least Square Support Vector Machine (LSSVM). The density and viscosity were estimated as a function of temperature, CO2 loading, pressure, and molecular weight of mixtures. In this regard, the actual data points were collected from the literature. Genetic Algorithm (GA) was employed to determine hyper variables of the LSSVM approach and Levenberg–Marquardt algorithm was employed to optimize bias and weight values of the ANN model. In addition, Particle Swarm Optimization algorithm (PSO) was used to determine membership parameters of the ANFIS approach and related parameters of the SVM were optimized using GA. The developed tools can be of massive value for chemical engineers and chemists to have a quick check of the densities and viscosities of the aforementioned amine solutions. Results obtained from the proposed models are in satisfactory agreement with actual data. According to statistical analyses, while the obtained values of Mean Squared Error (MSE) and R-squared (R2) for estimating densities of amine solutions are 0.000011 and 0.9938, 0.000013 and 0.9937, 0.000009 and 0.9953, 0.000001 and 0.9993 for the ANN, ANFIS, SVM, and LSSVM models respectively, these values obtained 0.079 and 0.994, 0.113 and 0.9911, 0.0634 and 0.9971, 0.0079 and 0.9996 for estimating the viscosities of amine-based solutions. © 2017 Elsevier B.V
  5. Keywords:
  6. Alkanolamine aqueous solution ; CO2 capture ; Density ; Model ; Carbon dioxide ; Density (specific gravity) ; Fuzzy inference ; Fuzzy neural networks ; Fuzzy systems ; Genetic algorithms ; Mixtures ; Models ; Neural networks ; Optimization ; Particle swarm optimization (PSO) ; Solutions ; Support vector machines ; Viscosity ; 2-amino-2-methyl-1-propanol ; Adaptive neuro-fuzzy inference system ; Least square support vector machines ; Marquardt algorithm ; MDEA ; Multi layer perceptron ; N-methyldiethanolamine ; Particle swarm optimization algorithm ; Ethanolamines
  7. Source: Journal of Molecular Liquids ; Volume 242 , 2017 , Pages 146-159 ; 01677322 (ISSN)
  8. URL: https://www.sciencedirect.com/science/article/pii/S0167732217316823