Towards estimating absorption of major air pollutant gasses in ionic liquids using soft computing methods

Amirkhani, Farid; Dashti, Amir; Abedsoltan, Hossein; Mohammadi, Amir H.; Chau, Kwok-wing

doi:10.1016/j.jtice.2021.07.032

Cited by 14 publications

(8 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulated annealing (SA) is the first proposed algorithm to broaden the local search methods. Prevention from the local optima is the key aspect of this method . SA was proposed by Metropolis et al and then popularized by Kirkpatrick et al SA is a stochastic optimization algorithm employed for finding the global solution in combinatorial optimization problems .…”

Section: Theory Of Smart Techniquesmentioning

confidence: 99%

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Azarpour

Zendehboudi

2023

ACS Omega

View full text Add to dashboard Cite

CO2 emission reduction is an essential step to achieve the climate change targets. Solvent-based post-combustion CO2 capture (PCC) processes are efficient to be retrofitted to the existing industrial operations/installations. Solvent degradation (and/or loss) is one of the main concerns in the PCC processes. In this study, the thermal degradation of monoethanolamine (MEA) is investigated through the utilization of hybrid connectionist strategies, including an artificial neural network-particle swarm optimization (ANN-PSO), a coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and an adaptive neuro-fuzzy inference system (ANFIS). Moreover, gene expression programming (GEP) is employed to generate a correlation that relates the solvent concentration to the operating variables involved in the adverse phenomenon of solvent thermal degradation. The input variables are the MEA initial concentration, CO2 loading, temperature, and time, and the output variable is the remaining/final MEA concentration after the degradation phenomenon. According to the training and testing phases, the most accurate model is ANFIS, and the reliability/performance of its optimal network is assessed by the coefficient of determination (R 2), mean squared error, and average absolute relative error percentage, which are 0.992, 0.066, and 2.745, respectively. This study reveals that the solvent initial concentration has the most significant impact, and temperature plays the second most influential effect on solvent degradation. The developed models can be used to predict the thermal degradation of any solvent in a solvent-based PCC process regardless of the complicated reactions involved in the degradation phenomenon. The models introduced in this study can be employed for the development of more accurate hybrid models to optimize the proposed systems in terms of cost, energy, and environmental prospects.

show abstract

Section: Theory Of Smart Techniquesmentioning

confidence: 99%

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Azarpour

Zendehboudi

2023

ACS Omega

View full text Add to dashboard Cite

show abstract

“…It will also save time, energy, and material usage by presenting a clear path on how a process should be done to generate experimental data. Some examples are to predict the solubility in ionic liquids, degree of polymerization, and viscosity values 42–47 …”

Section: Introductionmentioning

confidence: 99%

“…Some examples are to predict the solubility in ionic liquids, degree of polymerization, and viscosity values. [42][43][44][45][46][47] This study focuses on the application of artificial intelligence to predict the yield of TPA in hydrolysis of PET by considering factors effecting the yield. MLP-ANN and ANFIS models were designed to predict the yield of TPA, the output feature.…”

Section: Introductionmentioning

confidence: 99%

Prediction of terephthalic acid yield in aqueous hydrolysis of polyethylene terephthalate

Abedsoltan

Zoghi

Mohammadi

2023

J of Applied Polymer Sci

Self Cite

View full text Add to dashboard Cite

Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) due to production of high‐quality terephthalic acid (TPA), the PET monomer. PET hydrolysis depends on various factors including PET size, catalyst concentration, and reaction temperature. So, modeling PET hydrolysis by considering the effective factors can provide useful information for material researchers to specify how to design and run these reactions. It will save time, energy, and materials by optimizing the hydrolysis conditions. Machine learning algorithms enable to design models to predict the output results. For the first time, 381 experimental data were gathered to model aqueous hydrolysis of PET. Effective factors on PET hydrolysis were connected to the TPA yield. The logistic regression was applied to rank the effective factors. Two algorithms were proposed, artificial neural network multi‐layer perceptron (ANN‐MLP) and adaptive network‐based fuzzy inference system (ANFIS). The dataset was divided into training, validating, and testing sets to train, validate, and test the models, respectively. The models predicted TPA yield sufficiently where the ANFIS model outperformed. R‐squared (R2) and Root Mean Square Error (RMSE) loss functions were employed to measure the efficiency of the models and evaluate their performance.

show abstract

“…The upsurge of the average temperature of the globe is recognized as the leading concern and the most acute issue resulted from the emitted GHGs 3 . Carbon dioxide (CO 2 ) is among the well‐known GHGs which have a significant contribution to the aforesaid issues 4 . The rise in the amount of produced CO 2 is caused by human activities, principally the industrial applications of fossil fuels 5 .…”

Section: Introductionmentioning

confidence: 99%

“…3 Carbon dioxide (CO 2 ) is among the well-known GHGs which have a significant contribution to the aforesaid issues. 4 The rise in the amount of produced CO 2 is caused by human activities, principally the industrial applications of fossil fuels. 5 Due to this fact, the last decades have witnessed great attention being put into techniques allowing the removal or the reduction in the amounts of CO 2 in different industrial processes dealing with this gas, such as the production systems of natural gas.…”

mentioning

confidence: 99%

Toward robust models for predicting carbon dioxide absorption by nanofluids

et al. 2022

View full text Add to dashboard Cite

The application of nanofluids has received increased attention across a number of disciplines in recent years. Carbon dioxide (CO2) absorption by using nanofluids as the solvents for the capture of CO2 is among the attractive applications, which have recently gained high popularity in various industrial aspects. In this work, two robust explicit‐based machine learning (ML) methods, namely group method of data handling (GMDH) and genetic programming (GP) were implemented for establishing accurate correlations that can estimate the absorption of CO2 by nanofluids. The correlations were developed using a comprehensive database that involved 230 experimental measurements. The obtained results revealed that the proposed ML‐based correlations can predict the absorption of CO2 by nanofluids with high accuracy. Besides, it was found that the GP‐based correlation yielded more precise predictions compared to the GMDH‐based correlation. The GP‐based correlation has an overall coefficient of determination of 0.9914 and an overall average absolute relative deviation of 3.732%. Lastly, the carried‐out trend analysis confirmed the compatibility of the proposed GP‐based correlation with the real physical tendency of CO2 absorption by nanofluids. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

show abstract

Towards estimating absorption of major air pollutant gasses in ionic liquids using soft computing methods

Cited by 14 publications

References 66 publications

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Prediction of terephthalic acid yield in aqueous hydrolysis of polyethylene terephthalate

Toward robust models for predicting carbon dioxide absorption by nanofluids

Contact Info

Product

Resources

About

Towards estimating absorption of major air pollutant gasses in ionic liquids using soft computing methods

Cited by 14 publications

References 66 publications

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO2 Capture Processes

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO2 Capture Processes

Prediction of terephthalic acid yield in aqueous hydrolysis of polyethylene terephthalate

Toward robust models for predicting carbon dioxide absorption by nanofluids

Contact Info

Product

Resources

About

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes